Patent Grant
11468983
This disclosure relates to mobile telepresence robots.
A robot is generally an electro-mechanical machine guided by a computer or electronic programming. Telepresence robots and autonomous vehicle robots have the capability to move around in their environment and are not fixed to one physical location. An example of a telepresence robot that is in common use today is an automated guided vehicle or automatic guided vehicle (AGV). An AGV is generally a telepresence robot that follows markers or wires in the floor, or uses a vision system or lasers for navigation. Telepresence robots can be found in industry, military and security environments. They also appear as consumer products, for entertainment or to perform certain tasks like home assistance.
One aspect of the disclosure provides a telepresence robot system including a drive system configured to move the telepresence robot according to drive instructions; a control system in communication with the drive system, the control system configured to generate drive instructions to cause the drive system to move the telepresence robot along a navigation path; a mapping module in communication with the control system, the mapping module configured to access a map data source, the map data source including a map representative of robot-navigable areas of a robot operating surface and a plurality of time-dependent navigation tags, each time-dependent navigation tag being a data structure comprising spatial coordinates locatable relative to the map and tag information having a time-dependent robot action modifier, wherein the spatial coordinates are associated with robot-navigable areas of the operating surface, a positioning system in communication with the control system configured to provide positioning information associated with a current position; a tag identification system configured to identify at least one time-dependent navigation tag associated with the navigation path of the telepresence robot; and wherein the telepresence robot system is configured to be triggered to perform at least one of various actions (e.g., plan a navigation path, sound an alarm that the telepresence robot is approaching, fire an actuator to move the robot, for example, drive the robot, turn the head of the robot, etc.) based, at least in part, on the time-dependent action modifier associated with an identified time-dependent navigation tag.
In some embodiments, the telepresence robot system may include a navigation system configured to generate a navigation path, the navigation path comprising a sequence of coordinates from a current position on the map to a desired position on the map, wherein the navigation system is configured to be triggered to plan the navigation path based, at least in part, on the time-dependent action modifier associated with an identified time-dependent navigation tag and wherein the navigation system is configured to generate the navigation path by generating a plurality of potential navigation paths, wherein each potential navigation path has an associated cost based on the time-dependent action modifiers associated with the time-dependent navigation tags along the respective potential navigation path and selecting the navigation path from the plurality of potential navigation path based on the associated cost.
Other aspects of the disclosure provide a telepresence robot system including a local terminal and a remote telepresence robot. The local terminal may include an electronic display, a processor, and a memory in communication with the processor, the memory comprising instructions executable by the processor. The executable instructions may be configured to cause the processor to retrieve at least a portion of a plan view map representative of robot-navigable areas of a robot operating surface; retrieve at least one of a plurality of tags, each of the plurality of tags comprising tag coordinates describing the relative location of the tag and tag information, which may include a tag annotation; receive a video feed from an imaging system of a remote telepresence robot; receive positioning information; display the video feed from the imaging system of the remote telepresence robot; display the plan view map with an indication of a current position of the telepresence robot on the plan view map; display a rendition of the tag annotation of the at least one tag on at least one of the plan view map and the video feed using the tag coordinates; and transmit one or more commands to the remote telepresence robot.
In some embodiments, the instructions executable by the processor are further configured to cause the processor to determine a distortion (e.g., a coordinate transformation between a two-dimensional coordinate system and a three-dimensional coordinate system) between the plan view map and the video feed received from the imaging system of the remote telepresence robot; apply the distortion to the tag coordinates of the at least one tag to determine corresponding video coordinates and perspective data describing a location and perspective of the at least one tag relative to the video feed; and display a three-dimensional rendition of the tag annotation of the at least one tag overlaid on the video feed using the tag video coordinates.
In some embodiments, the three-dimensional rendition of the tag annotation may be dynamically re-rendered based on the current position of the remote telepresence robot and a perspective of the at least one tag relative to the video feed.
In some embodiments, the three-dimensional rendition of the tag annotation may be overlaid on the video feed with respect to an object detected in the video feed.
In some embodiments, the three-dimensional rendition of the tag annotation may be overlaid along a wall detected in the video feed.
In some embodiments, the tag information of the at least one tag comprises a telepresence robot action modifier and the robot action modifier may be configured to provide execution instructions to a control system of the telepresence robot to execute a first action in response to the telepresence robot being within a predetermined range of the tag coordinates of the at least one tag.
In some embodiments, the instructions executable by the processor are further configured to cause the processor to transmit the execution instruction to the control system of the telepresence robot when the telepresence robot is within a predetermined range of the tag coordinates of the at least one tag.
In some embodiments, the robot action modifier further comprises instructions regarding one of a time and a location on the plan view map that the control system of the telepresence robot should execute the first action.
In some embodiments, the instructions executable by the processor are further configured to cause the processor to receive a sequence of coordinates relative to the plan view map forming a path along which the remote telepresence robot has traveled; store the sequence of coordinates forming the path as a path tag comprising tag coordinates and tag information, which may include a tag annotation; retrieve the path tag when the remote telepresence robot arrives within a predetermined distance of the tag coordinates; and display a rendition of the tag annotation of the path tag on at least one of the plan view map and the video feed using the tag coordinates.
In some embodiments, the telepresence robot system local terminal further comprises at least one user input device and the sequence of coordinates forming the path may be provided by the user input device.
In some embodiments, the sequence of coordinates forming the path may be provided by the remote telepresence robot.
In some embodiments, the telepresence robot system further comprises a communication system configured to facilitate communication between the telepresence robot system local terminal and the remote telepresence robot.
In some embodiments, the local terminal further comprises at least one user input device and the user input device may be configured to allow a user to provide an indication of a desired destination of the remote telepresence robot on at least one of the plan view map and the video feed from the imaging system of the remote telepresence robot; and the command transmitted to the remote telepresence robot comprises the desired destination.
In some embodiments, the sequence of coordinates forming the robot path may be based at least in part on tagging information associated with the at least one tag.
In some embodiments, the instructions executable by the processor are further configured to cause the processor to determine a sequence of coordinates relative to the plan view map to create a robot path between the current position of the remote telepresence robot and the desired destination of the remote telepresence robot and the command transmitted to the remote telepresence robot comprises the sequence of coordinates forming the robot path.
In some embodiments, the instructions executable by the processor are further configured to cause the processor to display the sequence of coordinates forming the robot path overlaid on the plan view map.
In some embodiments, the instructions executable by the processor are further configured to cause the processor to determine a distortion (e.g., a coordinate transformation between a two-dimensional coordinate system and a three-dimensional coordinate system) between the plan view map and the video feed received from the imaging system of the remote telepresence robot; apply the distortion to the sequence of coordinates forming the robot path to determine corresponding video coordinates and perspective data describing a location and perspective of the sequence of coordinates relative to the video feed; and display a three-dimensional rendition of the sequence of coordinates forming the robot path overlaid on the video feed.
In some embodiments, the three-dimensional rendition of the sequence of coordinates forming the robot path may be overlaid on the video feed with respect to a floor detected in the video feed.
In some embodiments, the instructions executable by the processor are further configured to cause the processor to receive a sequence of coordinates relative to the plan view map from a navigation system of the remote telepresence robot, the sequence of coordinates forming a robot path between the current position of the remote telepresence robot and a desired destination of the remote telepresence robot; and display the sequence of coordinates forming the robot path overlaid on the plan view map.
In some embodiments, the instructions executable by the processor are further configured to cause the processor to determine a distortion (e.g., a coordinate transformation between a two-dimensional coordinate system and a three-dimensional coordinate system) between the plan view map and the video feed received from the imaging system of the remote telepresence robot; apply the distortion to the sequence of coordinates forming the robot path to determine corresponding video coordinates and perspective data describing the location and perspective of the sequence of coordinates relative to the video feed; and display a three-dimensional rendition of the sequence of coordinates forming the robot path overlaid on the video feed.
In some embodiments, the three-dimensional rendition of the sequence of coordinates forming the robot path may be overlaid on the video feed with respect to a floor detected in the video feed.
In some embodiments, the tag information comprises information regarding one of: an availability of a wireless communication signal, a speed the remote telepresence robot should travel, a location of a point of interest, a location of a person, a location of a docking station, a location of a rest area, a location of a glass wall, a location of a ramp, a location of an object, an optimal route to navigate a tight area, an optimal rout to navigate a congested area, and an action a remote telepresence robot should execute.
In some embodiments, the tag information may relate to a position, a path, and/or a volume, and the control system may be configured to execute an action relative to the position, the path, and/or the volume.
In some embodiments, the instructions executable by the processor are further configured to cause the processor to receive coordinates on the plan view map of an obstacle detected by a sensor system of the remote telepresence robot.
In some embodiments, the plan view map and the plurality of tags are stored remotely.
In some embodiments, the plan view map and the plurality of tags are stored within the remote telepresence robot.
In some embodiments, the instructions executable by the processor are further configured to cause the processor to determine a distortion (e.g., a coordinate transformation between a two-dimensional coordinate system and a three-dimensional coordinate system) between the plan view map and the video feed received from the imaging system of the remote telepresence robot; and generate a hybrid map view comprising a blended view of the plan view map and the video feed from the imaging system of the remote telepresence robot.
In some embodiments, the hybrid map view comprises a three-dimensional representation of the plan view map overlaid on the video feed.
In some embodiments, the telepresence robot system local terminal further comprises at least one user input device and the instructions executable by the processor are further configured to cause the processor to receive a request via the at least one input device for a rendered look ahead for a virtual location of the remote telepresence robot on the plan view map; determine a distortion (e.g., a coordinate transformation between a two-dimensional coordinate system and a three-dimensional coordinate system) between the plan view map and the video feed received from the imaging system of the remote telepresence robot; and generate a virtual three-dimensional video feed based on a virtual location of the remote telepresence robot; and display the virtual three-dimensional video feed based on the virtual location of the remote telepresence robot.
In some embodiments, the tag information of the at least one tag comprises a set of coordinates with respect to the plan view map defining a protected region, and the tag annotation of the at least one tag may be configured to indicate the presence of a protected region.
In some embodiments, the instructions executable by the processor are further configured to cause the processor to receive a request to create a new tag; associate tag coordinates describing a relative location of the new tag and tag information, which may include a tag annotation with the new tag; and display a rendition of the tag annotation of the new tag on at least one of the plan view map and the video feed using the tag coordinates.
In some embodiments, the request to create the new tag may be generated by the remote telepresence robot.
In some embodiments, the request to create the new tag may be automatically generated based on a detected object in the video feed.
In some embodiments, the new tag may be a temporary tag configured to expire once the detected object is no longer present in the video feed.
In some embodiments, the object may be a person and the tag information of the new tag comprises identification information associated with the person.
In some embodiments, the object may be a person and the tag information of the new tag comprises potential actions the remote telepresence robot can execute with respect to the person.
In some embodiments, the request to create the new tag may be generated by a user input device in communication with the telepresence robot system local terminal.
In some embodiments, the request to create the new tag is made with respect to the video feed.
In some embodiments, the request to create the new tag is made with respect to the plan view map.
In some embodiments, the request to create a new tag is made with respect to the current position of the remote telepresence robot.
In some embodiments, the tag information comprises information regarding one of: an availability of a wireless communication signal, a speed the remote telepresence robot should travel, a location of a point of interest, a location of a person, a location of a docking station, a location of a rest area, a location of a glass wall, a location of a ramp, a location of an object, an optimal route to navigate a tight area, an optimal rout to navigate a congested area, and an action a remote telepresence robot should execute.
In other embodiments, a telepresence robot may communicate with a remote terminal. The telepresence robot may include a drive system configured to move the telepresence robot according to drive instructions; a control system in communication with the drive system, the control system configured to generate drive instructions to cause the drive system to move the telepresence robot; an imaging system in communication with the control system; a mapping module in communication with the control system, the mapping module configured to access a map data source, the map data source comprising a plan view map representative of robot-navigable areas of a robot operating surface; and a plurality of tags, each tag being a data structure comprising tag coordinates describing the relative location of the tag and tag information, which may include a tag annotation; a positioning system in communication with the control system configured to provide a current position with respect to the plan view map; a tag identification system configured to identify at least one tag relevant to a navigation path of the telepresence robot; and a communication system configured to facilitate communication between the control system and a remote terminal, and the control system may be configured to execute an action based on an identified tag whose tag information comprises a telepresence robot action modifier.
In some embodiments, the tagging information for the identified tag comprises instructions regarding one of a time and a location on the plan view map that the control system should execute the action.
In some embodiments, the control system may be configured to transmit a video feed from the imaging system to the remote terminal via the communication system and the control system may be configured to receive an indication of a desired destination on the plan view map from the remote terminal via the communication system.
In some embodiments, the telepresence robot may further comprise: a plurality of sensors configured to identify obstacles in the vicinity of the telepresence robot and an obstacle avoidance system in communication with the plurality of sensors and in communication with the control system, where the control system may be further configured to generate additional drive instructions to avoid obstacles in the vicinity of the telepresence robot.
In some embodiments, the plurality of sensors comprises at least one of a proximity sensor, a contact sensor, an odometry sensor, and a three-dimensional image sensor.
In some embodiments, the plurality of sensors may comprise a three-dimensional image sensor that forms a point cloud, including a three-dimensional occupancy of obstacles, and the drive instructions may be configured to avoid the three-dimensional occupancy of the obstacles.
In some embodiments, the telepresence robot may further comprise: a map generation system in communication with the control system, the map generation system configured to autonomously create the plan view map of the robot operating surface, where the control system generates drive instructions to cause the telepresence robot to move throughout the robot operating surface and obtain a plurality of measurements, and the map generation system uses the plurality of measurements to generate the plan view map.
In some embodiments, the telepresence robot may further comprise a navigation system configured to generate a navigation path comprising a sequence of coordinates from the current position on the plan view map to the desired destination on the plan view map.
In some embodiments, the telepresence robot may transmit coordinates relative to the plan view map of a detected obstacle to the remote terminal via the communication system.
In some embodiments, the sequence of coordinates forming the navigation path may be based at least in part on tagging information associated with the identified tag.
In some embodiments, the navigation system is configured to generate the navigation path by selecting a navigation path from a plurality of potential navigation paths, and the tags relevant to the navigation path of the telepresence robot are associated with the plurality of potential navigation paths, and the navigation system is configured to select the navigation path based at least in part on the identified relevant tags.
In some embodiments, the sequence of coordinates forming the navigation path is transmitted via the communication system to the remote terminal.
In some embodiments, the telepresence robot may be configured to create a new tag using the sequence of coordinates forming the navigation path, such that the new tag comprises the sequence of coordinates, tagging information related to the navigation path, and a tag annotation related to the navigation path.
In some embodiments, the tag information of each of the plurality of tags comprises information regarding one of: an availability of a wireless communication signal, a speed the remote telepresence robot should travel, a location of a point of interest, a location of a person, a location of a docking station, a location of a rest area, a location of a glass wall, a location of a ramp, a location of an object, an optimal route to navigate a tight area, an optimal rout to navigate a congested area, and an action a remote telepresence robot should execute.
In some embodiments, the control system may be further configured to receive a navigation path from the current position on the plan view map to the desired destination on the plan view map and the control system may be further configured to generate drive instructions to cause the drive system to move the telepresence robot to the desired destination based on the navigation path.
In some embodiments, the communication system may be configured to detect a disruption in communication between the telepresence robot and a remote terminal, wherein the control system may be further configured to continue to generate drive instructions to cause the telepresence robot to autonomously move to the desired destination during the disruption in communication.
In some embodiments, the map data source may be stored remotely, such that the mapping module may be configured to access the map data source via the communication system.
In some embodiments, the map data source may be stored within the telepresence robot, such that the mapping module may be configured to access an internal map data source.
In some embodiments, the internal map data source may be synced with a remotely stored map data source.
In some embodiments, the positioning system may be further configured to provide a robot pose relative to the plan view map.
In some embodiments, the telepresence robot may be configured to create a new tag by: associating tag coordinates describing the relative location of the new tag with respect to one of the plan view map and a video feed generated by the imaging system; associating tag information with the new tag; and associating a tag annotation with the new tag.
In some embodiments, the new tag may be created in response to the telepresence robot detecting an object in the video feed.
In some embodiments, the object may be a person and the tag information of the new tag comprises identification information associated with the person.
In some embodiments, the object may be a person and the tag information of the new tag comprises potential actions the remote telepresence robot can execute with respect to the person.
In some embodiments, the tag information comprises information regarding one of: an availability of a wireless communication signal, a speed the remote telepresence robot should travel, a location of a point of interest, a location of a person, a location of a docking station, a location of a rest area, a location of a glass wall, a location of a ramp, a location of an object, an optimal route to navigate a tight area, an optimal rout to navigate a congested area, and an action a remote telepresence robot should execute.
In some embodiments, the telepresence robot system may further comprise: an RFID reader in communication with the positioning system, where the positioning system associates a plurality of RFID chips with a corresponding plurality of coordinates on the plan view map, and the positioning system may be configured to determine the current position of the telepresence robot based at least in part on the location of one or more RFID chips within range of the RFID reader.
Various methods of control may be employed in the present systems and methods. For example, a telepresence robot system local terminal may comprise: an electronic display; a processor in communication with the electronic display interface; a memory in communication with the processor, the memory comprising instructions executable by the processor configured to cause the processor to: retrieve at least a portion of a plan view map representative of robot-navigable areas of a robot operating surface; receive a video feed from an imaging system of the remote telepresence robot at a first perspective; receive a current position from a positioning system of the remote telepresence robot with respect to a plan view map; display the video feed from the imaging system of the remote telepresence robot; display the plan view map with an indication of the current position of the telepresence robot on the plan view map; transmit a command to the remote telepresence robot; and a user input device in communication with the processor, the user input device configured to allow a user to select a movement for a remote telepresence robot, the selection of the movement comprising selecting a destination of the remote telepresence robot with respect to the video feed; with respect to the plan view map; and by incrementally advancing the remote telepresence robot in one of at least four possible directions relative to the current position of the remote telepresence robot.
In some embodiments, the selection of the movement comprises selecting an alternative perspective of the video feed by selecting a point within the video feed. This mode would likely be used for intermediate distances to get to locations within view on the video feed.
In some embodiments, selection of the movement comprises selecting an alternative perspective of the video feed by selecting a point on the plan view map. This mode would likely be used for farther distances (e.g., down hallways, between rooms, etc.) to locations not within view on the video feed. In some embodiments, selection of the movement comprises using a joystick or meta joystick in manual control. This mode would likely be used for micro/finer adjustments, e.g., within a room in close proximity to humans/patients.
In some embodiments, the selection of the movement comprises selecting an alternative perspective of the video feed by incrementally panning or tilting the imaging system while the remote telepresence robot remains in the current position.
In some embodiments, wherein the selection of the movement may relate to rotating one of a lower portion of the remote telepresence robot and an upper portion of the remote telepresence robot.
In some embodiments, there will be a way to switch between modes, e.g. multi-modal user interface wherein one can select to control either head/imaging system movement or movement of the base/lower portion of the remote presence robot.
In some embodiments when control of head/imaging system movement is selected there may be options to select either position-based box-zoom head motion via mouse or velocity-based head motion via mouse.
In some embodiments when control of base/lower portion of the remote presence robot is selected there may be options to select from one of the following: (1) click-on-map, i.e. top down map view and click on target destination or select from destination list; (2) click-on-video, i.e. position-based control that enables click on location in the video and robot drives there; (3) joystick or meta joystick, e.g., mouse velocity-based control or arrows specifying forward, left, right, etc.
In some embodiments the functionality/information needed to be accessed by user at all times while robot base is moving includes: (1) remote view, i.e., where the robot is headed (view should be large enough to provide meaningful visual information for user to operate safely); (2) for supervisory control modes, potential need for override capability to cancel/abort operation as needed.
In some embodiments, the instructions executable by the processor are further configured to cause the processor to receive a selection of a destination of the remote robot from the user input device; determine a sequence of coordinates relative to the plan view map to create a navigation path between the current position of the remote telepresence robot and the selected destination of the remote telepresence robot; and transmit a command to the remote telepresence robot comprising the sequence of coordinates forming the navigation path.
In some embodiments, the instructions executable by the processor are further configured to cause the processor to display the sequence of coordinates forming the navigation path overlaid on the plan view map.
In some embodiments, the instructions executable by the processor are further configured to cause the processor to: determine a distortion between the plan view map and the video feed received from the imaging system of the remote telepresence robot (e.g., a coordinate transformation between a two-dimensional coordinate system and a three-dimensional coordinate system); apply the distortion to the sequence of coordinates forming the navigation path to determine corresponding video coordinates and perspective data describing the location and perspective of the sequence of coordinates relative to the video feed; and display a three-dimensional rendition of the sequence of coordinates forming the navigation path overlaid on the video feed.
In some embodiments, the three-dimensional rendition of the sequence of coordinates forming the navigation path may be overlaid on the video feed with respect to a floor detected in the video feed.
In some embodiments, the instructions executable by the processor are further configured to cause the processor to receive a selection of a destination of the remote robot from the user input device; transmit destination coordinates relative to the plan view map to the remote telepresence robot, the destination coordinates corresponding to the selected destination; receive a sequence of coordinates relative to the plan view map from a navigation system of the remote telepresence robot, the sequence of coordinates forming a navigation path between the current position of the remote telepresence robot and the desired destination of the remote telepresence robot; and display the sequence of coordinates forming the navigation path overlaid on the plan view map.
In some embodiments, the instructions executable by the processor are further configured to cause the processor to determine a distortion between the plan view map and the video feed received from the imaging system of the remote telepresence robot (e.g., a coordinate transformation between a two-dimensional coordinate system and a three-dimensional coordinate system); apply the distortion to the sequence of coordinates forming the navigation path to determine corresponding video coordinates and perspective data describing the location and perspective of the sequence of coordinates relative to the video feed; and display a three-dimensional rendition of the sequence of coordinates forming the navigation path overlaid on the video feed.
In some embodiments, the three-dimensional rendition of the sequence of coordinates forming the navigation path may be overlaid on the video feed with respect to a floor detected in the video feed.
In some embodiments, the instructions executable by the processor are further configured to cause the processor to receive coordinates on the plan view map of an obstacle detected by a sensor system of the remote telepresence robot.
In some embodiments, the plan view map is stored remotely.
In some embodiments, the plan view map is stored within the remote telepresence robot.
In some embodiments, the instructions executable by the processor are further configured to cause the processor to determine a distortion between the plan view map and the video feed received from the imaging system of the remote telepresence robot (e.g., a coordinate transformation between a two-dimensional coordinate system and a three-dimensional coordinate system); and generate a hybrid map view comprising a blended view of the plan view map and the video feed from the imaging system of the remote telepresence robot.
In some embodiments, the hybrid map view comprises a three-dimensional representation of the plan view map overlaid on the video feed.
In some embodiments, the instructions executable by the processor are further configured to cause the processor to receive a request via the input device for a rendered look-ahead for a virtual location of the remote telepresence robot on the plan view map determine a distortion between the plan view map and the video feed received from the imaging system of the remote telepresence robot (e.g., a coordinate transformation between a two-dimensional coordinate system and a three-dimensional coordinate system); and generate a virtual three-dimensional video feed based on a virtual location of the remote telepresence robot; and display the virtual three-dimensional video feed based on the virtual location of the remote telepresence robot.
In some embodiments, a robot may be configured to unwind and/or to control an upper portion and a lower portion independently in order to appear human-like. For example, a telepresence robot may comprise: an upper portion; a lower portion rotatably connected to the upper portion; a drive system configured to move the telepresence robot according to drive instructions; a control system in communication with the drive system, the control system configured to generate drive instructions to cause the drive system to move the telepresence robot; a rotation system configured to rotate the robot from a first heading to a second heading by rotating the upper portion and the lower portion independently.
In some embodiments, the rotation system may be configured to rotate the robot toward a second heading by rotating the upper portion of the robot toward the second heading; detecting that the upper portion of the robot has reached a panning limit of the upper portion of the robot relative to the lower portion of the robot; begin rotating the lower portion of the robot toward the second heading at the panning limit of the upper portion of the robot; detect that the upper portion of the robot has reached the second heading; and continue rotating the lower portion of the robot toward the second heading while simultaneously counter-rotating the upper portion of the robot, such that the upper portion of the robot maintains the second heading.
In some embodiments, the panning limit may be reached when the upper portion cannot physically rotate anymore with respect to the lower portion of the robot.
In some embodiments, the panning limit may be reached when the upper portion is misaligned with respect to the lower portion a predefined number of rotation degrees.
In some embodiments, the panning limit may be a function of the number of degrees the upper portion is misaligned with respect to the lower portion and the length of time the upper portion has been misaligned with respect to the lower portion.
In some embodiments, the rotation system may be configured to rotate the robot toward a second heading by rotating the upper portion of the robot toward the second heading at a first rotational velocity; rotating the lower portion of the robot toward the second heading at a second rotation velocity; detect that the upper portion of the robot has reached the second heading; and continue rotating the lower portion of the robot toward the second heading while simultaneously counter-rotating the upper portion of the robot, such that the upper portion of the robot maintains the second heading.
In some embodiments the telepresence robot may further comprise an imaging system in communication with the control system and a positioning system in communication with the control system configured to provide a current position of the robot relative to a plan view map and a current alignment of the upper portion with respect to the plan view map, where the control system may be configured to transmit a video feed from the imaging system, the current position of the robot, and the current alignment of the upper portion to a remote terminal, such that the remote terminal can determine a distortion between the plan view map and the video feed received from the imaging system of the remote telepresence robot (e.g., a coordinate transformation between a two-dimensional coordinate system and a three-dimensional coordinate system); apply the distortion to a tag having coordinates associated with the plan view map in order determine corresponding video coordinates and perspective data describing the location and perspective of the tag relative to the video feed; and display a three-dimensional rendition of the tag overlaid on the video feed using the video coordinates.
The above described embodiments are described from the perspective of a robot and/or a local terminal. It should be apparent to one of skill in the art that the embodiments described above could be implemented as systems, adapted as methods performed by a system, or embodied in a computer-readable medium that could be executed by a system. For example, a method for changing a heading of a robot may comprise transmitting a heading to a control system of a robot, the control system of the robot in communication with a drive system configured to move the robot according to drive instructions and rotating an upper portion of the robot toward the heading independently from the lower portion of the robot.
In some embodiments, a method for controlling a remote telepresence robot may comprise retrieving at least a portion of a plan view map representative of robot-navigable areas of a robot operating surface; retrieving at least one of a plurality of tags, each of the plurality of tags comprising tag coordinates describing the relative location of the tag and tag information; receiving a video feed from an imaging system of a remote telepresence robot; receiving positioning information associated with a current position of the remote telepresence robot; displaying, via an electronic display, the video feed from the imaging system of the remote telepresence robot; displaying, via the electronic display, a rendition of the tag information of the at least one tag on the video feed using the tag coordinates; and transmitting a command to the remote telepresence robot. A method for controlling a telepresence robot, comprising retrieving at least a portion of a plan view map; retrieving at least one of a plurality of tags, each tag being a data structure comprising tag coordinates describing the relative location of the tag and tag information; determining a current position relative to the plan view map; identifying at least one tag of the plurality of tags relevant to a navigation path of the telepresence robot; executing an action based on the identified tag whose tag information comprises a telepresence action modifier.
In some embodiments, a method for controlling a telepresence robot may comprise retrieving at least a portion of a plan view map representative of robot-navigable areas of a robot operating surface; receiving a video feed from an imaging system of the remote telepresence robot at a first perspective; receiving positioning data associated with a current position of the remote telepresence robot; displaying the video feed from the imaging system of the remote telepresence robot; and transmitting a command to the remote telepresence robot; and receiving a plurality of movement selections from a user input device movement, the movement selections made (1) with respect to the video feed; (2) with respect to the plan view map; and/or (3) by incrementally advancing the remote telepresence robot in a direction relative to the current position of the remote telepresence robot.
The details of one or more implementations of the disclosure are set forth in the accompanying drawings and the description below. Other aspects, features, and advantages will be apparent from the description and drawings, and from the claims.
Like reference symbols in the various drawings indicate like elements.
Autonomous driven, self-navigating robots, such as autonomous vehicles and telepresence robots, manage encounters and interactions with obstacles and other points of interest (POI) through their autonomous travels. In one embodiment of the present invention, autonomous vehicles travel trafficked routes (e.g. local roads and interstate highways and freeways) using GPS and maps having tags including POI that trigger a robot behavior routine. Such routines guide the self-navigating robot in response to the POI (e.g. turning the vehicle onto an alternate street or highway to circumvent a traffic jam by electing an alternate route during peak traffic hours), thereby efficiently and effectively traveling autonomously from a start point and to a defined end point on a map. Telepresence robots similarly manage encounters and interactions with obstacles and other POI throughout their autonomous travels through areas of frequent or occasional high traffic volumes, such as, for example, the navigable spaces of office buildings and hospitals. Telepresence robots can interact or interface with humans to provide a number of services, such as a physician or healthcare worker providing remote medical consultation, home assistance, commercial assistance, and more. In the example of home assistance, a telepresence robot can assist elderly people with everyday tasks, including, but not limited to, maintaining a medication regime, mobility assistance, communication assistance (e.g., video conferencing, telecommunications, Internet access, etc.), home or site monitoring (inside and/or outside), person monitoring, and/or providing a personal emergency response system (PERS). For commercial assistance, the telepresence robot can provide videoconferencing (e.g., in a hospital setting), a point of sale terminal, an interactive information/marketing terminal, etc.
Referring to
The robot body 110, in the examples shown, includes a base 120, at least one leg 130 extending upwardly from the base 120, and a torso 140 supported by the at least one leg 130. The base 120 may support the drive system 200. The robot body (lower portion) 110 also includes a neck 150 supported by the torso 140. The neck 150 supports a head (upper portion) 160, which supports at least a portion of the interfacing module 300. The base 120 includes enough weight (e.g., by supporting the power source 105 (batteries) to maintain a low center of gravity CGB of the base 120 and a low overall center of gravity CGR of the robot 100 for maintaining mechanical stability.
Referring to
In some implementations, the drive system 200 provides omni-directional and/or holonomic motion control of the robot 100. As used herein the term “omni-directional” refers to the ability to move in substantially any planar direction, i.e., side-to-side (lateral), forward/back, and rotational. These directions are generally referred to herein as x, y, and Oz, respectively. Furthermore, the term “holonomic” is used in a manner substantially consistent with the literature use of the term and refers to the ability to move in a planar direction with three planar degrees of freedom, i.e., two translations and one rotation. Hence, a holonomic robot has the ability to move in a planar direction at a velocity made up of substantially any proportion of the three planar velocities (lateral, forward/back, and rotational), as well as the ability to change these proportions in a substantially continuous manner.
The robot 100 can operate in human environments (e.g., environments typically designed for bipedal, walking occupants) using wheeled mobility. In some implementations, the drive system 200 includes first, second, and third drive wheels 210a, 210b, 210c equally spaced (i.e., trilaterally symmetric) about the vertical axis Z (e.g., 120 degrees apart); however, other arrangements are possible as well. Referring to
Referring to
In some examples, as illustrated in
In some implementations of the drive system 200, each drive wheel 210a, 210b, 210 has a rolling direction DR radially aligned with a vertical axis Z, which is orthogonal to X and Y axes of the robot 100. The first drive wheel 210a can be arranged as a leading drive wheel along the forward drive direction F with the remaining two drive wheels 210b, 210c trailing behind. In this arrangement, to drive forward, the controller 500 may issue a drive command that causes the first drive wheel 210a to drive in a forward rolling direction and the second and third drive wheels 210b, 210c to drive at an equal rate as the first drive wheel 210a, but in a reverse direction.
In other implementations, the drive system 200 can be arranged to have the first and second drive wheels 210a, 210b positioned such that an angle bisector of an angle between the two drive wheels 210a, 210b is aligned with the forward drive direction F of the robot 100. In this arrangement, to drive forward, the controller 500 may issue a drive command that causes the first and second drive wheels 210a, 210b to drive in a forward rolling direction and an equal rate, while the third drive wheel 210c drives in a reverse direction or remains idle and is dragged behind the first and second drive wheels 210a, 210b. To turn left or right while driving forward, the controller 500 may issue a command that causes the corresponding first or second drive wheel 210a, 210b to drive at a relatively quicker/slower rate. Other drive system arrangements can be used as well. The drive wheels 210a, 210b, 210c may define a cylindrical, circular, elliptical, or polygonal profile.
Referring again to
Generally, telescopic arrangements include successively smaller diameter extrusions telescopically moving up and out of relatively larger extrusions at the base 120 in order to keep a center of gravity CGL of the entire leg 130 as low as possible. Moreover, stronger and/or larger components can be placed at the bottom to deal with the greater torques experienced at the base 120 when the leg 130 is fully extended. This approach, however, offers two problems. First, when the relatively smaller components are placed at the top of the leg 130, any rain, dust, or other particulate tends to run or fall down the extrusions, infiltrating a space between the extrusions, thus obstructing nesting of the extrusions. This creates a very difficult sealing problem while still trying to maintain full mobility/articulation of the leg 130. Second, it may be desirable to mount payloads or accessories on the robot 100. One common place to mount accessories is at the top of the torso 140. If the second leg portion 134 moves telescopically in and out of the first leg portion, accessories and components could only be mounted above the entire second leg portion 134, if they need to move with the torso 140. Otherwise, any components mounted on the second leg portion 134 would limit the telescopic movement of the leg 130.
By having the second leg portion 134 move telescopically over the first leg portion 132, the second leg portion 134 provides additional payload attachment points that can move vertically with respect to the base 120. This type of arrangement causes water or airborne particulate to run down the torso 140 on the outside of every leg portion 132, 134 (e.g., extrusion) without entering a space between the leg portions 132, 134. This greatly simplifies sealing any joints of the leg 130. Moreover, payload/accessory mounting features of the torso 140 and/or second leg portion 134 are always exposed and available no matter how the leg 130 is extended.
Referring to
The robot 100 may include one or more accessory ports 170 (e.g., mechanical and/or electrical interconnect points) for receiving payloads. The accessory ports 170 can be located so that received payloads do not occlude or obstruct sensors of the sensor system 400 (e.g., on the bottom and/or top surfaces 144, 146 of the torso 140, etc.).
An external surface of the torso 140 may be sensitive to contact or touching by a user, so as to receive touch commands from the user. For example, when the user touches the top surface 146 of the torso 140, the robot 100 responds by lowering a height of the torso with respect to the floor (e.g., by decreasing the height of the leg(s) 130 supporting the torso 140). Similarly, when the user touches the bottom surface 144 of the torso 140, the robot 100 responds by raising the torso 140 with respect to the floor (e.g., by increasing the height of the leg(s) 130 supporting the torso 140). Moreover, upon receiving a user touch on forward, rearward, right or left portions of side surface 148 of the torso 140, the robot 100 responds by moving in a corresponding direction of the received touch command (e.g., rearward, forward, left, and right, respectively). The external surface(s) of the torso 140 may include a capacitive sensor in communication with the controller that detects user contact.
Referring again to
The head 160 may be sensitive to contact or touching by a user, so as to receive touch commands from the user. For example, when the user pulls the head 160 forward, the head 160 tilts forward with passive resistance and then holds the position. Moreover, if the user pushes/pulls the head 160 vertically downward, the torso 140 may lower (via a reduction in length of the leg 130) the head 160. The head 160 and/or neck 150 may include strain gauges and/or contact sensors 165 that sense user contact or manipulation.
In some implementations, the head 160 supports one or more portions of the interfacing module 300. The head 160 may include a dock 302 for releasably receiving one or more computing tablets 310, also referred to as a web pad or a tablet PC, each of which may have a touch screen 312. The web pad 310 may be oriented forward, rearward or upward. In some implementations, web pad 310 includes a touch screen, optional I/O (e.g., buttons and/or connectors, such as micro-USB, etc.) a processor, and memory in communication with the processor. An example web pad 310 includes the Apple iPad by Apple, Inc. In some examples, the web pad 310 functions as the controller 500 or assist the controller 500 in controlling the robot 100. The touch screen may detect, monitor, and/or reproduce points of user touching thereon for receiving user inputs and providing a graphical user interface that is touch interactive. In some examples, the web pad 310 includes a touch screen caller that allows the user to find it when it has been removed from the robot 100.
The interfacing module 300 may include a camera 320 disposed on the head 160 (see e.g.,
The robot 100 can provide videoconferencing (e.g., at 24 fps or higher) through the interface module 300 (e.g., using a web pad 310, the camera 320, the microphones 330, and/or the speakers 340). The videoconferencing can be multiparty. The robot 100 can provide eye contact between both parties of the videoconferencing by maneuvering the head 160 to face the user. Moreover, the robot 100 can have a gaze angle of <5° (e.g., an angle away from an axis normal to the forward face of the head 160). At least one three-dimensional image sensor 450 and/or the camera 320 on the robot 100 can capture life-size images including body language. The controller 500 can synchronize audio and video (e.g., with a difference of <50 ms). The camera 320 may be movable within at least 1° of freedom separately from the web pad 310. The head 160 may include one or more speakers 340 so as to have sound emanate from the head 160 near the web pad 310 displaying the videoconferencing.
The interfacing module 300 may include a microphone 330 (or micro-phone array) for receiving sound inputs and one or more speakers 340 disposed on the robot body 110 for delivering sound outputs.
Referring to
In some implementations, the sensor system 400 includes a set or an array of proximity sensors 410, 420 in communication with the controller 500 and arranged in one or more zones or portions of the robot 100 (e.g., disposed on or near the base body portion 124a, 124b, 124c of the robot body 110) for detecting any nearby or intruding obstacles. The proximity sensors 410, 420 may be converging infrared (IR) emitter-sensor elements, sonar sensors, ultrasonic sensors, and/or imaging sensors (e.g., 3D depth map image sensors) that provide a signal to the controller 500 when an object is within a given range of the robot 100.
In the example shown in
In some examples, the set of sonar proximity sensors 410 (e.g., 410a-410i) disposed around the base 124 of body 120 are arranged to point upward (e.g., substantially in the Z direction) and optionally angled outward away from the Z axis, thus creating a detection curtain 412 around the robot 100. Each sonar proximity sensor 410a-410i may have a shroud or emission guide 414 that guides the sonar emission upward or at least not toward the other portions of the robot body 110 (e.g., so as not to detect movement of the robot body 110 with respect to itself). The emission guide 414 may define a shell or half-shell shape. In the example shown, the base 124 of body 120 extends laterally beyond the leg 130, and the sonar proximity sensors 410 (e.g., 410a-410i) are disposed on the base 124 of body 120 (e.g., substantially along a perimeter of the base 124 of body 120) around the leg 130. Moreover, the upward pointing sonar proximity sensors 410 are spaced to create a continuous or substantially continuous sonar detection curtain 412 around the leg 130. The sonar detection curtain 412 can be used to detect obstacles having elevated lateral protruding portions, such as table tops, shelves, etc.
The upward looking sonar proximity sensors 410 provide the ability to see objects that are primarily in the horizontal plane, such as table tops. These objects, due to their aspect ratio, may be missed by other sensors of the sensor system, such as the laser scanner 440 or imaging sensors 450, and as such, can pose a problem to the robot 100. The upward viewing sonar proximity sensors 410 arranged around the perimeter of the base 120 provide a means for seeing or detecting those types of objects/obstacles. Moreover, the sonar proximity sensors 410 can be placed around the widest points of the base perimeter and angled slightly outwards, so as not to be occluded or obstructed by the torso 140 or head 160 of the robot 100, thus not resulting in false positives for sensing portions of the robot 100 itself. In some implementations, the sonar proximity sensors 410 are arranged (upward and outward) to leave a volume about the torso 140 outside of a field of view of the sonar proximity sensors 410 and thus free to receive mounted payloads or accessories, such as the basket 360. The sonar proximity sensors 410 can be recessed into the base body 124 to provide visual concealment and no external features to snag on or hit obstacles.
The sensor system 400 may include one or more sonar proximity sensors 410 (e.g., a rear proximity sensor 410j) directed rearward (e.g., opposite to the forward drive direction F) for detecting obstacles while backing up. The rear sonar proximity sensor 410j may include an emission guide 414 to direct its sonar detection field 412. Moreover, the rear sonar proximity sensor 410j can be used for ranging to determine a distance between the robot 100 and a detected object in the field of view of the rear sonar proximity sensor 410j (e.g., as “back-up alert”). In some examples, the rear sonar proximity sensor 410j is mounted recessed within the base 124 of body 120 so as not to provide any visual or functional irregularity in the housing form.
Referring to
The cliff proximity sensors 420 can detect when the robot 100 has encountered a falling edge of the floor, such as when it encounters a set of stairs. The controller 500 (executing a control system) may execute behaviors that cause the robot 100 to take an action, such as changing its direction of travel, when an edge is detected. In some implementations, the sensor system 400 includes one or more secondary cliff sensors (e.g., other sensors configured for cliff sensing and optionally other types of sensing). The cliff detecting proximity sensors 420 can be arranged to provide early detection of cliffs, provide data for discriminating between actual cliffs and safe events (such as climbing over thresholds), and be positioned down and out so that their field of view includes at least part of the robot body 110 and an area away from the robot body 110. In some implementations, the controller 500 executes cliff detection routine that identifies and detects an edge of the supporting work surface (e.g., floor), an increase in distance past the edge of the work surface, and/or an increase in distance between the robot body 110 and the work surface. This implementation allows: 1) early detection of potential cliffs (which may allow faster mobility speeds in unknown environments); 2) increased reliability of autonomous mobility since the controller 500 receives cliff imaging information from the cliff detecting proximity sensors 420 to know if a cliff event is truly unsafe or if it can be safely traversed (e.g., such as climbing up and over a threshold); 3) a reduction in false positives of cliffs (e.g., due to the use of edge detection versus the multiple discrete IR proximity sensors with a narrow field of view). Additional sensors arranged as “wheel drop” sensors can be used for redundancy and for detecting situations where a range-sensing camera cannot reliably detect a certain type of cliff.
Threshold and step detection allows the robot 100 to effectively plan for either traversing a climbable threshold or avoiding a step that is too tall. This can be the same for random objects on the work surface that the robot 100 may or may not be able to safely traverse. For those obstacles or thresholds that the robot 100 determines it can climb, knowing their heights allows the robot 100 to slow down appropriately, if deemed needed, to allow for a smooth transition in order to maximize smoothness and minimize any instability due to sudden accelerations. In some implementations, threshold and step detection is based on object height above the work surface along with geometry recognition (e.g., discerning between a threshold or an electrical cable versus a blob, such as a sock). Thresholds may be recognized by edge detection. The controller 500 may receive imaging data from the cliff detecting proximity sensors 420 (or another imaging sensor on the robot 100), execute an edge detection routine, and issue a drive command based on results of the edge detection routine. The controller 500 may use pattern recognition to identify objects as well. Threshold detection allows the robot 100 to change its orientation with respect to the threshold to maximize smooth step climbing ability.
The proximity sensors 410, 420 may function alone, or as an alternative, may function in combination with one or more contact sensors 430 (e.g., bump switches) for redundancy. For example, one or more contact or bump sensors 430 on the robot body 110 can detect if the robot 100 physically encounters an obstacle. Such sensors may use a physical property such as capacitance or physical displacement within the robot 100 to determine when it has encountered an obstacle. In some implementations, each base body portion 124a, 124b, 124c of the base 120 has an associated contact sensor 430 (e.g., capacitive sensor, read switch, etc.) that detects movement of the corresponding base body portion 124a, 124b, 124c with respect to the base chassis 122 (see e.g.,
Referring again to
The laser scanner 440 scans an area about the robot 100 and the controller 500, using signals received from the laser scanner 440, and creates an environment map or object map of the scanned area. The controller 500 may use the object map for navigation, obstacle detection, and obstacle avoidance. Moreover, the controller 500 may use sensory inputs from other sensors of the sensor system 400 for creating an object map and/or for navigation.
In some examples, the laser scanner 440 is a scanning LIDAR, which may use a laser that quickly scans an area in one dimension, as a “main” scan line, and a time-of-flight imaging element that uses a phase difference or similar technique to assign a depth to each pixel generated in the line (returning a two-dimensional depth line in the plane of scanning). In order to generate a three-dimensional map, the LIDAR can perform an “auxiliary” scan in a second direction (for example, by “nodding” the scanner). This mechanical scanning technique can be complemented, if not supplemented, by technologies such as the “Flash” LIDAR/LADAR and “Swiss Ranger” type focal plane imaging element sensors, techniques which use semiconductor stacks to permit time of flight calculations for a full two-dimensional matrix of pixels to provide a depth at each pixel, or even a series of depths at each pixel (with an encoded illuminator or illuminating laser).
The sensor system 400 may include one or more three-dimensional image sensors 450 in communication with the controller 500. If the three-dimensional image sensor 450 has a limited field of view, the controller 500 or the sensor system 400 can actuate the three-dimensional image sensor 450a in a side-to-side scanning manner to create a relatively wider field of view to perform robust obstacle detection/obstacle avoidance (ODOA). Referring to
In some implementations, the sensor system 400 includes additional three-dimensional image sensors 450 disposed on the base 124 of body 120, the leg 130, the neck 150, and/or the head 160. In the example shown in
A forward facing three-dimensional image sensor 450 disposed on the neck 150 and/or the head 160 can be used for person, face, and/or gesture recognition of people about the robot 100. For example, using signal inputs from the three-dimensional image sensor 450 on the head 160, the controller 500 may recognize a user by creating a three-dimensional map of the viewed/captured user's face and comparing the created three-dimensional map with known three-dimensional images of people's faces and determining a match with one of the known three-dimensional facial images. Facial recognition may be used for validating users as allowable users of the robot 100. Moreover, one or more of the three-dimensional image sensors 450 can be used for determining gestures of a person viewed by the robot 100, and optionally reacting based on the determined gesture(s) (e.g., hand pointing, waving, and/or hand signals). For example, the controller 500 may issue a drive command in response to a recognized hand pointing in a particular direction.
The three-dimensional image sensors 450 may be capable of producing the following types of data: (i) a depth map, (ii) a reflectivity based intensity image, and/or (iii) a regular intensity image. The three-dimensional image sensors 450 may obtain such data by image pattern matching, measuring the flight time and/or phase delay shift for light emitted from a source and reflected off of a target.
In some implementations, reasoning or control software, executable on a processor (e.g., of the robot controller 500), uses a combination of algorithms executed using various data types generated by the sensor system 400. The reasoning software processes the data collected from the sensor system 400 and outputs data for making navigational decisions on where the robot 100 can move without colliding with an obstacle, for example. By accumulating imaging data over time of the robot's surroundings, the reasoning software can in turn apply effective methods to selected segments of the sensed image(s) to improve depth measurements of the three-dimensional image sensors 450. This may include using appropriate temporal and spatial averaging techniques.
The reliability of executing robot collision-free moves may be based on: (i) a confidence level built by high-level reasoning over time and (ii) a depth-perceptive sensor that accumulates three major types of data for analysis: (a) a depth image, (b) an active illumination image and (c) an ambient illumination image. Algorithms cognizant of the different types of data can be executed on each of the images obtained by the depth-perceptive image sensor 450. The aggregate data may improve the confidence level compared to a system using only one of the kinds of data.
The three-dimensional image sensors 450 may obtain images containing depth and brightness data from a scene about the robot 100 (e.g., a sensor view portion of a room or work area) that contains one or more objects. The controller 500 may be configured to determine occupancy data for the object based on the captured reflected light from the scene. Moreover, the controller 500, in some examples, issues a drive command to the drive system 200 based at least in part on the occupancy data to circumnavigate obstacles (i.e., the object in the scene). The three-dimensional image sensors 450 may repeatedly capture scene depth images for real-time decision-making by the controller 500 to navigate the robot 100 about the scene without colliding into any objects in the scene. For example, the speed or frequency in which the depth image data is obtained by the three-dimensional image sensors 450 may be controlled by a shutter speed of the three-dimensional image sensors 450. In addition, the controller 500 may receive an event trigger (e.g., from another sensor component of the sensor system 400, such as proximity sensor 410, 420, notifying the controller 500 of a nearby object or hazard. The controller 500, in response to the event trigger, can cause the three-dimensional image sensors 450 to increase a frequency at which depth images are captured and occupancy information is obtained.
In some implementations, the robot includes a sonar scanner 460 for acoustic imaging of an area surrounding the robot 100. In the examples shown in
Referring to
The second three-dimensional image sensor 450b is mounted on the head 160, which can pan and tilt via the neck 150. The second three-dimensional image sensor 450b can be useful for remote driving since it allows a human operator to see where the robot 100 is going. The neck 150 enables the operator tilt and/or pan the second three-dimensional image sensor 450b to see both close and distant objects. Panning the second three-dimensional image sensor 450b increases an associated horizontal field of view. During fast travel, the robot 100 may tilt the second three-dimensional image sensor 450b downward slightly to increase a total or combined field of view of both three-dimensional image sensors 450a, 450b, and to give sufficient time for the robot 100 to avoid an obstacle (since higher speeds generally mean less time to react to obstacles). At slower speeds, the robot 100 may tilt the second three-dimensional image sensor 450b upward or substantially parallel to the ground G to track a person that the robot 100 is meant to follow. Moreover, while driving at relatively low speeds, the robot 100 can pan the second three-dimensional image sensor 450b to increase its field of view around the robot 100. The first three-dimensional image sensor 450a can stay fixed (e.g., not moved with respect to the base 120) when the robot is driving to expand its perceptual range. Additionally and/or alternatively, the first three-dimensional image sensor 450a can scan at low speeds in order to detect potential obstacles around the robot when it is maneuvering. In some examples, the height of the first three-dimensional image sensor 450a can be adjusted upward, such as through the use of a Z-lift, in order to optimize the field of view of the first three-dimensional sensor 450a.
In some implementations, at least one of three-dimensional image sensors 450 can be a volumetric point cloud imaging device (such as a speckle or time-of-flight camera) positioned on the robot 100 at a height of greater than one or two feet above the ground (or at a height of about one or two feet above the ground) and directed to obtain a point cloud from a volume of space including a floor plane in a direction of movement of the robot (via the omni-directional drive system 200). In the examples shown in
Referring to
The controller 500 may monitor any deviation in feedback from the IMU 470 from a threshold signal corresponding to normal unencumbered operation. For example, if the robot begins to pitch away from an upright position, it may be “clotheslined” or otherwise impeded, or someone may have suddenly added a heavy payload. In these instances, it may be necessary to take urgent action (including, but not limited to, evasive maneuvers, recalibration, and/or issuing an audio/visual warning) in order to assure safe operation of the robot 100.
Since robot 100 may operate in a human environment, it may interact with humans and operate in spaces designed for humans (and without regard for robot constraints). The robot 100 can limit its drive speeds and accelerations when in a congested, constrained, or highly dynamic environment, such as at a cocktail party or busy hospital. However, the robot 100 may encounter situations where it is safe to drive relatively fast, as in a long empty corridor, but yet be able to decelerate suddenly, as when something crosses the robot's motion path.
When accelerating from a stop, the controller 500 may take into account a moment of inertia of the robot 100 from its overall center of gravity CGR to prevent robot tipping. The controller 500 may use a model of its pose, including its current moment of inertia. When payloads are supported, the controller 500 may measure a load impact on the overall center of gravity CGR and monitor movement of the robot moment of inertia. For example, the torso 140 and/or neck 150 may include strain gauges to measure strain. If this is not possible, the controller 500 may apply a test torque command to the drive wheels 210 and measure actual linear and angular acceleration of the robot using the IMU 470, in order to experimentally determine safe limits.
During a sudden deceleration, a commanded load on the second and third drive wheels 210b, 210c (the rear wheels) is reduced, while the first drive wheel 210a (the front wheel) slips in the forward drive direction and supports the robot 100. If the loading of the second and third drive wheels 210b, 210c (the rear wheels) is asymmetrical, the robot 100 may “yaw” which will reduce dynamic stability. The IMU 470 (e.g., a gyro) can be used to detect this yaw and command the second and third drive wheels 210b, 210c to reorient the robot 100.
Referring to
The applications 520 can be stored in memory of or communicated to the robot 100, to run concurrently on (e.g., a processor) and simultaneously control the robot 100. The applications 520 may access behaviors 512 of the behavior system 510a. The independently deployed applications 520 are combined dynamically at runtime and share robot resources 530 (e.g., drive system 200, arm(s), head(s), etc.) of the robot 100. A low-level policy is implemented for dynamically sharing the robot resources 530 among the applications 520 at run-time. The policy determines which application 520 has control of the robot resources 530 required by that application 520 (e.g. it creates a priority hierarchy among the applications 520). Applications 520 can start and stop dynamically and run completely independently of each other. The control system 510 also allows for complex behaviors 512 which can be combined together to assist each other.
The control arbitration system 510b includes one or more resource controllers 540, a robot manager 550, and one or more control arbiters 560. These components do not need to be in a common process or computer, and do not need to be started in any particular order. The resource controller 540 component provides an interface to the control arbitration system 510b for applications 520. There is an instance of this component for every application 520. The resource controller 540 abstracts and encapsulates away the complexities of authentication, distributed resource control arbiters, command buffering, and the like. The robot manager 550 coordinates the prioritization of applications 520, by controlling which application 520 has exclusive control of any of the robot resources 530 at any particular time. Since this is the central coordinator of information, there is only one instance of the robot manager 550 per robot. The robot manager 550 implements a priority policy, which has a linear prioritized order of the resource controllers 540, and keeps track of the resource control arbiters 560 that provide hardware control. The control arbiter 560 receives the commands from every application 520 generates a single command based on the applications' priorities and publishes it for its associated resources 530. The control arbiter 560 also receives state feedback from its associated resources 530 and sends it back up to the applications 520. The robot resources 530 may be a network of functional modules (e.g. actuators, drive systems, and groups thereof) with one or more hardware controllers. The commands of the control arbiter 560 are specific to the resource 530 to carry out specific actions.
A dynamics model 570 executable on the controller 500 can be configured to compute the center of gravity (CG), moments of inertia, and cross products of inertia of various portions of the robot 100 for the assessing a current robot state. The dynamics model 570 may also model the shapes, weight, and/or moments of inertia of these components. In some examples, the dynamics model 570 communicates with the IMU 470 or portions of one (e.g., accelerometers and/or gyros) disposed on the robot 100 and in communication with the controller 500 for calculating the various centers of gravity of the robot 100. The dynamics model 570 can be used by the controller 500, along with other programs 520 or behaviors 512 to determine operating envelopes of the robot 100 and its components.
Each application 520 has an action selection engine 580 and a resource controller 540, one or more behaviors 512 connected to the action selection engine 580, and one or more action models 590 connected to action selection engine 580. The behavior system 510a provides predictive modeling and allows the behaviors 512 to collaboratively decide on the robot's actions by evaluating possible outcomes of robot actions. In some examples, a behavior 512 is a plug-in component that provides a hierarchical, state-full evaluation function that couples sensory feedback from multiple sources with a-priori limits and information into evaluation feedback on the allowable actions of the robot. Since the behaviors 512 can be plugged into the application 520 (e.g., residing inside or outside of the application 520), they can be removed and added without having to modify the application 520 or any other part of the control system 510. Each behavior 512 is a standalone policy. To make behaviors 512 more powerful, it is possible to attach the output of multiple behaviors 512 together into the input of another so as to have complex combination functions. The behaviors 512 are intended to implement manageable portions of the total cognizance of the robot 100.
The action selection engine 580 is the coordinating element of the control system 510 and runs a fast, optimized action selection cycle (prediction/correction cycle) searching for the best action given the inputs of all the behaviors 512. The action selection engine 580 has three phases: nomination, action selection search, and completion. In the nomination phase, each behavior 512 is notified that the action selection cycle has started and is provided with the cycle start time, the current state, and limits of the robot actuator space. Based on internal policy or external input, each behavior 512 decides whether or not it wants to participate in this action selection cycle. During this phase, a list of active behavior primitives is generated whose input will affect the selection of the commands to be executed on the robot 100.
In the action selection search phase, the action selection engine 580 generates feasible outcomes from the space of available actions, also referred to as the action space. The action selection engine 580 uses the action models 590 to provide a pool of feasible commands (within limits) and corresponding outcomes as a result of simulating the action of each command at different time steps with a time horizon in the future. The action selection engine 580 calculates a preferred outcome, based on the outcome evaluations of the behaviors 512, and sends the corresponding command to the control arbitration system 510b and notifies the action model 590 of the chosen command as feedback.
In the completion phase, the commands that correspond to a collaborative best scored outcome are combined together as an overall command, which is presented to the resource controller 540 for execution on the robot resources 530. The best outcome is provided as feedback to the active behaviors 512, to be used in future evaluation cycles.
Received sensor signals from the sensor system 400 can cause interactions with one or more behaviors 512 to execute actions. For example, using the control system 510, the controller 500 selects an action (or move command) for each robotic component (e.g., motor or actuator) from a corresponding action space (e.g., a collection of possible actions or moves for that particular component) to effectuate a coordinated move of each robotic component in an efficient manner that avoids collisions with itself and any objects about the robot 100, which the robot 100 is aware of. The controller 500 can issue a coordinated command over robot network, such as an EtherIO network, as described in U.S. Ser. No. 61/305,069, filed Feb. 16, 2010, the entire contents of which are hereby incorporated by reference.
Referring to
Referring to
A dashboard 640 may provide information regarding the orientation of the robot 100, an indication of the robot's battery charge, an indication of the strength of a wireless data signal, and/or an indication of the network quality. The orientation of the robot 100 may be indicated by an icon 642 displaying the orientation of the head 160 of the robot 100 with respect to the torso 140 or the base 120. Such an indication may assist a user in orienting the robot 100 to view items of interest. The range of motion of the robot head 160 may be limited. Accordingly, certain implementations may display an indication of the rotational position of the head 160 and a range of motion of the head 160.
Media controls 647 may allow the user to interact with the patient 614 using various types of media and to acquire and store media documenting the interactions of the user and the patient 614. The media controls 647 may allow the user to play audio and/or video clips, for example, that may be used to educate the patient 614 about a medical condition or procedure. Still photographs may be acquired using a camera 320, 450 of the robot 100 in order to document various conditions. Further, the robot 100 may acquire audio (e.g., using the microphone 330) or video (e.g., using the camera 320) documenting the user's interaction with the patient 614 and optionally storing the acquired audio/video in memory of the controller 500 and/or transmitting the acquired audio/video to a remote device or cloud service.
In some implementations, the media controls 647 allow the user to manage temporary connectivity issues. For example, upon the unexpected disconnection of a session, video recording may begin. The robot 100 may continue recording video and saving it to local memory, such as that of the controller 500. Upon an unexpected disconnection, a message may be displayed by the robot, such as “Session terminated—Video recording continuing . . . .” A button below may be displayed with a caption of “Stop recording.” A nurse on the robot side may touch the “Stop recording” button (e.g., on the touch screen 312) and terminate the local recording. Otherwise, the recording may continue for a specified time interval. If the same user logs back into the robot 100 in the specified time interval, the record button on the remote station may show that recording is in progress. When the robot's local recording is complete, it may begin to transmit the video file to a remote station or other location that may be accessible to the disconnected user. Accordingly, the user may be able to see what events transpired during the time that the session was interrupted.
In the example shown in
Alternative screen layouts may be displayed to a user as may be appropriate for the task being performed by the user. In the example shown in
A map view switch button 645 of the user interface may allow the user to invoke the alternative user interface 605b that includes a relatively larger map window 620. For example, the user interface 605b shown in
Referring to
Simultaneous localization and mapping (SLAM) technology may utilize laser range scanners, odometry, acoustic range finders, or all of the above to build a map of the local environment and place the robot 100 on the map. Images recorded by a robot 100 (e.g., via the camera 320 or three-dimensional image sensor 450) as it traverses an environment may be stored in an internal database (e.g., of the controller 500) and/or a remote database (e.g., a cloud service). When the robot 100 reacquires an image currently in the database, the algorithm resets the robot's current position to that which was recorded when the landmark was originally entered in the database. This method helps to counter the inherent drift of wheel encoder odometry. Systems may also utilize RFID chips and/or triangulation of wireless access points. Further, the names or identifying numbers of specific rooms may be associated with locations on the map. Image data can accumulate over time and, as a cost savings or space savings, the robot 100 may use remote data storage and/or remote processing for storing and/or processing the image data, respectively. For example, an RFID reader may detect RFID chips associated with coordinates on a plan view map in order to identify a current location of a robot. An “RFID chip” may include an RFID device or an RFID “tag” as is understood by those having skill in the art. The RFID chips may be embodied as passive, active, or battery assisted passive (BAP) RFID chips.
The cloud 720 provides cloud computing and/or cloud storage capabilities. Cloud computing may provide Internet-based computing, whereby shared servers provide resources, software, and data to computers and other devices on demand. For example, the cloud 720 may be a cloud computing service that includes at least one server computing device, which may include a service abstraction layer and a hypertext transfer protocol wrapper over a server virtual machine instantiated thereon. The server computing device may be configured to parse HTTP requests and send HTTP responses. Cloud computing may be a technology that uses the Internet and central remote servers to maintain data and applications. Cloud computing can allow users to access and use applications 710 without installation and to access personal files at any computer with Internet access. Cloud computing allows for relatively more efficient computing by centralizing storage, memory, processing and bandwidth. The cloud 720 can provide scalable, on-demand computing power, storage, and bandwidth, while reducing robot hardware requirements (e.g., by freeing up CPU and memory usage). Robot connectivity to the cloud 720 allows automatic data gathering of robot operation and usage histories without requiring the robot 100 to return to a base station. Moreover, continuous data collection over time can yield a wealth of data that can be mined for marketing, product development, and support.
Cloud storage 722 can be a model of networked computer data storage where data is stored on multiple virtual servers, generally hosted by third parties. By providing communication between the robot 100 and the cloud 720, information gathered by the robot 100 can be securely viewed by authorized users via a web-based information portal.
The portal 730 may be a web-based user portal for gathering and/or providing information, such as personal information, home status information, and robot status information. Information can be integrated with third-party information to provide additional functionality and resources to the user and/or the robot 100. The robot system architecture 700 can facilitate proactive data collection. For example, applications 710 executed on the computing device 310 may collect data and report on actions performed by the robot 100 and/or a person or an environment viewed by the robot 100 (using the sensor system 400). This data can be a unique property of the robot 100.
“Dense data” vs. “sparse data” and “dense features” vs. “sparse features” are referred to herein with respect to spatial data sets. Without limiting or narrowing the meaning from that of how those skilled in the art would interpret such terms to mean, “dense” vs. “sparse” generally means many data points per spatial representation vs. few data points, and specifically may mean:
(i) in the context of two-dimensional image data or three-dimensional “images” including two-dimensional data and range, “dense” image data includes image data substantially fully populated with pixels, or capable of being rasterized to pixels with substantially no losses and/or artifacting from the original image capture (including substantially uncompressed, raw, or losslessly compressed images), while a “sparse” image is one where the image is quantized, sampled, lossy compressed, vectorized, segmented (e.g., into superpixels, nodes, edges, surfaces, interest points, voxels), or otherwise materially reduced in fidelity from the original capture, or must be interpolated in being rasterized to pixels to re-represent an image;
(ii) in the context of two-dimensional or three-dimensional features, “dense features” may be features that are populated in a substantially unconstrained manner, to the resolution of the detection approach, all that can be detected and recorded, and/or features that are recognized by detectors recognized to collect many features (HOG, wavelets) over a sub-image; “sparse features” may be purposefully constrained in number, in the number of feature inputs, lateral inhibition, and/or feature selection, and/or may be recognized by detectors recognized to identify a limited number of isolated points in an image (Harris corner, edges, Shi-Tomasi).
With respect to three-dimensional environment structure, the robot 100 may acquire images, such as dense images 701, of a scene 10 about the robot 100 while maneuvering about a work surface 5. In some implementations, the robot 100 uses a camera 320 and/or an image sensor 450 (e.g., volumetric point cloud imaging device) for obtaining the dense images 701. The controller 500, which is in communication with the camera 320 and/or the image sensor 450 may associate information with the dense images 701 (e.g., mark-up or tag the dense images 701 with data), such as accelerometer data traces, odometry data, and/or other data from the sensor system 400 along with timestamps. In some examples, the robot 100 captures a streaming sequence of dense images 701 and marks the dense image sequence with mark-up data, providing a marked-up dense image sequence. The cloud service 720 may process the received image data 701 and return a processed data set to the robot controller 500, which may issue drive commands to the drive system 200 based on the received processed data set for maneuvering about the scene 10.
The cloud service 720 may execute one of a variety of off-line methods to process a stored image data set 703 into a dense three-dimensional map or model 705 of the scene 10 (environment) and then simplify this dense three-dimensional map or model 705 into a two-dimensional height map 707, which can be a two-dimensional map with height data at each point (e.g., similar to a two-dimensional topographical map). In some examples, the two-dimensional height map 707 is a topographical map having X and Y coordinates with Z data. Each X,Y coordinate may have one or more Z points (i.e., height data). Unlike the dense three-dimensional map, which may have numerous Z points (e.g., hundreds or thousands of Z points) for each X,Y coordinate, the two-dimensional height map 707 may have less than threshold number of Z points for each X,Y coordinate, such as between 2 and 20 (e.g., 10) points. A two-dimensional height map 707 derived from a three-dimensional map of a table in a room may show a first Z point for the bottom surface of a table top and a second Z point for the top surface of the table top for each X,Y coordinate along the table. This information allows the robot 100 to determine if it can pass under the table top. By reducing the Z points from a dense data set of a continuous range of Z points for each X,Y coordinate to a sparse data set of a select number of Z points indicative of a detected objects 12, the robot 100 can receive a two-dimensional height map 707 having a relatively smaller size than the three-dimensional map used by the cloud service 720. This, in turn, allows the robot 100 to store the two-dimensional height map 707 on local memory having a practical and cost effective size as compared to the scalable memory space available to the cloud service 720. The robot 100 receives the two-dimensional height map 707 from the cloud 720, which provides the robot 100 and associated controller 500 with navigational data for future work in the scene 10.
Additional methods and features of three-dimensional map data compression are disclosed in “Multi-Level Surface Maps for Outdoor Terrain Mapping and Loop Closing” by R. Triebel, P. Pfaff and W. Burgard; IEEE/RSJ International Conference on Intelligent Robots and Systems, 2006, which is hereby incorporated by reference in its entirety.
The cloud 720 provides the robot 100 with on-demand scaling of resources (e.g., computational, processing, memory, etc.) that may not otherwise be practical or cost effective on the robot 100. For example, the cloud 720 can provide scalable cloud storage 722 that scales up to a first size for storing and/or processing a relatively large amount of data 701, which may only be used for a short period of time and then discarded, and then scaled back down to a second size. Moreover, the cloud 720 can provide computer processing power for executing relatively complex computations or “brute force” algorithms that might not otherwise be possible on the robot. By displacing computer processing power and memory to a scalable cloud 720, the robot 100 can use a controller 500 having relatively less computing power and memory, thus providing a cost effective solution. Moreover, the robot 100 may execute real-time tasks (on the controller 500 or the web pad 310), such as obstacle avoidance, while passing non-real-time or non-time-sensitive tasks to the cloud 720 for processing and later retrieval.
The cloud 720 may execute one or more filters (e.g., a Bundle Adjustment, RANSAC, Expectation Maximization, SAM or other 3D structural estimation algorithms) for processing the stored image data set 703 into a 3D representation. Once processed and a dense three-dimensional map 705 has been created or updated, the image data set 703 can be discarded from the cloud storage 722, freeing up resources and allowing the cloud 720 to scale accordingly. As a result, the robot 100 needs neither the on-board storage nor the processing to handle the storage and processing of the image data set 703, due to the use of cloud based resources. The cloud 720 may return processed navigational data 701 or a map 707 (e.g., a compressed two-dimensional height map) to the robot 100, which it can then use for relatively simpler localization and navigation processing.
Additional methods and features of three-dimensional reconstruction are disclosed in “3D Models from Extended Uncalibrated Video Sequences: Addressing Key-frame Selection and Projective Drift” by J. Repko and M. Pollefeys; Fifth International Conference on three-dimensional Digital Imaging and Modeling, 2005, which is hereby incorporated by reference in its entirety.
Referring to
The controller 500 may communicate the occupancy map 800 to the telepresence software application 601 for displaying a map 622 in the user interface 605. The user interface map 622 may be derived partly or wholly from the occupancy map 800. Moreover, referring also to
Referring again to
The controller 500 may execute a scale-invariant feature transform (SIFT) to detect and describe local features in captured images. For any object 12 in an image, interesting points on the object 12 can be extracted to provide a “feature description” of the object 12. This description, extracted from a training image, can then be used to identify the object 12 when attempting to locate the object 12 in a test image containing many other objects. To perform reliable recognition, it is important that the features extracted from the training image be detectable even under changes in image scale, noise and illumination. Such points usually lie on high-contrast regions of the image, such as object edges. For object recognition and detection, the robot 100 may use a SIFT to find distinctive key points that are invariant to location, scale and rotation, and robust to affine transformations (changes in scale, rotation, shear, and position) and changes in illumination. In some implementations, the robot 100 captures multiple images (using the camera 320 and/or image sensor 450) of a scene 10 or object 12 (e.g., under different conditions, from different angles, etc.) and stores the images, such as in a matrix. The robot 100 can access the stored images to identify a new image by comparison, filter, etc. For example, SIFT features can be obtained from an input image and matched to a SIFT feature database obtained from training images (captured previously). The feature matching can be done through a Euclidean-distance based nearest neighbor approach. A Hough transform may be used to increase object identification by clustering those features that belong to the same object and reject the matches that are left out in the clustering process. A speeded up robust feature (SURF) may be a robust image detector and descriptor.
In addition to localization of the robot 100 in the scene 10 (e.g., the environment about the robot 100), the robot 100 may travel to other points in a connected space (e.g., the work surface 5) using the sensor system 400. The robot 100 may include a short range type of image sensor 450a (e.g., mounted on the underside of the torso 140, as shown in
In some implementations, a second object 12b of interest, located behind a detected first object 12a in the scene 10, may be initially undetected as an occlusion 16 in the scene 10. An occlusion 16 can be an area in the scene 10 that is not readily detectable or viewable by the image sensor 450, 450a, 450b. In the example shown, the sensor system 400 (e.g., or a portion thereof, such as image sensor 450, 450a, 450b) of the robot 100 has a field of view 452 with a viewing angle θV (which can be any angle between 0 degrees and 360 degrees) to view the scene 10. In some examples, the image sensor 450 includes omni-directional optics for a 360 degree viewing angle θV while in other examples, the image sensor 450, 450a, 450b has a viewing angle θV of less than 360 degrees (e.g., between about 45 degrees and 180 degrees). In examples, where the viewing angle θV is less than 360 degrees, the image sensor 450, 450a, 450b (or components thereof) may rotate with respect to the robot body 110 to achieve a viewing angle θV of 360 degrees. In some implementations, the image sensor 450, 450a, 450b or portions thereof, can move with respect to the robot body 110 and/or drive system 200. Moreover, in order to detect the second object 12b, the robot 100 may move the image sensor 450, 450a, 450b by driving about the scene 10 in one or more directions (e.g., by translating and/or rotating on the work surface 5) to obtain a vantage point that allows detection of the second object 12b. Robot movement or independent movement of the image sensor 450, 450a, 450b, or portions thereof, may resolve monocular difficulties as well.
A confidence level may be assigned to detected locations or tracked movements of objects 12 in the working area 5. For example, upon producing or updating the occupancy map 800, the controller 500 may assign a confidence level for each object 12 on the map 800. The confidence level can be directly proportional to a probability that the object 12 is actually located in the working area 5 as indicated on the map 800. The confidence level may be determined by a number of factors, such as the number and type of sensors used to detect the object 12. For example, the contact sensor 430 may provide the highest level of confidence, as the contact sensor 430 senses actual contact with the object 12 by the robot 100. The image sensor 450 may provide a different level of confidence, which may be higher than the proximity sensor 430. Data received from more than one sensor of the sensor system 400 can be aggregated or accumulated for providing a relatively higher level of confidence over any single sensor.
Odometry is the use of data from the movement of actuators to estimate change in position over time (distance traveled). In some examples, an encoder is disposed on the drive system 200 for measuring wheel revolutions, therefore a distance traveled by the robot 100. The controller 500 may use odometry in assessing a confidence level for an object location. In some implementations, the sensor system 400 includes an odometer and/or an angular rate sensor (e.g., gyroscope or the IMU 470) for sensing a distance traveled by the robot 100. A gyroscope is a device for measuring or maintaining orientation, based on the principles of conservation of angular momentum. The controller 500 may use odometry and/or gyro signals received from the odometer and/or angular rate sensor, respectively, to determine a location of the robot 100 in a working area 5 and/or on an occupancy map 800. In some examples, the controller 500 uses dead reckoning. Dead reckoning is the process of estimating a current position based upon a previously determined position, and advancing that position based upon known or estimated speeds over elapsed time, and course. By knowing a robot location in the working area 5 (e.g., via odometry, gyroscope) as well as a sensed location of one or more objects 12 in the working area 5 (via the sensor system 400), the controller 500 can assess a relatively higher confidence level of a location or movement of an object 12 on the occupancy map 800 and in the working area 5 (versus without the use of odometry or a gyroscope).
Odometry based on wheel motion can be electrically noisy. The controller 500 may utilize scan matching in conjunction with or in place of wheel odometry. The use of scan matching may improve accuracy and/or reduce the computational burden. In such an embodiment, two partial maps obtained using LIDAR and/or other mapping methods may be merged into a single map. The two or more partial maps may be merged using a known scanning location. Alternatively, two or more partial maps may be merged using geometrical features of the partial scans. The controller 500 may receive image data from the image sensor 450 of the environment or scene 10 about the robot 100 for computing robot motion, independently of wheel based odometry of the drive system 200, through visual odometry. Visual odometry may entail using optical flow to determine the motion of the image sensor 450. The controller 500 can use the calculated motion based on imaging data of the image sensor 450 for correcting any errors in the wheel based odometry, thus allowing for improved mapping and motion control. Visual odometry may have limitations with low-texture or low-light scenes 10, if the image sensor 450 cannot track features within the captured image(s).
Other details and features on odometry and imaging systems, which may be combinable with those described herein, can be found in U.S. Pat. No. 7,158,317 (describing a “depth-of field” imaging system), and U.S. Pat. No. 7,115,849 (describing wavefront coding interference contrast imaging systems), the contents of which are hereby incorporated by reference in their entireties.
Referring to
In another example, other GPS enabled vehicles, autonomous or piloted, traveling in the same geographic area as an autonomous vehicle robot may provide detailed travel condition information actively or passively at a defined map coordinate or series of coordinates mapped to a route or portion of a route. Such information may include travel speeds along a route, predicted and actual traffic conditions, and other POI that may be shared with the autonomous map accessed by the autonomous vehicle. The shared information may generate tags at locations on the map that trigger predetermined robot functions.
At the time of tagging, the robot 100 may store the tag, with a point on the plan view map 810 and a corresponding point on a robot map 820, such as the one shown in
Using the sensor system 400, the robot 100 may build the robot map 820 as it moves around. For example, the sensor system 400 can provide information on how far the robot 100 has moved and a direction of travel. The robot map 820 may include fixed obstacles in addition to the walls provided in the plan view map 810. The robot 100 may use the robot map 820 to execute autonomous navigation. In the robot map 820, the “walls” may not look perfectly straight, for example, due to detected packing crates along the wall in the corresponding hallway and/or furniture detected inside various cubicles. Moreover, rotational and resolution differences may exist between the plan view map 810 and the robot map 820.
Referring to
In the description herein, a robot refers to any autonomously driven robot having sensors, one or more processors, one or more remote communications capabilities (e.g. wifi, 4G, GPS, etc.) and mechanical drive elements. For example, in some embodiments, the term “robot” may refer to a telepresence robot such as the embodiments described above, or the term “robot” may refer to an autonomously driven, self-navigating indoor or outdoor vehicle, including partially autonomous or autonomous passenger or cargo automobiles and/or trucks. The tags and robot action-modifiers described herein apply to autonomously driven robots based at least in part on map triggers having associated robot-action modifiers (e.g., predetermined functions).
The user, a remote terminal, the robot, and/or other robots or sensors statically or dynamically occupying the same space, may insert tags 662, actively or passively, onto specific locations of the plan view map 810 and/or robot map 820 to mark map locations with information, such as driving hazards, obstacles, robot aids, etc. In one embodiment, active tag placement involves a user inserting tags on a user interface, for example the robot touch screen displaying a plan view map 810 or at a remote terminal. For example, the user may drag-and-drop tags 662 onto specific location of the plan view map 810. In various embodiments, passive tag placement on the plan view map 810 includes a remote terminal in wireless communication with sensors transmitting sensor data continuously or in defined intervals. The sensor data comprises information associated at least in part with environmental conditions (e.g., lighting conditions, traffic volume, time of day associated with high traffic volume, etc.). With regard to monitoring traffic, the sensors may monitor the special and temporal frequency of occurrences of dynamic obstacles, such as, for example, humans, vehicles, and other robots. In some embodiments, the remote terminal may employ GPS tracking of a plurality of autonomous robots occupying a single travel route or mapped area, such as the area represented by the plan view map 810. By monitoring robot speed and other conditions evaluated by onboard robot sensors, the remote terminal may create time-dependent tags indicative of travel conditions along a route. In embodiments, the remote terminal acts as a nexus for the plurality of robots in communication therewith such that the time-dependent tags are available to the plurality of robots. Each of the plurality of robots thereby benefits from the robot-action modifiers included in the data of the universally shared, time-dependent tags.
As is described herein, the tags may include tag coordinates associated with a point or a region, tag information defining the purpose of the tag, type of the tag, nature of the tag, instructions for the user and/or robot related to the tag, and/or other information relevant to the tag, and finally, the tag may include a tag annotation comprising a two-dimensional and/or three-dimensional graphic or text corresponding to the tag. An example of a tag annotation is an octagonal red stop sign associated with a tag containing tag information indicating an area that a robot should not enter. A tag annotation may be human and/or machine interpretable. Tag coordinates may be points, lines, planes, surfaces, volumes, and/or 2.5D or hybrid surfaces. Tags may be formed as data structures having any number of additional fields and/or parameters. For example, a tag may include fields associated with time, scheduling, spatial coordinates, and/or triggers for predetermined functions.
The term annotation, as used herein, includes text, words, or other verbal representations. Accordingly, the tag annotation may be a picture, graphical image, a pictograph, a hieroannotation, a non-verbal symbol. In addition, a tag annotation may be a word, a letter, a phrase, or other text form. For example, a tag associated with a nurse's station may include a tag annotation comprising a textual representation of the nurse's name. The textual representation of the nurse's name could be two-dimensional text, or it could be a three-dimensional text. Alternatively, the tag annotation associated with the nurse's station could be a large letter N, or a symbol representative of a nurse's station (e.g., a nurse's hat or nursing symbol).
The tags 662 may include a wireless local area network (WLAN) warm tag 662a denoting an area having relatively good signal reception and a WLAN cold tag 662b denoting an area having relatively poor signal reception. The robot 100 may use this information to navigate from one location to another through an area having relatively good wireless signal reception, while avoiding areas having relatively poor wireless signal reception.
A low traffic tag 662c denotes an area having relatively low traffic (person and/or robot). The robot 100 may select a travel path through an area having a relatively low traffic volume, rather than through an area having a relatively high traffic volume. Moreover, if the robot 100 must travel through a high traffic area, the robot 100 may execute one or more specific object detection obstacle avoidance (ODOA) behaviors to navigate the area successfully without collisions.
A dock tag 662d denotes a location of a robot docking station. A low battery event may signal the controller 500 to seek recharging. The robot 100 may use the map location tagged with a dock tag 662d to locate a robot docking station for recharging. For example, by applying a resolved distortion between the plan view map 810 and the robot map 820 (
Some tags 662 may be used to indicate obstacles or special traversal areas. For example, a glass tag 662e indicates the location of a glass wall, window, or door. The robot 100 may use this information to avoid the tagged glass structures, since infrared proximity sensors may not detect them. A ramp tag 662f indicates the location of a floor ramp. For a distance, the robot 100 may detect the ramp as an obstacle, since it may appear to have a vertical height greater than a threshold traversal height. When approaching a tagged ramp, the robot 100 may execute a ramp or traversal behavior to successfully negotiate the ramp. A tight tag 662g indicates the location of a relatively narrow corridor or throughway. The robot 100 may avoid such areas, so as to avoid any confining situations.
A slow tag 662h indicates a location or area in which the robot 100 drives relatively slowly. This location or area may coincide with a high traffic area. An avoid tag 662i denotes a location or area that the robot 100 should avoid (i.e., not drive through). In some embodiments, an avoid tag 622i may be operation mode-dependent. For example, the avoid tag 622i may be applicable only when the robot is operating in a fully autonomous mode. During teleoperation, the avoid tag 622i may be effectively ignored by the robot.
In other embodiments, the avoid tag 622i may include time-dependent navigation information. For example, the data structure for an avoid tag may include a cost field associated with a time period (e.g., a time of day interval, a point in time, etc.). The cost field may include data that represents a relative cost of navigation through an area associated with the avoid tag during the time period. A relatively high cost value may indicate that navigation through the area is relatively difficult during that time period and should be avoided if possible. In determining navigation routes, the robot or a remote processor may generate multiple potential navigation paths and calculate a total cost (or ranking score) for each potential navigation path based on the time-dependent navigation information in avoid tags along the path. The robot or remote processor may then determine a navigation path based on the path that has the relatively lower overall cost value during the navigation time period, i.e., relatively more favorable ranking score during the navigation time period.
In one embodiment, the time-dependent navigation information may be input by the user in the process of inserting a tag on the plan view map 810 and/or robot map 820. In other embodiments, the time-dependent information may be associated with a newly instantiated tag or appended to an existing tag based on information gathered by a robot during navigation. For example, a robot traveling down a hallway may have its sensors “blinded” by sunlight coming through windows during a particular time of day. An infrared proximity sensor, for example, will fail if the receiver is flooded with light. The robot may note the time of day in the time-dependent information associated with a tag and associate a high cost with the time dependent tag if the conditions associated with the time-dependent tag interfere with travel. Subsequently, the robot 100, and any other robot navigating the same space and receiving the time-dependent tag at coordinates on their own map or a universally accessible and shared map, will avoid navigating the route identified by the tag coordinates at the particular time of day. For example, a robot 100 travelling a route that floods with sunlight at a halfway point in the route at an approaching point in time will avoid traveling that route and will divert to an alternate route or alternate portion of a route that has a lower associated cost (i.e., more favorable ranking). The robot 100 thus avoids sunlight flooding the navigation path and disabling sensors associated with effective and safe (i.e., collision free) navigation at a certain day and time at the identified location.
Additionally, occupancy sensors may be used to insert a tag on a map in response to sensed environmental conditions and thereby associate time-dependence with a reactive override, or robot-action modifier. Such sensors may include at least some or all of the following sensor types, either alone or in combination: Passive Infrared (PIR) sensors, RF disturbance sensors, Internet enabled sensor nodes, stationary sensors such as wall-mounted digital cameras or video cameras transmitting wirelessly and/or through wired transmission cables, mobile sensors embedded in worn employee badges, IMU-located sensors, IMU, accelerometer, GPS and/or Internet enabled mobile phones, sensors on one or more autonomous robots sharing the mapped area, and sensors incorporated in or in remote communication with robots such as InTouch Health®'s RP-Lite®, RP-Vantage®, and RP-xpress®.
For example, a potential navigation path may have a high cost (relatively low ranking/unfavorable ranking score among all potential navigation paths) if occupancy sensors in a particular area communicate data indicative of a high volume of traffic at one or more coordinate points along that navigation path which would make autonomous robot navigation difficult, inefficient, or impossible along some or all of that navigation path. In response to such time-dependent data, the robot 100 will elect a relatively more favorable navigation path having fewer indications of traffic instances and/or traffic volume.
The robot 100, and/or a remote processing terminal in communication with the robot and a plurality of sensors, communicates with the plurality of stationary and/or affixed environmental monitoring sensors. In embodiments, the robot 100 and/or remote processing terminal (which is, in embodiments, a central hub communicating remotely with a plurality of robots 100) respond in real time to sensor output by modifying behavior, for example by selecting an alternate navigation path with a more favorable ranking (lower associated cost value) enabling a more efficient and effective traversal of a travel route. In other embodiments, the robot 100 and/or remote terminal in communication with the robot 100 densely maps tags associating time-dependence to various environmental and traffic volume conditions. The tags have associated robot action modifiers for execution by the robot 100 in response to those tags at the associated point in time or associated time period. In embodiments, tag icons on a map change in appearance in accordance with their time dependence.
An operating room user interface (OR UI) tag 662j indicates the location or area of a hospital operating room. The robot 100 may use this tag to find the operating room to provide telepresence support and/or to display a specific user interface (e.g., an OR UI) upon entering the OR area. A training tag 662k can be used to mark general locations, such as hallways and rooms, to train the robot 100 to learn its environment 10.
A manual elevator tag 6621 indicates the location of an elevator where the robot 100 should allow a user to aid the robot's traversal into/out of the elevator. Manual elevator negotiation may be based on remote user piloting or robot-local user guidance. For remote user piloting, a remote user provides drive commands to the robot 100 (e.g., using a joystick). For robot-local user guidance, a person adjacent to the robot 100 may physically touch the robot 100 and, in response to those touches, the robot 100 moves accordingly. Features regarding robot responsiveness to user touching combinable with those described herein can be found in application Ser. No. 13/032,390, filed on Feb. 22, 2011, which is hereby incorporated by reference in its entirety.
An auto elevator tag 662m indicates the location of an elevator that the robot 100 may negotiate (into and out of) autonomously. The robot 100 may execute a threshold traversal behavior 512d (
A keep right tag 662n indicates a map location or area in which the robot 100 should keep to the right. A user may place this tag along certain corridors, such as high traffic corridors. In response to the keep right tag 662n, the robot 100 may execute a wall following behavior to stay along the wall while driving in the tagged area.
After map training, when a user wants to send the robot 100 to a location, the user can either refer to a label/tag 622 (e.g., enter a label or tag into a location text box displayed on the web pad 310) or the robot 100 can display the plan view map 810 to the user on the web pad 310 and the user may select the location on the plan view map 810. If the user selects a tagged layout map location 814, the robot 100 can easily determine the corresponding robot map location 824 on the robot map 820 and can proceed to navigate to the selected location 814.
In some implementations, the robot controller 500 may execute a first behavior 512 while maneuvering about a first area and then execute a second behavior 512 while maneuvering about a second area associated with a tag having an associated robot behavior modifier. For example, while executing a person follow behavior 512b, the robot controller may either cease execution of that behavior 512b or concurrently execute a threshold traversal behavior 512d upon reaching a map location 814 tagged with a ramp tag 662f or an auto elevator tag 622m.
If the selected location on the plan view map 810 is not a tagged location 814, the robot 100 determines a corresponding location 824 on the robot map 820. In some implementations, the robot 100 computes a scaling size, origin mapping, and rotation between the plan view map 810 and the robot map 820 using existing tagged locations, and then applies the computed parameters to determine the robot map location (e.g., using an affine transformation or coordinates).
The robot map 820 may not be the same orientation and scale as the plan view map 810. Moreover, the layout map may not be to scale and may have distortions that vary by map area. For example, a plan view map 810 created by scanning a fire evacuation map typically seen in hotels, offices, and hospitals is usually not drawn to scale and can even have different scales in different regions of the map. The robot map 820 may have its own errors. For example, locations on the robot map 820 may have been computed by counting wheel turns as a measure of distance, and if the floor was slightly slippery or turning around corners caused extra wheel turns, inaccurate rotation calculations may cause the robot 100 to determine inaccurate locations of mapped objects.
A method of mapping a given point 814 on the plan view map 810 to a corresponding point 824 on the robot map 820 may include using existing tagged points 812 to compute a local distortion (in the same two-dimensional coordinate system) between the plan view map 810 and the robot map 820 in a region (e.g., within a threshold radius) containing the layout map point. The method further includes applying a distortion calculation to the layout map point 814 in order to find a corresponding robot map point 824. The reverse can be done if you are starting with a given point on the robot map 820 and want to find a corresponding point on the plan view map 810, for example, for asking the robot for its current location.
Any of a wide variety of tag schemas and data structures may be used. Tags are a collection of information and the data comprising a tag can be distributed in different data tables. For example, tags may contain attributes in the form of key-value pairs that specify a purpose of the tag, tag parameters, and tag attributes (generally “tag information”). Table 1 below provides a specific example.
Tags associated with regions may have attributes associated with them that specify their purpose and specify parameters that influence behaviors associated with the region. These key-value pairs may be stored using a data structure similar to the example in Table 2 below:
The data structure for each tag may include tag coordinates and tag information, which may include a tag annotation (such as a two-dimensional and/or three-dimensional graphical representation of the tag). Table 3 below provides a specific example of a data structure for a tag.
As described herein, a tag may be associated with a region, rather than a specific point, on a map. There may be a many-to-one relationship between tag information and a tag. A specific example of a data structure for a tag associated with a region is provided below in Table 4A.
In some examples, the tag or region may be time-dependent, such that path navigation is altered by the time-dependent action modifiers. These may be pre-planned, scheduled action modifiers for time-dependent navigation of the robot. A specific example of a data structure for a tag or region associated with a schedule of action modifiers is provided below in Table 4B.
An array of scheduled actions may be, for example, an optionally concatenated array of “Code, time” combinations such as “33, 800, 0, 1700” associated with Wednesdays, where “33” is a identifier for an action modifier instructing the robot to “go slow” in a location at peak traffic hours, e.g., 8 am (“800” hours) and to change from the “go slow” code at 5 pm (“1700” hours), to a default pace of travel represented by “0”. Concatenating code combinations allows for multiple robot action modifiers to be coded sequentially with a triggering time, and for a null code to deactivate any robot action modifier at another time. Although the example schedule in Table 4A is a weekly schedule of action modifiers, the time dependent tags may include action modifiers associated with any schedule such as, for example, hourly, bi-weekly, monthly, specific calendar date-associated and annual schedules.
In some examples, the geometry of regions may be broken into the components of their centroid and offsets from the centroid to allow for the position and rotation of many objects to be updated quickly. When CPU resources permit, the bounding box of the final coordinates (the polygon points relative to the centroid, transformed by the centroid's pose into the map's coordinate system) can be indexed using an R*-tree or similar data structure for fast lookup based on geometric constraints. The points comprising the region's polygon may be stored in clockwise (or counter-clockwise) order to facilitate point-in-polygon tests based on ray-tracing algorithms.
As an example, a tag indicating a region that is a slow zone may have a data structure as provided below in Table 5.
Table 5 illustrates an example of a slow-zone tag in which the speed limits are explicitly set based on values associated with the region itself. Alternatively, a region may be defined in such a manner that a robot may interpret the region as a slow zone. For example, in Table 6 below, a robot may interpret a region defined as an intersection as a slow zone and reduce its speed to a predetermined velocity.
Tags associated with points or regions on a map may include tag coordinates, tag information, and tag annotations of any of a wide variety. Additionally, the tags may be implemented using any of a wide variety of data types, including those illustrated above in Tables 1-6. The terms tag information and tag annotation are referred herein as separate elements. However, according to various embodiments, a tag annotation may be a part of the tag information. Specifically, a data structure may or may not include a distinct field for the tag annotation. Rather, a field in the data structure for the tag information may incorporate the tag annotation.
Other details and features combinable herewith can be found in PCT application Serial No.: PCT/US11/60935, filed on Nov. 16, 2011, which is hereby incorporated by reference in its entirety.
Referring to
To provide the hybrid map 622b, the teleoperation software application 601 may determine a distortion (between two-dimensional coordinates and three-dimensional coordinates) between the remote view 612 and the plan view map 810 using recorded robot map locations 822 of a robot map 820 and corresponding layout map locations 812 and then applying the determined distortion to fit the plan view map 810 to the remote view 612. In some implementations, determining the distortion includes determining a scaling size, origin mapping, and rotation between the plan view map 810 and the remote view 612, for example, by applying an affine transformation to the determined scaling size, origin mapping, and rotation. Determining a distortion between a two-dimensional plan view map and a three-dimensional video feed may include determining a coordinate transformation between the disparate coordinate systems.
Referring to
In some implementations, as the user drives the robot 100 along a corridor using a joystick in communication with the telepresence software application 601, the user may invoke the look-ahead command 624 (e.g., by selecting a corresponding button on the user interface 605 or the joystick). For example, at a location 50 feet away from a turn in the corridor, the user may invoke the look-ahead command 624, causing the generation of a look-ahead view 612a and stopping further movement of the robot 100 along the corridor. The user may continue, however, to virtually move the robot 100 in a look-ahead mode. The user interface 605 may display the look-ahead view 612a (e.g., a three-dimensional model) of the same corridor at the same position. As the user drives forward in the look-ahead mode, continues 50 feet, turns left, and continues driving, the user can see the location of rooms and other corridors along the way in the three-dimensional model/look-ahead view 612a. In some examples, for the first 30 feet of “virtual” driving, the telepresence software application 601 may display a blend of the actual view (from the stationary physical robot, further magnified and perspective-warped to match the virtual location) and the three-dimensional model/look-ahead view 612a.
Referring to
Navigable area on the layout map may be highlighted based on information in the robot's internal obstacle map. In one embodiment, the navigable area may be identified as white-colored pixels in the image. The robot may return its position on the robot map and the position and orientation of the 3D depth camera. A processor may use the robot position and the kinematic state (e.g., pan and tilt angles) of the head camera to determine which pixels on the video screen represent the ground plane. That is, the processor may utilize the robot position and the perspective of the video feed to calculate the coordinates for each ground level pixel on the video feed. The white-colored pixels designating the navigable areas may then be overlaid on the ground level pixels on the video feed. Accordingly, the robot and/or a user controlling the robot could identify navigable areas by following the white-colored pixels. In alternative embodiments, any color pixel or other identifying mark could be used. Alternative data structures or marks could be used in place of white-colored pixels. Specifically, from the robot's POV the coordinates of ground level pixels that are navigable could be tagged in any of a wide variety of ways, so long as the robot is configured to recognize them.
The user may select a robot destination 618 in the navigable area 616, which causes the telepresence software application 601 to issue a drive command to the robot 100 to move to a location corresponding to the selected robot destination 618. In the example shown, the remote video feed window 610 of the user interface 605 provides a remote navigation view 612b of the robot 100 in a hospital room. The user selects a robot destination 618 as a location in the room next to a patient bed. The telepresence software application 601 may use the plan view map 810, robot map 820, three-dimensional map 705, two-dimensional (or 2.5D hybrid) height map 707, and/or stored image data 701 to resolve a distortion (within the same dimension and/or between dimensions) between the selected robot destination 618 on the remote navigation view 612b and a corresponding robot map location 824. The telepresence software application 601 may then issue a drive command to the robot 100 to maneuver autonomously or semi-autonomously to the robot map location 824, using its sensor system 400 and behavior system 510a to avoid any obstacles, such as moving people.
In one example, a map may be returned from a robot API call as an image, such as a PNG, JPG, or TIFF image. The robot could process the image in order to detect the pixels (e.g., black-colored pixels) that form the outline of an obstacle in the image. A curve fitting algorithm could be used to process the pixels that form the outline of the obstacle. The resulting curve(s) could then be used to generate an obstacle map. Additional processing may be done to further improve the obstacle detection and/or improve the accuracy of the curves fitting the detected obstacle outlines. For example, if a curve closes and forms a shape similar to a circle, the obstacle map could simply use a circle as a replacement. A similar idea could be applied to shapes like rectangles or ovals, people, faces, and/or those objects approximating a database of known object shapes from various perspectives.
The user interface 605 may provide a proximity sensor window 670 which displays a proximity of obstacles within a sensor field of view 442, 452 (e.g., within an three-dimensional imaging sensor field of view 452 and/or a laser scanner field of view 442).
In some implementations, the user may mark a protected region/zone on the remote video feed window 610 and/or the plan view map window 620 (not shown), using an avoid tag 662, 662i. A protected zone may be treated by the robot 100 as an object 12, and accordingly, protected zones may be avoided during autonomous navigation. Protected zones can be used to help create a wide berth around delicate equipment, or in order to ensure that the robot avoids other areas. The user may place an avoid tag 662, 662i on the plan view map 810 in the tagging view 660 or on the remote navigation view 612b. Moreover, the user may place other tags 662 on the remote navigation view 612b. The telepresence software application 601 may resolve a distortion between the remote navigation view 612b and the plan view map 810 and/or robot map 820 and then update the robot map 820 accordingly.
For example, determining a distortion between a plan view map and a video feed may comprise creating a transformation mapping between coordinate points in any of the navigation view 612b, the plan view map 810, and/or the robot map 820. Similar to overlaying a restricted region on a ground level in a video feed, the ground level of two-dimensional maps may be effectively coordinate-mapped onto a detected ground level in a video feed provided by a robot.
Although the ramp 1122 is within the navigable area 616, the telepresence software application 601 may determine the robot destination 618 on the ramp 1122 is an unsafe location to stop. The telepresence software application 601 may display an alert dialog box 1130, noting that the selected robot destination is an unsafe location to stop. In the example shown, the alert dialog box 1130 indicates that the user selected a ramp 1122 for the robot destination 618 and offers an alternative robot destination 619 just before the ramp 1122. Stopping the robot 100 on the ramp 1122 may be hazardous for people near the robot 100 and for the robot 100 itself, if the robot 100 tips or rolls down the ramp 1122. By determining that the robot destination 618 is on the ramp 1122, the telepresence software application 601 can either prohibit such a robot destination 618 and/or offer a safe alternative destination 619, in this case before the ramp 1122.
Referring to
Referring to
A display window may include a fly-out icon panel that is revealed by a mouse-over. For example, the icon panel may “fly” out from the top left of the window. The icon panel may allow a user to select a manual drive, a click-to-drive, and a head motion icon. In one embodiment, a user may toggle through the icons using the space bar. Manual drive may allow a user to click-to-destination and/or click-and-drag a path. After drawing a path on the map, the user may right-click and select “Save Path” from the popup menu. They can give a name to the path. Later the user may “Load Path”, and the robot will navigate to the starting point of the path, and then navigate along the prescribed path to the destination. The path may be stored as a tag data structure, including tag coordinates and tag information. The tag path may include multiple stops along the way. When drawing the path, the user may indicate way points along the path. In some embodiments, the way points may be represented by tag annotations that include stop signs. Later, when traversing the path, upon reaching a way point, the robot may flashes the “stop” button and the path may become lighter and/or translucent. At this point the user may perform a consult and do local driving, then click “go” to resume the path. Accordingly, a physician can save a path for his evening rounds, hitting all the rooms and stations in a preferred order and with a pre-planned route.
In head mode, a user may draw a box or outline on a portion of a video feed in order to center the head (upper portion) of the robot on the center of the box or an object within the box. Additionally, a user may click a location in order to change the heading of the head (upper portion) of the robot and/or the entire robot. Various button and peripheral control toggles may be used to independently control the base (lower portion) and head (upper portion) of the robot. For example, holding the shift key while in the head mode may make the curser a hand icon on the screen and allow the user to grab and drag the viewpoint of the head.
In some embodiments, a star icon may be used to control the navigation of the robot. The star icon may be displayed in any of the various views and may be selectively moved by the user to change the direction and/or velocity of the robot. Alternative icons in addition to a star icon may be used.
Returning to
A “stitched” video image may be displayed in the virtual joystick window 680. The “stitch” video and image may be generated using a live downward pointing camera 320, 450 on the front of the robot 100 and using a live downward pointing camera on the rear of the robot 100. A user may grab (e.g., using a mouse or touch gesture) and drag on robot motion indicator 686 in order to specify a direction of robot movement and a drive velocity. Driving the robot 100 from the virtual joystick window 680 has advantages over using the remote video feed window 610 for mouse-based or virtual joystick driving. Specifically, this view may reduce lens distortion, lack of depth information, and perception issues based on the rotation of the robot head 160 that a user may experience while driving the robot 100 using the video feed displayed in remote video feed window 610.
In addition to allowing the user to specify a desired path within the remote video feed window 610, the user may specify the robot path 652 on the map 622 displayed in the map window 620. Specifying the robot path 652 in the plan view map window 620 may allow the robot 100 to navigate over longer distances, and thus may free the user to perform other tasks while the robot 100 is in transit. Various controls may also be provided in order to manipulate the zoom and displayed area of the map 622 shown in the map window 620. A desired zoom may be specified using slider 1210, and a desired area may be displayed using an area pan control 1212.
Accordingly, a non-technical user may be able to navigate from one location to another using any combination of the various navigation methods and controls. For example, in long-distance travel, a user may click a destination on a plan view map and the robot may autonomously navigate to the selected location. In mid-range travel, the user may select a destination in a video window of a location within the field of view of the robot. In close range travel, the user may manually control the robot's navigation path, rotations, head movements, and the like using a mouse, joystick, virtual joystick, or meta joystick.
In the example shown, a variety of hyper-tags (tags) 1310 providing context-sensitive actions are displayed and made available to a user. The context-sensitive actions include an approach command 1312 and a follow command 1314. These context-sensitive actions may be generated upon the identification of a person 1330 within the field of view 322, 442, 452 of the robot 100. The user may invoke the approach command 1312 in order to position the robot 100 in front of the person 1330. The approach command 1312 may cause the execution of an approach behavior 512a (
The telepresence software application 601 may determine a distortion between the displayed two-dimensional map 622a and the first-person video feed captured by the robot camera 320. Determining such a distortion may include determining a coordinate transformation between the two-dimensional map and the three-dimensional “map.” When the user places a tag 662 and/or hyper-tag (which may comprise a tag) 1310 either on the remote view 612 of the remote video feed window 610 or on the two-dimensional map 622a of the map window 620, the telepresence software application 601 may apply the distortion to tag map coordinates associated with the tag 662 and/or the hyper-tag 1310 to determine corresponding video coordinates or plan view map coordinates, respectively, and overlay a tag annotation associated with the tag 662 or hyper-tag 1310 on the displayed remote view 612 (i.e., first-person video feed) or the map 622, respectively, using the determined video or map view coordinates. In various embodiments, the three-dimensional rendition of the tag annotation may be dynamically re-rendered based on the current position of the remote telepresence robot and a perspective of the tag relative to the video feed. Accordingly, as the location of the robot and/or the perspective of the live video feed changes, such as when the head (upper portion) of the robot pans or tilts, the tag annotation may be dynamically re-rendered. For example, a tag annotation corresponding to a ramp may be overlaid in the video feed with respect to the floor. Similarly, a tag annotation associated with an object on a wall may be overlaid with respect to the object or the wall.
As described herein, the tag may include tag information comprising a robot action modifier. The tag may be interpreted by a robot operator, a local terminal, or the remote telepresence robot and cause the robot to execute a predetermined action. For example, the robot action modifier may direct a robot to not enter a specific area, to travel slowly through a certain area, to travel fast through a certain area, to use extra caution, and/or to perform other actions. Tags in general may include any of a wide variety of information, such as an availability of a wireless communication signal, a speed the remote telepresence robot should travel, a location of a point of interest, a location of a person, a location of a docking station, a location of a rest area, a location of a glass wall, a location of a ramp, a location of an object, an optimal route to navigate a tight area, an optimal rout to navigate a congested area, and an action a remote telepresence robot should execute. The tag may be created by a user, automatically by a terminal, automatically by a robot, and/or in response to historical data collected by the terminal and/or robot.
The robot may include a tag identification system configured to identify tags having tag coordinates encountered along a navigation path. A robot may “encounter” a tag when the tag coordinates are within the local perceptual space of the robot and/or the tag coordinates are relevant to an objective, planned navigation path, or the planning of a navigation path. Accordingly, a tag identification system may “encounter” a tag along a navigation path, even if the robot is not yet in proximity and/or may never be in proximity to the tag coordinates of the tag.
A robot and/or remote terminal determining a navigation path for a robot may take into account tags or potential tags that could influence the navigation path. Accordingly, the tag identification system may be used to identify tags having tag coordinates projected to be along potential navigation paths during the determination of a navigation path. For instance, several potential navigation paths may be used to reach a desired destination and the selection of which navigation path will be used may depend on the tags relevant to each of the potential navigation paths. A robot selecting between multiple potential navigation paths may identify relevant tags in order to determine which navigation path would provide the best wireless connectivity. Other factors, such as ramps, elevators, distance, congestion, objects,
In the example user interface 605 shown, the dashboard window 640 provides a battery charge status, a wireless signal strength indicator, and a robot outline having portions that may light up when service is required. An options window 690 allows the user to disconnect or dock the robot with a docking station and set software and/or robot options.
Referring to
Additional details and features concerning person recognition and person following can be found in PCT application serial number PCT/US11/35488, filed on May 6, 2011, which is hereby incorporated by reference in its entirety.
The telepresence software application 601 may display information on the remote video view 612 and/or a map 622, indicating physical areas of interest. For example, a small arrow with an attached bubble reading “Pharma” may indicate the location of the pharmacy room. Such a bubble could comprise a tag. For example, the tag could include tag coordinates indicating where the word “Pharma” should be displayed; tag information, such as relevant information related to the pharmacy; and a tag annotation, such as a graphical representation in two-dimensional and/or three-dimensional of the word “Pharma.” In some examples, the user can determine what information is available about nearby rooms by placing the mouse over or gesturing over that area, causing the display of any corresponding available information. With this information, the user can quickly choose to go to a destination (e.g., the pharmacy room) by selecting a robot destination 618 in the remote navigation view 612b (
For example, according to one example a robot or remote terminal could retrieve tag coordinates that correspond to tags associated with a robot map. Using the robot position, tags that are in close proximity to the robot may be identified. Tags within the field of view of the robot may be identified using the orientation of the robot's head (upper portion). The robot and/or remote terminal could then calculate a set of coordinates for all of the pixels on the video screen and render a tag annotation associated with each tag within the line of sight based on the position of the robot and the perspective provided by the robot's current head orientation (pan and/or tilt). According to some embodiments, Denavit-Hartenberg parameters (DH parameters) may be used as a standard coordinate system for spatial linkages between the video feed and the map plan view.
Referring again to
In addition or alternatively to allowing the user to place tags 662 and hyper-tags 1310 in the user interface 605, the user may enter user-specific hyper-tags 1310 during operation of the robot 100. The user may invoke a command that allows for the insertion of a hyper-tag 1310 at a current robot location. Another command may allow for the removal of the hyper-tag 1310. Further, other users (e.g., nurses) may be allowed to add hyper-tags 1310 that may be shown to a user of the robot 100. A “nurse map application” may display a top-down map or a tagging view 660 that allows placement of temporary hyper-tags 1310, for example, to identify rooms of interest to a doctor who may soon be logging in. Moreover, some hyper-tags 1310 may be user-specific and/or time-specific. For example, a stroke patient in a room may be showing signs of deterioration. The nurse can call up the “nurse map application,” find that room on the map and enter a hyper-tag 1310. The nurse may fill in a hyper-tag as follows: hyper-tag_name=“Stroke patient deteriorating,” user_specific=“Dr. Reynolds,” duration=1 hour. Thus, if Dr. Reynolds logs in within the next hour, he would see a hyper-tag 1310 associated with the patient's room on the map additionally indicating “Stroke patient deteriorating.” On approaching that wing, he may also see a hyper-tag 1310 pop up pointing to that room in the video stream, labeled “Stroke patient deteriorating.” No other doctor would see those labels, and Dr. Reynolds would only see them during that first hour.
A doctor may also set up temporary bookmark and reminder hyper-tags 1310, directly at a local or remote station interface 606, 608, to assist with his/her work plan. In some examples, the doctor may assign numbers to several patient rooms at the start of the session. Then during the session, he/she may see the numbers on the displayed map 622 and in popup hyper-tags 1310 to remind him/her of the order in which to visit the patients 614. The doctor may add notes which can be viewed through the remainder of the session or upon next returning, for example, “come back at end of session” on one patient, or “write prescription” or “check in again at 4 pm.”
Additionally, “smart” hyper-tags 1310 may be displayed automatically. For example, a nurse may enter photos of incoming patients 614 into a database (e.g., stored locally and/or on cloud storage 722) cross-referenced with their electronic medical record. The telepresence software application 601 may execute a face recognition algorithm on a video stream captured by the robot camera 320 to identify the patients 614, 1330, which can be cross-referenced to the database. Upon recognition of a patient's face, the telepresence software application 601 may automatically pull up and display the patient's electronic medical record.
Referring again to
Referring to
A map data source 1620, such as a database stored on the robot 100, cloud storage 722, the local robot endpoint server 604a and/or the remote endpoint server 604b, can store information for the plan view map 810, the robot map 820, tag 662 information, and/or hyper-tag 1310 information. The map data source 1620 may be a single database or combination of data sources 1610, such as the robot sensor data source 1610a and the head data source 1610b. The telepresence software application 601 and/or the robot 100 (e.g., the controller 500) may access the map data source 1620 to execute real-time or off-line concordance processing, provide user interface feedback, perform navigation routines, render maps 622, etc.
In some implementations, the control system 510 executing on the controller 500 of the robot 100 accesses one or more of the data sources 1610, such as the robot sensor data source 1610a, the head data source 1610b, and/or the map data source 1620 to issue events recognizable by the behavior system 510a. In response to raised events, the behavior system 510a may execute one or more behaviors 512 that affect the selection of a command executed by the resource control arbiter 560 on the robot resources 530 (
Referring again to the ramp example shown in
Referring again to
Accordingly, it should be broadly understood that the term “distortion” as used herein relates to resolving spatial coordinate errors, differences of transformation from one coordinate system to another, including between coordinates systems of different dimensions. For example, a robot and/or remote terminal may determine a distortion between a two-dimensional plan view map and a two-dimensional map generated, at least in part, by a robot, such as those generated using various robot sensor or laser scans. Additionally, a robot and/or remote terminal may determine a distortion between a three-dimensional map or video feed and a two-dimensional plan view map. Moreover, determining a distortion may relate to transforming coordinates between first person views, third person views, plan view map views, hybrid map views, and/or between any two different coordinate systems or perspectives within the same coordinate system.
Referring again to
Referring to
The user may click within the zone defined by first and second rings 1722 and 1724 in order to rotate a virtual head 1728 to that point. As the user rotates the virtual had 1728, the robot head 160 may move in real time with the telepresence software application 601 updating the live video feed from the robot camera 320 displayed in the remote view 612 of the remote video feed window 610 in real-time as well. In the example shown, the augmented overlay 1710 has a virtual base 1726 corresponding to the robot base 120 and a virtual head 1728 corresponding to the robot head 160 arranged at an angle/orientation with respect to the virtual base 1726 that corresponds to a current pose of the robot 100. In some examples, one of the virtual base 1726 and the virtual head 1728 is shown static while the other is free to move relative to the static one.
If the user clicks within the zone defined by the first and second rings 1722 and 1724 to rotate the head 1728 outside of the current field of view 1720, the robot 100 may rotate the head 160 and/or the base 120 in order to accomplish the user's command. In some examples, after rotating the head 160 according to the user command, the base 120 may rotate and the head 160 may then move to a center position. Such changes in position may be problematic if the user then attempts to reposition the robot 100 based on the previous rotation. To mitigate this, certain implementations may employ a system to reduce the requirement of base rotations in order to accommodate head rotations. For example, a counter may be initiated when the virtual head 1728 is turned to an angle. If the robot head 160 remains at that angle for a specified interval, the system may slowly rotate the base 120 in order to center the head 160 with respect to the base 120 while simultaneously rotating the head 160 in the opposite direction at the same speed. This keeps the current subject in view, while also ensuring that the head 160 and the base 120 are now in alignment and the forward frame of reference is dictated by where the user is looking. Further, if the user wishes to continue looking farther in that direction, the full panning range of motion of the head 160 is available.
In some examples, the robot 100 continues to rotate the base 120, so that the forward drive direction F coincides with the new heading, thus providing the head 160 relatively equal left/right panning ability. As the robot 100 rotates the base, it may simultaneously rotate the head 160 so as to face the heading and optionally so that a field of view 322, 452 of a sensor 320, 450, 450b on the head 160 can point along the new heading. In some implementations, the robot 100 turns the base 120 and the head 160 together, so as to allow the head 160 to face the new heading relatively quicker. If the base 120 over-rotates, the head 160 can counter-pan to recover alignment.
A highlight box 1920 may highlight an area of interest within the remote video view 612. The user may create the highlight box 1920 around an area of interest on a portion of the remote video view 612, for example, by dragging and dropping a box onto the screen and/or by clicking and dragging open a box around the area of interest. In response, the telepresence software application 601 may cause the robot head 160 to move to center on the highlight box 1920. Moreover, the camera 320 may zoom in to match the dimensions of the highlight box 1920.
Referring to
When the robot 100 experiences a loss of communication connectivity, the robot 100 may refer to a last trusted localization/pose (e.g., stored locally by the controller 500) and/or a current determined localization/pose (e.g., based on the robot sensor system 400) to continue navigating to the destination. If the robot path is a planned path, the robot 100 may resume the planned path to the destination. On the other hand, if the user was teleoperating the robot 100 to the destination, the robot 100 may follow a planned path to a nearest/last trusted location having communication connectivity (e.g., radiofrequency and/or wireless). Alternatively, the robot 100 may drive along a shortest path (i.e., a new path) to a nearest/last trusted location having communication connectivity.
After reaching the nearest/last trusted location, the robot 100 may determine if communication connectivity has been reestablished and if so, whether the destination has been reached. If communication connectivity was not reestablished, the robot 100 may synchronize its video recordings (and any other sensor data of the sensor system 400) to move to a next trusted location stored by the controller 500. Moreover, if the destination has not been reached, but communication connectivity was reestablished, the controller 500 may execute safe harbor countermeasures, which may entail continuing recording sensor data and displaying a new robot-side user interface (e.g., on the web pad 310) indicating that the session was terminated due to loss of communication connectivity. The robot 100 may improve its connectivity recovery percentage by reassessing and/or executing path planning to move to the last trusted location (using ODOA). The robot 100 may also move its antennas 490a, 490b (
In some examples, the robot 100 may improve the integrity/accuracy of the robot map 820 by marking the location of lost communications and any location of reestablished communications. The robot 100 may use waypoint navigation to move to an area having known connectivity (e.g., a WLAN warm zone or high signal zone). Waypoints are sets of coordinates that identify a point in physical space. The robot 100 may use waypoints established on the robot map 820 to maneuver to the destination.
Additional safe harbor countermeasures may include planning a path to a nearest lowest traffic area or moving to a nearest charging/docking station based on the robot map 820. In some examples, the robot 100 may move toward another nearest robot 100, which may use multiple antennas 490a, 490b for multiple-input and multiple-output (MIMO) to act as a Wi-Fi bridge 602.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed application specific integrated circuits (ASICs), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms “machine-readable medium” and “computer-readable medium” refer to any computer program product, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine-readable signal” refers to any signal used to provide machine instructions and/or data to a programmable processor.
Implementations of the subject matter and the functional operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Embodiments of the subject matter described in this specification can be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a computer-readable medium for execution by, or to control the operation of, data processing apparatus. The computer-readable medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter effecting a machine-readable propagated signal, or a combination of one or more of them. The term “data processing apparatus” encompasses all apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers. The apparatus can include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them. A propagated signal is an artificially generated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode information for transmission to suitable receiver apparatus.
A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a standalone program or as a module, a component, a subroutine, or other unit suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.
The processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., a field programmable gate array (FPGA) or an ASIC.
Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of a digital computer. Generally, a processor will receive instructions and data from a read only memory or a random access memory or both. The essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic disks, magneto optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, e.g., a mobile telephone, a personal digital assistant (PDA), a mobile audio player, a Global Positioning System (GPS) receiver, to name just a few. Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
Implementations of the subject matter described in this specification can be implemented in a computing system that includes a back end component, e.g., as a data server; that includes a middleware component, e.g., an application server; or that includes a front end component, e.g., a client computer having a graphical user interface or a web browser through which a user can interact with an implementation of the subject matter described is this specification, or any combination of one or more such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (LAN) and a wide area network (WAN), e.g., the Internet.
The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of a client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
While this specification contains many specifics, these should not be construed as limitations on the scope of the invention or of what may be claimed, but rather as descriptions of features specific to particular implementations of the invention. Certain features that are described in this specification in the context of separate implementations can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multi-tasking and parallel processing may be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.
A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the disclosure. Accordingly, other implementations are within the scope of the following claims. For example, the actions recited in the claims can be performed in a different order and still achieve desirable results.
This application is a continuation of U.S. patent application Ser. No. 13/958,413, filed Aug. 2, 2013, which is a continuation-in-part of U.S. patent application Ser. No. 13/360,579 filed on Jan. 27, 2012, which claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Application No. 61/437,433 filed on Jan. 28, 2011, all of which are hereby incorporated by reference in their entireties. In addition, U.S. Patent Publication No. 2007/0199108 and U.S. Pat. No. 6,535,793 are also incorporated herein by reference in their entireties.
| Number | Name | Date | Kind |
|---|---|---|---|
| 3821995 | Aghnides | Jul 1974 | A |
| 4107689 | Jellinek | Aug 1978 | A |
| 4213182 | Eichelberger et al. | Jul 1980 | A |
| 4413693 | Derby | Nov 1983 | A |
| 4471354 | Smith | Sep 1984 | A |
| 4519466 | Shiraishi | May 1985 | A |
| 4553309 | Hess et al. | Nov 1985 | A |
| 4572594 | Schwartz | Feb 1986 | A |
| 4625274 | Schroeder | Nov 1986 | A |
| 4638445 | Mattaboni | Jan 1987 | A |
| 4652204 | Amett | Mar 1987 | A |
| 4669168 | Tamura et al. | Jun 1987 | A |
| 4679152 | Perdue | Jul 1987 | A |
| 4697278 | Fleischer | Sep 1987 | A |
| 4697472 | Hiyane | Oct 1987 | A |
| 4709265 | Silverman et al. | Nov 1987 | A |
| 4733737 | Falamak | Mar 1988 | A |
| 4751658 | Kadonoff et al. | Jun 1988 | A |
| 4766581 | Korn et al. | Aug 1988 | A |
| 4777416 | Georg et al. | Oct 1988 | A |
| 4797557 | Ohman | Jan 1989 | A |
| 4803625 | Fu et al. | Feb 1989 | A |
| 4847764 | Halvorson | Jul 1989 | A |
| 4875172 | Kanayama | Oct 1989 | A |
| 4878501 | Shue | Nov 1989 | A |
| 4942512 | Kohno | Jul 1990 | A |
| 4942538 | Yuan et al. | Jul 1990 | A |
| 4953159 | Hayden et al. | Aug 1990 | A |
| 4974607 | Miwa | Dec 1990 | A |
| 4977971 | Cran et al. | Dec 1990 | A |
| 5006988 | Borenstein et al. | Apr 1991 | A |
| 5040116 | Evans, Jr. et al. | Aug 1991 | A |
| 5051906 | Evans, Jr. et al. | Sep 1991 | A |
| 5073749 | Kanayama | Dec 1991 | A |
| 5084828 | Kaufman et al. | Jan 1992 | A |
| 5130794 | Ritchey | Jul 1992 | A |
| 5148591 | Pryor | Sep 1992 | A |
| 5153833 | Gordon et al. | Oct 1992 | A |
| 5155684 | Burke et al. | Oct 1992 | A |
| 5157491 | Kassatly | Oct 1992 | A |
| 5182641 | Diner et al. | Jan 1993 | A |
| 5186270 | West | Feb 1993 | A |
| 5193143 | Kaemmerer et al. | Mar 1993 | A |
| 5217453 | Wilk | Jun 1993 | A |
| 5220263 | Onishi et al. | Jun 1993 | A |
| 5224157 | Yamada et al. | Jun 1993 | A |
| 5230023 | Nakano | Jul 1993 | A |
| 5231693 | Backes et al. | Jul 1993 | A |
| 5236432 | Matse et al. | Aug 1993 | A |
| 5262944 | Weisner et al. | Nov 1993 | A |
| 5305427 | Nagata | Apr 1994 | A |
| 5315287 | Sol | May 1994 | A |
| 5319611 | Korba | Jun 1994 | A |
| 5341242 | Gilboa et al. | Aug 1994 | A |
| 5341459 | Backes | Aug 1994 | A |
| 5341854 | Zezulka et al. | Aug 1994 | A |
| 5347306 | Nitta | Sep 1994 | A |
| 5347457 | Tanaka et al. | Sep 1994 | A |
| 5350033 | Kraft | Sep 1994 | A |
| 5366896 | Margrey et al. | Nov 1994 | A |
| 5374879 | Pin et al. | Dec 1994 | A |
| 5375195 | Johnston | Dec 1994 | A |
| 5400068 | Ishida et al. | Mar 1995 | A |
| 5413693 | Redepenning | May 1995 | A |
| 5417210 | Funda et al. | May 1995 | A |
| 5419008 | West | May 1995 | A |
| 5436542 | Petelin et al. | Jul 1995 | A |
| 5441042 | Putman | Aug 1995 | A |
| 5441047 | David et al. | Aug 1995 | A |
| 5442728 | Kaufman et al. | Aug 1995 | A |
| 5462051 | Oka et al. | Oct 1995 | A |
| 5474434 | Wada | Dec 1995 | A |
| 5486853 | Baxter et al. | Jan 1996 | A |
| 5510832 | Garcia | Apr 1996 | A |
| 5511147 | Abdel-Malek | Apr 1996 | A |
| 5528289 | Cortjens et al. | Jun 1996 | A |
| 5539741 | Barraclough et al. | Jul 1996 | A |
| 5544649 | David et al. | Aug 1996 | A |
| 5550577 | Verbiest et al. | Aug 1996 | A |
| 5553609 | Chen et al. | Sep 1996 | A |
| 5563998 | Yaksich et al. | Oct 1996 | A |
| 5572229 | Fisher | Nov 1996 | A |
| 5572999 | Funda et al. | Nov 1996 | A |
| 5594859 | Palmer et al. | Jan 1997 | A |
| 5598208 | McClintock | Jan 1997 | A |
| 5600573 | Hendricks et al. | Feb 1997 | A |
| 5617539 | Ludwig et al. | Apr 1997 | A |
| 5619341 | Auyeung et al. | Apr 1997 | A |
| 5623679 | Rivette et al. | Apr 1997 | A |
| 5630566 | Case | May 1997 | A |
| 5636218 | Ishikawa et al. | Jun 1997 | A |
| 5652849 | Conway et al. | Jul 1997 | A |
| 5657246 | Hogan et al. | Aug 1997 | A |
| 5659779 | Laird et al. | Aug 1997 | A |
| 5673082 | Wells et al. | Sep 1997 | A |
| 5675229 | Thorne | Oct 1997 | A |
| 5682199 | Lankford | Oct 1997 | A |
| 5684695 | Bauer | Nov 1997 | A |
| 5701904 | Simmons et al. | Dec 1997 | A |
| 5734804 | Bergner | Mar 1998 | A |
| 5734805 | Isensee et al. | Mar 1998 | A |
| 5739657 | Takayama et al. | Apr 1998 | A |
| 5748629 | Caldara et al. | May 1998 | A |
| 5749058 | Hashimoto | May 1998 | A |
| 5749362 | Funda et al. | May 1998 | A |
| 5754631 | Cave | May 1998 | A |
| 5758079 | Ludwig et al. | May 1998 | A |
| 5762458 | Wang et al. | Jun 1998 | A |
| 5764731 | Yablon | Jun 1998 | A |
| 5767897 | Howell | Jun 1998 | A |
| 5786846 | Hiroaki | Jul 1998 | A |
| 5787545 | Colens | Aug 1998 | A |
| 5793365 | Tang et al. | Aug 1998 | A |
| 5801755 | Echerer | Sep 1998 | A |
| 5802494 | Kuno | Sep 1998 | A |
| 5836872 | Kenet et al. | Nov 1998 | A |
| 5838575 | Lion | Nov 1998 | A |
| 5844599 | Hildin | Dec 1998 | A |
| 5857534 | DeVault et al. | Jan 1999 | A |
| 5867494 | Krishnaswamy et al. | Feb 1999 | A |
| 5867653 | Aras et al. | Feb 1999 | A |
| 5871451 | Unger et al. | Feb 1999 | A |
| 5872922 | Hogan et al. | Feb 1999 | A |
| 5876325 | Mizuno et al. | Mar 1999 | A |
| 5911036 | Wright et al. | Jun 1999 | A |
| 5917958 | Nunally et al. | Jun 1999 | A |
| 5927423 | Wada et al. | Jul 1999 | A |
| 5936679 | Kasahara et al. | Aug 1999 | A |
| 5949758 | Kober | Sep 1999 | A |
| 5954692 | Smith et al. | Sep 1999 | A |
| 5959423 | Nakanishi et al. | Sep 1999 | A |
| 5961446 | Beller et al. | Oct 1999 | A |
| 5966130 | Benman, Jr. | Oct 1999 | A |
| 5973724 | Riddle | Oct 1999 | A |
| 5974446 | Sonnenreich et al. | Oct 1999 | A |
| 5983263 | Rothrock et al. | Nov 1999 | A |
| 5995119 | Cosatto et al. | Nov 1999 | A |
| 5995884 | Allen et al. | Nov 1999 | A |
| 5999977 | Riddle | Dec 1999 | A |
| 6006140 | Carter | Dec 1999 | A |
| 6006946 | Williams et al. | Dec 1999 | A |
| 6031845 | Walding | Feb 2000 | A |
| 6036812 | Williams et al. | Mar 2000 | A |
| 6047259 | Campbell et al. | Apr 2000 | A |
| 6091219 | Maruo et al. | Jul 2000 | A |
| 6113343 | Goldenberg et al. | Sep 2000 | A |
| 6133944 | Braun et al. | Oct 2000 | A |
| 6135228 | Asada et al. | Oct 2000 | A |
| 6148100 | Anderson et al. | Nov 2000 | A |
| 6160582 | Hill | Dec 2000 | A |
| 6170929 | Wilson et al. | Jan 2001 | B1 |
| 6175779 | Barrett | Jan 2001 | B1 |
| 6189034 | Riddle | Feb 2001 | B1 |
| 6201984 | Funda et al. | Mar 2001 | B1 |
| 6211903 | Bullister | Apr 2001 | B1 |
| 6219587 | Ahlin et al. | Apr 2001 | B1 |
| 6232735 | Baba et al. | May 2001 | B1 |
| 6233504 | Das et al. | May 2001 | B1 |
| 6233735 | Ebihara | May 2001 | B1 |
| 6250928 | Poggio et al. | Jun 2001 | B1 |
| 6256556 | Zenke | Jul 2001 | B1 |
| 6259806 | Green | Jul 2001 | B1 |
| 6259956 | Myers et al. | Jul 2001 | B1 |
| 6266162 | Okamura et al. | Jul 2001 | B1 |
| 6266577 | Popp et al. | Jul 2001 | B1 |
| 6289263 | Mukherjee | Sep 2001 | B1 |
| 6292713 | Jouppi et al. | Sep 2001 | B1 |
| 6292714 | Okabayashi | Sep 2001 | B1 |
| 6304050 | Skaar et al. | Oct 2001 | B1 |
| 6314631 | Pryor | Nov 2001 | B1 |
| 6317652 | Osada | Nov 2001 | B1 |
| 6317953 | Pryor | Nov 2001 | B1 |
| 6321137 | De Smet | Nov 2001 | B1 |
| 6324184 | Hou et al. | Nov 2001 | B1 |
| 6324443 | Kurakake et al. | Nov 2001 | B1 |
| 6325756 | Webb et al. | Dec 2001 | B1 |
| 6327516 | Zenke | Dec 2001 | B1 |
| 6330486 | Padula | Dec 2001 | B1 |
| 6330493 | Takahashi et al. | Dec 2001 | B1 |
| 6342915 | Ozaki et al. | Jan 2002 | B1 |
| 6346950 | Jouppi | Feb 2002 | B1 |
| 6346962 | Goodridge | Feb 2002 | B1 |
| 6369847 | James et al. | Apr 2002 | B1 |
| 6373855 | Downing et al. | Apr 2002 | B1 |
| 6381515 | Inoue et al. | Apr 2002 | B1 |
| 6389329 | Colens | May 2002 | B1 |
| 6400378 | Snook | Jun 2002 | B1 |
| 6408230 | Wada | Jun 2002 | B2 |
| 6411055 | Fujita et al. | Jun 2002 | B1 |
| 6430471 | Kintou et al. | Aug 2002 | B1 |
| 6430475 | Okamoto et al. | Aug 2002 | B2 |
| 6438457 | Yokoo et al. | Aug 2002 | B1 |
| 6445964 | White et al. | Sep 2002 | B1 |
| 6449762 | McElvain | Sep 2002 | B1 |
| 6452915 | Jorgensen | Sep 2002 | B1 |
| 6457043 | Kwak et al. | Sep 2002 | B1 |
| 6459955 | Bartsch et al. | Oct 2002 | B1 |
| 6463352 | Tadokoro et al. | Oct 2002 | B1 |
| 6463361 | Wang et al. | Oct 2002 | B1 |
| 6466844 | Ikeda et al. | Oct 2002 | B1 |
| 6468265 | Evans et al. | Oct 2002 | B1 |
| 6470235 | Kasuga et al. | Oct 2002 | B2 |
| 6480762 | Uchikubo et al. | Nov 2002 | B1 |
| 6491701 | Tierney et al. | Dec 2002 | B2 |
| 6496099 | Wang et al. | Dec 2002 | B2 |
| 6496755 | Wallach et al. | Dec 2002 | B2 |
| 6501740 | Sun et al. | Dec 2002 | B1 |
| 6507773 | Parker et al. | Jan 2003 | B2 |
| 6522906 | Salisbury, Jr. et al. | Feb 2003 | B1 |
| 6523629 | Buttz et al. | Feb 2003 | B1 |
| 6526332 | Sakamoto et al. | Feb 2003 | B2 |
| 6529620 | Thompson | Mar 2003 | B2 |
| 6529765 | Franck et al. | Mar 2003 | B1 |
| 6529802 | Kawakita et al. | Mar 2003 | B1 |
| 6532404 | Colens | Mar 2003 | B2 |
| 6535182 | Stanton | Mar 2003 | B2 |
| 6535793 | Allard | Mar 2003 | B2 |
| 6540039 | Yu et al. | Apr 2003 | B1 |
| 6543899 | Covannon et al. | Apr 2003 | B2 |
| 6549215 | Jouppi | Apr 2003 | B2 |
| 6563533 | Colby | May 2003 | B1 |
| 6567038 | Granot et al. | May 2003 | B1 |
| 6580246 | Jacobs | Jun 2003 | B2 |
| 6581798 | Liff et al. | Jun 2003 | B2 |
| 6584376 | Van Kommer | Jun 2003 | B1 |
| 6587750 | Gerbi et al. | Jul 2003 | B2 |
| 6590604 | Tucker et al. | Jul 2003 | B1 |
| 6594269 | Polcyn | Jul 2003 | B1 |
| 6594552 | Nowlin et al. | Jul 2003 | B1 |
| 6597392 | Jenkins et al. | Jul 2003 | B1 |
| 6602469 | Maus et al. | Aug 2003 | B1 |
| 6604019 | Ahlin et al. | Aug 2003 | B2 |
| 6604021 | Imai et al. | Aug 2003 | B2 |
| 6604022 | Parker et al. | Aug 2003 | B2 |
| 6611120 | Song et al. | Aug 2003 | B2 |
| 6643496 | Shimoyama et al. | Nov 2003 | B1 |
| 6646677 | Noro et al. | Nov 2003 | B2 |
| 6650748 | Edwards et al. | Nov 2003 | B1 |
| 6666374 | Green et al. | Dec 2003 | B1 |
| 6667592 | Jacobs et al. | Dec 2003 | B2 |
| 6674259 | Norman et al. | Jan 2004 | B1 |
| 6684129 | Salisbury, Jr. et al. | Jan 2004 | B2 |
| 6691000 | Nagai et al. | Feb 2004 | B2 |
| 6693585 | MacLeod | Feb 2004 | B1 |
| 6710797 | McNelley et al. | Mar 2004 | B1 |
| 6724823 | Rovati et al. | Apr 2004 | B2 |
| 6728599 | Wang et al. | Apr 2004 | B2 |
| 6763282 | Glenn et al. | Jul 2004 | B2 |
| 6764373 | Osawa et al. | Jul 2004 | B1 |
| 6769771 | Trumbull | Aug 2004 | B2 |
| 6781606 | Jouppi | Aug 2004 | B2 |
| 6784916 | Smith | Aug 2004 | B2 |
| 6785589 | Eggenberger et al. | Aug 2004 | B2 |
| 6791550 | Goldhor et al. | Sep 2004 | B2 |
| 6798753 | Doganata et al. | Sep 2004 | B1 |
| 6799065 | Niemeyer | Sep 2004 | B1 |
| 6799088 | Wang et al. | Sep 2004 | B2 |
| 6804580 | Stoddard et al. | Oct 2004 | B1 |
| 6804656 | Rosenfeld et al. | Oct 2004 | B1 |
| 6810411 | Coughlin et al. | Oct 2004 | B1 |
| 6816192 | Nishikawa | Nov 2004 | B1 |
| 6816754 | Mukai et al. | Nov 2004 | B2 |
| 6836703 | Wang et al. | Dec 2004 | B2 |
| 6839612 | Sanchez et al. | Jan 2005 | B2 |
| 6840904 | Goldberg | Jan 2005 | B2 |
| 6845297 | Allard | Jan 2005 | B2 |
| 6852107 | Wang et al. | Feb 2005 | B2 |
| 6853878 | Hirayama et al. | Feb 2005 | B2 |
| 6853880 | Sakagami et al. | Feb 2005 | B2 |
| 6856662 | Glass et al. | Feb 2005 | B2 |
| 6871117 | Wang et al. | Mar 2005 | B2 |
| 6879879 | Jouppi et al. | Apr 2005 | B2 |
| 6888333 | Laby | May 2005 | B2 |
| 6892112 | Wang et al. | May 2005 | B2 |
| 6893267 | Yueh | May 2005 | B1 |
| 6895305 | Lathan et al. | May 2005 | B2 |
| 6898484 | Lemelson et al. | May 2005 | B2 |
| 6914622 | Smith et al. | Jul 2005 | B1 |
| 6925357 | Wang et al. | Aug 2005 | B2 |
| 6951535 | Ghodoussi et al. | Oct 2005 | B2 |
| 6952470 | Tioe et al. | Oct 2005 | B1 |
| 6957712 | Song et al. | Oct 2005 | B2 |
| 6958706 | Chaco et al. | Oct 2005 | B2 |
| 6965394 | Gutta et al. | Nov 2005 | B2 |
| 6990112 | Brent et al. | Jan 2006 | B1 |
| 6995664 | Darling | Feb 2006 | B1 |
| 7007235 | Hussein et al. | Feb 2006 | B1 |
| 7011538 | Chang | Mar 2006 | B2 |
| 7015934 | Toyama et al. | Mar 2006 | B2 |
| RE39080 | Johnston | Apr 2006 | E |
| 7030757 | Matsuhira et al. | Apr 2006 | B2 |
| 7053578 | Diehl et al. | May 2006 | B2 |
| 7055210 | Keppler et al. | Jun 2006 | B2 |
| 7058689 | Parker et al. | Jun 2006 | B2 |
| 7092001 | Schulz | Aug 2006 | B2 |
| 7096090 | Zweig | Aug 2006 | B1 |
| 7115102 | Abbruscato | Oct 2006 | B2 |
| 7117067 | McLurkin et al. | Oct 2006 | B2 |
| 7123285 | Smith et al. | Oct 2006 | B2 |
| 7123292 | Seeger et al. | Oct 2006 | B1 |
| 7123974 | Hamilton | Oct 2006 | B1 |
| 7123991 | Graf et al. | Oct 2006 | B2 |
| 7127325 | Nagata et al. | Oct 2006 | B2 |
| 7129970 | James et al. | Oct 2006 | B2 |
| 7133062 | Castles et al. | Nov 2006 | B2 |
| 7142945 | Wang et al. | Nov 2006 | B2 |
| 7142947 | Wang et al. | Nov 2006 | B2 |
| 7151982 | Lift et al. | Dec 2006 | B2 |
| 7154526 | Foote et al. | Dec 2006 | B2 |
| 7155306 | Haitin et al. | Dec 2006 | B2 |
| 7156809 | Quy | Jan 2007 | B2 |
| 7158859 | Wang et al. | Jan 2007 | B2 |
| 7158860 | Wang et al. | Jan 2007 | B2 |
| 7158861 | Wang et al. | Jan 2007 | B2 |
| 7161322 | Wang et al. | Jan 2007 | B2 |
| 7162338 | Goncalves et al. | Jan 2007 | B2 |
| 7164969 | Wang et al. | Jan 2007 | B2 |
| 7164970 | Wang et al. | Jan 2007 | B2 |
| 7167448 | Wookey et al. | Jan 2007 | B2 |
| 7171286 | Wang et al. | Jan 2007 | B2 |
| 7174238 | Zweig | Feb 2007 | B1 |
| 7181455 | Wookey et al. | Feb 2007 | B2 |
| 7184559 | Jouppi | Feb 2007 | B2 |
| 7188000 | Chiappetta et al. | Mar 2007 | B2 |
| 7199790 | Rosenberg et al. | Apr 2007 | B2 |
| 7202851 | Cunningham et al. | Apr 2007 | B2 |
| 7206627 | Abovitz et al. | Apr 2007 | B2 |
| 7215786 | Nakadai et al. | May 2007 | B2 |
| 7219364 | Bolle et al. | May 2007 | B2 |
| 7222000 | Wang et al. | May 2007 | B2 |
| 7227334 | Yang et al. | Jun 2007 | B2 |
| 7256708 | Rosenfeld et al. | Aug 2007 | B2 |
| 7262573 | Wang et al. | Aug 2007 | B2 |
| 7283153 | Provost et al. | Oct 2007 | B2 |
| 7289883 | Wang et al. | Oct 2007 | B2 |
| 7292257 | Kang et al. | Nov 2007 | B2 |
| 7292912 | Wang et al. | Nov 2007 | B2 |
| 7305114 | Wolff et al. | Dec 2007 | B2 |
| 7317685 | Flott et al. | Jan 2008 | B1 |
| 7321807 | Laski | Jan 2008 | B2 |
| 7332890 | Cohen et al. | Feb 2008 | B2 |
| 7333642 | Green | Feb 2008 | B2 |
| 7346429 | Goldenberg et al. | Mar 2008 | B2 |
| 7352153 | Yan | Apr 2008 | B2 |
| 7363121 | Chen et al. | Apr 2008 | B1 |
| 7382399 | McCall et al. | Jun 2008 | B1 |
| 7386730 | Uchikubo | Jun 2008 | B2 |
| 7391432 | Terada | Jun 2008 | B2 |
| 7400578 | Guthrie et al. | Jul 2008 | B2 |
| 7404140 | O'Rourke | Jul 2008 | B2 |
| 7421470 | Ludwig et al. | Sep 2008 | B2 |
| 7430209 | Porter | Sep 2008 | B2 |
| 7432949 | Remy et al. | Oct 2008 | B2 |
| 7433921 | Ludwig et al. | Oct 2008 | B2 |
| 7441953 | Banks | Oct 2008 | B2 |
| 7467211 | Herman et al. | Dec 2008 | B1 |
| 7483867 | Ansari et al. | Jan 2009 | B2 |
| 7492731 | Hagendorf | Feb 2009 | B2 |
| 7510428 | Obata et al. | Mar 2009 | B2 |
| 7523069 | Friedl et al. | Apr 2009 | B1 |
| 7525281 | Koyanagi et al. | Apr 2009 | B2 |
| 7535486 | Motomura et al. | May 2009 | B2 |
| 7557758 | Rofougaran | Jul 2009 | B2 |
| 7584020 | Bruemmer et al. | Sep 2009 | B2 |
| 7587260 | Bruemmer et al. | Sep 2009 | B2 |
| 7587512 | Ta et al. | Sep 2009 | B2 |
| 7590060 | Miceli | Sep 2009 | B2 |
| 7593030 | Wang et al. | Sep 2009 | B2 |
| 7599290 | Dos Remedios et al. | Oct 2009 | B2 |
| 7620477 | Bruemmer | Nov 2009 | B2 |
| 7624166 | Foote et al. | Nov 2009 | B2 |
| 7630314 | Dos Remedios et al. | Dec 2009 | B2 |
| 7631833 | Ghaleb et al. | Dec 2009 | B1 |
| 7643051 | Sandberg et al. | Jan 2010 | B2 |
| 7647320 | Mok et al. | Jan 2010 | B2 |
| 7657560 | DiRienzo | Feb 2010 | B1 |
| 7668621 | Bruemmer | Feb 2010 | B2 |
| 7680038 | Gourlay | Mar 2010 | B1 |
| 7693757 | Zimmerman | Apr 2010 | B2 |
| 7698432 | Short et al. | Apr 2010 | B2 |
| 7703113 | Dawson | Apr 2010 | B2 |
| 7719229 | Kaneko et al. | May 2010 | B2 |
| 7737993 | Kaasila et al. | Jun 2010 | B2 |
| 7739383 | Short et al. | Jun 2010 | B1 |
| 7756614 | Jouppi | Jul 2010 | B2 |
| 7761185 | Wang et al. | Jul 2010 | B2 |
| 7769492 | Wang et al. | Aug 2010 | B2 |
| 7769705 | Luechtefeld | Aug 2010 | B1 |
| 7774158 | Domingues Goncalves et al. | Aug 2010 | B2 |
| 7801644 | Bruemmer et al. | Sep 2010 | B2 |
| 7813836 | Wang et al. | Oct 2010 | B2 |
| 7831575 | Trossell et al. | Nov 2010 | B2 |
| 7835775 | Sawayama et al. | Nov 2010 | B2 |
| 7860680 | Arms et al. | Dec 2010 | B2 |
| 7861366 | Hahm et al. | Jan 2011 | B2 |
| 7873448 | Takeda et al. | Jan 2011 | B2 |
| 7885822 | Akers et al. | Feb 2011 | B2 |
| 7890210 | Choi et al. | Feb 2011 | B2 |
| 7890382 | Robb et al. | Feb 2011 | B2 |
| 7912583 | Gutmann et al. | Mar 2011 | B2 |
| RE42288 | Degioanni | Apr 2011 | E |
| 7924323 | Walker et al. | Apr 2011 | B2 |
| 7949616 | Levy et al. | May 2011 | B2 |
| 7956894 | Akers et al. | Jun 2011 | B2 |
| 7957837 | Ziegler et al. | Jun 2011 | B2 |
| 7974738 | Bruemmer et al. | Jul 2011 | B2 |
| 7982763 | King | Jul 2011 | B2 |
| 7982769 | Jenkins et al. | Jul 2011 | B2 |
| 7987069 | Rodgers et al. | Jul 2011 | B2 |
| 8073564 | Bruemmer et al. | Dec 2011 | B2 |
| 8077963 | Wang et al. | Dec 2011 | B2 |
| 8116910 | Walters et al. | Feb 2012 | B2 |
| 8126960 | Obradovich et al. | Feb 2012 | B2 |
| 8170241 | Roe et al. | May 2012 | B2 |
| 8179418 | Wright et al. | May 2012 | B2 |
| 8180486 | Saito et al. | May 2012 | B2 |
| 8209051 | Wang et al. | Jun 2012 | B2 |
| 8212533 | Ota | Jul 2012 | B2 |
| 8234010 | Thompson et al. | Jul 2012 | B2 |
| 8265793 | Cross et al. | Sep 2012 | B2 |
| 8271132 | Nielsen et al. | Sep 2012 | B2 |
| 8287522 | Moses et al. | Oct 2012 | B2 |
| 8292807 | Perkins et al. | Oct 2012 | B2 |
| 8301193 | Lynch et al. | Oct 2012 | B1 |
| 8320534 | Kim et al. | Nov 2012 | B2 |
| 8340654 | Bratton et al. | Dec 2012 | B2 |
| 8340819 | Mangaser et al. | Dec 2012 | B2 |
| 8348675 | Dohrmann | Jan 2013 | B2 |
| 8355818 | Nielsen et al. | Jan 2013 | B2 |
| 8374171 | Cho et al. | Feb 2013 | B2 |
| 8384755 | Wang et al. | Feb 2013 | B2 |
| 8400491 | Panpaliya et al. | Mar 2013 | B1 |
| 8401229 | Hassan-Shafique et al. | Mar 2013 | B2 |
| 8401275 | Wang et al. | Mar 2013 | B2 |
| 8423284 | O'Shea | Apr 2013 | B2 |
| 8451731 | Lee et al. | May 2013 | B1 |
| 8462192 | O'Connell | Jun 2013 | B2 |
| 8463435 | Herzog et al. | Jun 2013 | B2 |
| 8489234 | Rew et al. | Jul 2013 | B2 |
| 8503340 | Xu | Aug 2013 | B1 |
| 8515577 | Wang et al. | Aug 2013 | B2 |
| 8527094 | Kumar et al. | Sep 2013 | B2 |
| 8531502 | Cheng et al. | Sep 2013 | B2 |
| 8532860 | Daly | Sep 2013 | B2 |
| 8610786 | Ortiz | Dec 2013 | B2 |
| 8612051 | Norman et al. | Dec 2013 | B2 |
| 8620077 | Grundmann et al. | Dec 2013 | B1 |
| 8621213 | Logan et al. | Dec 2013 | B2 |
| 8632376 | Dooley et al. | Jan 2014 | B2 |
| 8639797 | Pan et al. | Jan 2014 | B1 |
| 8670017 | Stuart et al. | Mar 2014 | B2 |
| 8712120 | Reicher et al. | Apr 2014 | B1 |
| 8712162 | Kirsch | Apr 2014 | B2 |
| 8718837 | Wang et al. | May 2014 | B2 |
| 8726454 | Gilbert, Jr. et al. | May 2014 | B2 |
| 8836751 | Ballantyne et al. | Sep 2014 | B2 |
| 8849679 | Wang et al. | Sep 2014 | B2 |
| 8849680 | Wright et al. | Sep 2014 | B2 |
| 8861750 | Roe et al. | Oct 2014 | B2 |
| 8897920 | Wang et al. | Nov 2014 | B2 |
| 8902278 | Pinter et al. | Dec 2014 | B2 |
| 8935005 | Rosenstein et al. | Jan 2015 | B2 |
| 8965578 | Versteeg et al. | Feb 2015 | B2 |
| 8965579 | Wang et al. | Feb 2015 | B2 |
| 8983772 | Lee | Mar 2015 | B2 |
| 9001207 | Webb et al. | Apr 2015 | B1 |
| 9098611 | Pinter et al. | Aug 2015 | B2 |
| 9147284 | Keene et al. | Sep 2015 | B2 |
| 9219857 | Eldon | Dec 2015 | B2 |
| 9224181 | Pinter et al. | Dec 2015 | B2 |
| 9323250 | Wang et al. | Apr 2016 | B2 |
| 9361021 | Jordan et al. | Jun 2016 | B2 |
| 9469030 | Wang et al. | Oct 2016 | B2 |
| 9571789 | Pinter et al. | Feb 2017 | B2 |
| 9776327 | Pinter et al. | Oct 2017 | B2 |
| 9785149 | Wang et al. | Oct 2017 | B2 |
| 9902069 | Farlow et al. | Feb 2018 | B2 |
| 9974612 | Pinter et al. | May 2018 | B2 |
| 10061896 | Jordan et al. | Aug 2018 | B2 |
| 10334205 | Pinter et al. | Jun 2019 | B2 |
| 10591921 | Wang et al. | Mar 2020 | B2 |
| 10603792 | Lai et al. | Mar 2020 | B2 |
| 10658083 | Jordan et al. | May 2020 | B2 |
| 20010002448 | Wilson et al. | May 2001 | A1 |
| 20010010053 | Ben-Shachar et al. | Jul 2001 | A1 |
| 20010020200 | Das et al. | Sep 2001 | A1 |
| 20010034475 | Flach et al. | Oct 2001 | A1 |
| 20010034544 | Mo | Oct 2001 | A1 |
| 20010037163 | Mlard | Nov 2001 | A1 |
| 20010048464 | Bamett | Dec 2001 | A1 |
| 20010051881 | Filler | Dec 2001 | A1 |
| 20010054071 | Loeb | Dec 2001 | A1 |
| 20010055373 | Yamashita | Dec 2001 | A1 |
| 20020013641 | Nourbakhsh et al. | Jan 2002 | A1 |
| 20020015296 | Howell et al. | Feb 2002 | A1 |
| 20020027597 | Sachau | Mar 2002 | A1 |
| 20020027652 | Paromtchik et al. | Mar 2002 | A1 |
| 20020033880 | Sul et al. | Mar 2002 | A1 |
| 20020038168 | Kasuga et al. | Mar 2002 | A1 |
| 20020044201 | Mexander et al. | Apr 2002 | A1 |
| 20020049517 | Ruffner | Apr 2002 | A1 |
| 20020055917 | Muraca | May 2002 | A1 |
| 20020057279 | Jouppi | May 2002 | A1 |
| 20020058929 | Green | May 2002 | A1 |
| 20020059587 | Cofano et al. | May 2002 | A1 |
| 20020063726 | Jouppi | May 2002 | A1 |
| 20020073429 | Beane et al. | Jun 2002 | A1 |
| 20020082498 | Wendt et al. | Jun 2002 | A1 |
| 20020085030 | Ghani | Jul 2002 | A1 |
| 20020086262 | Rainey | Jul 2002 | A1 |
| 20020091659 | Beaulieu et al. | Jul 2002 | A1 |
| 20020095238 | Ahlin et al. | Jul 2002 | A1 |
| 20020095239 | Wallach et al. | Jul 2002 | A1 |
| 20020098879 | Rheey | Jul 2002 | A1 |
| 20020104094 | Alexander et al. | Aug 2002 | A1 |
| 20020106998 | Presley et al. | Aug 2002 | A1 |
| 20020109770 | Terada | Aug 2002 | A1 |
| 20020109775 | White et al. | Aug 2002 | A1 |
| 20020111988 | Sato | Aug 2002 | A1 |
| 20020113813 | Yoshimine | Aug 2002 | A1 |
| 20020120362 | Lathan et al. | Aug 2002 | A1 |
| 20020128985 | Greenwald | Sep 2002 | A1 |
| 20020130950 | James et al. | Sep 2002 | A1 |
| 20020133062 | Arling et al. | Sep 2002 | A1 |
| 20020141595 | Jouppi | Oct 2002 | A1 |
| 20020143923 | Alexander | Oct 2002 | A1 |
| 20020177925 | Onishi et al. | Nov 2002 | A1 |
| 20020183894 | Wang et al. | Dec 2002 | A1 |
| 20020184674 | Xi et al. | Dec 2002 | A1 |
| 20020186243 | Ellis et al. | Dec 2002 | A1 |
| 20020198626 | Imai et al. | Dec 2002 | A1 |
| 20030016726 | Pavlidis | Jan 2003 | A1 |
| 20030021107 | Howell et al. | Jan 2003 | A1 |
| 20030030397 | Simmons | Feb 2003 | A1 |
| 20030048481 | Kobayashi et al. | Mar 2003 | A1 |
| 20030050733 | Wang et al. | Mar 2003 | A1 |
| 20030050734 | Lapham | Mar 2003 | A1 |
| 20030053662 | Evoy et al. | Mar 2003 | A1 |
| 20030060808 | Wilk | Mar 2003 | A1 |
| 20030063600 | Noma et al. | Apr 2003 | A1 |
| 20030069752 | Ledain et al. | Apr 2003 | A1 |
| 20030080901 | Piotrowski | May 2003 | A1 |
| 20030100892 | Morley et al. | May 2003 | A1 |
| 20030104806 | Ruef et al. | Jun 2003 | A1 |
| 20030112823 | Collins et al. | Jun 2003 | A1 |
| 20030114736 | Reed et al. | Jun 2003 | A1 |
| 20030114962 | Niemeyer | Jun 2003 | A1 |
| 20030120714 | Wolff et al. | Jun 2003 | A1 |
| 20030126361 | Slater et al. | Jul 2003 | A1 |
| 20030135097 | Wiederhold et al. | Jul 2003 | A1 |
| 20030135203 | Wang et al. | Jul 2003 | A1 |
| 20030144579 | Buss | Jul 2003 | A1 |
| 20030144649 | Ghodoussi et al. | Jul 2003 | A1 |
| 20030151658 | Smith | Aug 2003 | A1 |
| 20030152145 | Kawakita | Aug 2003 | A1 |
| 20030171710 | Bassuk et al. | Sep 2003 | A1 |
| 20030174285 | Trumbull | Sep 2003 | A1 |
| 20030179223 | Ying et al. | Sep 2003 | A1 |
| 20030180697 | Kim et al. | Sep 2003 | A1 |
| 20030195662 | Wang et al. | Oct 2003 | A1 |
| 20030199000 | Valkirs et al. | Oct 2003 | A1 |
| 20030206242 | Choi | Nov 2003 | A1 |
| 20030212472 | Mckee | Nov 2003 | A1 |
| 20030216833 | Mukai et al. | Nov 2003 | A1 |
| 20030216834 | Allard | Nov 2003 | A1 |
| 20030220541 | Salisbury et al. | Nov 2003 | A1 |
| 20030220715 | Kneifel et al. | Nov 2003 | A1 |
| 20030231244 | Bonilla et al. | Dec 2003 | A1 |
| 20030232649 | Gizis et al. | Dec 2003 | A1 |
| 20030236590 | Park et al. | Dec 2003 | A1 |
| 20040001197 | Ko et al. | Jan 2004 | A1 |
| 20040001676 | Colgan et al. | Jan 2004 | A1 |
| 20040008138 | Hockley et al. | Jan 2004 | A1 |
| 20040010344 | Hiratsuka et al. | Jan 2004 | A1 |
| 20040012362 | Tsurumi | Jan 2004 | A1 |
| 20040013295 | Sabe et al. | Jan 2004 | A1 |
| 20040017475 | Akers et al. | Jan 2004 | A1 |
| 20040019406 | Wang et al. | Jan 2004 | A1 |
| 20040024490 | Mclurkin et al. | Feb 2004 | A1 |
| 20040027243 | Carrender | Feb 2004 | A1 |
| 20040041904 | Lapalme et al. | Mar 2004 | A1 |
| 20040064080 | Cruz et al. | Apr 2004 | A1 |
| 20040065073 | Nash | Apr 2004 | A1 |
| 20040068657 | Alexander et al. | Apr 2004 | A1 |
| 20040078219 | Kaylor et al. | Apr 2004 | A1 |
| 20040080610 | James et al. | Apr 2004 | A1 |
| 20040088077 | Jouppi et al. | May 2004 | A1 |
| 20040088078 | Jouppi et al. | May 2004 | A1 |
| 20040093409 | Thompson et al. | May 2004 | A1 |
| 20040095516 | Rohlicek | May 2004 | A1 |
| 20040098167 | Yi et al. | May 2004 | A1 |
| 20040102167 | Shim et al. | May 2004 | A1 |
| 20040107254 | Ludwig et al. | Jun 2004 | A1 |
| 20040107255 | Ludwig et al. | Jun 2004 | A1 |
| 20040117065 | Wang et al. | Jun 2004 | A1 |
| 20040117067 | Jouppi | Jun 2004 | A1 |
| 20040123158 | Roskind | Jun 2004 | A1 |
| 20040135879 | Stacy et al. | Jul 2004 | A1 |
| 20040138547 | Wang et al. | Jul 2004 | A1 |
| 20040140404 | Ohta et al. | Jul 2004 | A1 |
| 20040143421 | Wang et al. | Jul 2004 | A1 |
| 20040148638 | Weisman et al. | Jul 2004 | A1 |
| 20040150725 | Taguchi | Aug 2004 | A1 |
| 20040153211 | Kamoto et al. | Aug 2004 | A1 |
| 20040157612 | Kim | Aug 2004 | A1 |
| 20040162637 | Wang et al. | Aug 2004 | A1 |
| 20040167666 | Wang et al. | Aug 2004 | A1 |
| 20040167668 | Wang et al. | Aug 2004 | A1 |
| 20040168148 | Goncalves et al. | Aug 2004 | A1 |
| 20040170300 | Jouppi | Sep 2004 | A1 |
| 20040172301 | Mihai et al. | Sep 2004 | A1 |
| 20040172306 | Wohl et al. | Sep 2004 | A1 |
| 20040174129 | Wang et al. | Sep 2004 | A1 |
| 20040175684 | Kaasa et al. | Sep 2004 | A1 |
| 20040176118 | Strittmatter et al. | Sep 2004 | A1 |
| 20040179714 | Jouppi | Sep 2004 | A1 |
| 20040186623 | Dooley et al. | Sep 2004 | A1 |
| 20040189700 | Mandavilli et al. | Sep 2004 | A1 |
| 20040201602 | Mody et al. | Oct 2004 | A1 |
| 20040205664 | Prendergast | Oct 2004 | A1 |
| 20040215490 | Duchon et al. | Oct 2004 | A1 |
| 20040218099 | Washington | Nov 2004 | A1 |
| 20040222638 | Bednyak | Nov 2004 | A1 |
| 20040224676 | Iseki | Nov 2004 | A1 |
| 20040230340 | Fukuchi et al. | Nov 2004 | A1 |
| 20040236830 | Nelson et al. | Nov 2004 | A1 |
| 20040240981 | Dothan et al. | Dec 2004 | A1 |
| 20040241981 | Doris et al. | Dec 2004 | A1 |
| 20040252966 | Holloway et al. | Dec 2004 | A1 |
| 20040260790 | Balloni et al. | Dec 2004 | A1 |
| 20050003330 | Asgarinejad et al. | Jan 2005 | A1 |
| 20050004708 | Goldenberg et al. | Jan 2005 | A1 |
| 20050007445 | Foote et al. | Jan 2005 | A1 |
| 20050013149 | Trossell | Jan 2005 | A1 |
| 20050021182 | Wang et al. | Jan 2005 | A1 |
| 20050021183 | Wang et al. | Jan 2005 | A1 |
| 20050021187 | Wang et al. | Jan 2005 | A1 |
| 20050021309 | Alexander et al. | Jan 2005 | A1 |
| 20050024485 | Castles et al. | Feb 2005 | A1 |
| 20050027400 | Wang et al. | Feb 2005 | A1 |
| 20050027567 | Taha | Feb 2005 | A1 |
| 20050027794 | Decker | Feb 2005 | A1 |
| 20050028221 | Liu et al. | Feb 2005 | A1 |
| 20050035862 | Wildman et al. | Feb 2005 | A1 |
| 20050038416 | Wang et al. | Feb 2005 | A1 |
| 20050038564 | Burick | Feb 2005 | A1 |
| 20050049898 | Hirakawa | Mar 2005 | A1 |
| 20050052527 | Remy et al. | Mar 2005 | A1 |
| 20050060211 | Xiao et al. | Mar 2005 | A1 |
| 20050065435 | Rauch et al. | Mar 2005 | A1 |
| 20050065438 | Miller | Mar 2005 | A1 |
| 20050065659 | Tanaka et al. | Mar 2005 | A1 |
| 20050065813 | Mishelevich et al. | Mar 2005 | A1 |
| 20050071046 | Miyazaki et al. | Mar 2005 | A1 |
| 20050073575 | Thacher et al. | Apr 2005 | A1 |
| 20050078816 | Sekiguchi et al. | Apr 2005 | A1 |
| 20050083011 | Yang et al. | Apr 2005 | A1 |
| 20050099493 | Chew | May 2005 | A1 |
| 20050104964 | Bovyrin et al. | May 2005 | A1 |
| 20050110867 | Schulz | May 2005 | A1 |
| 20050122390 | Wang et al. | Jun 2005 | A1 |
| 20050125083 | Kiko | Jun 2005 | A1 |
| 20050125098 | Wang et al. | Jun 2005 | A1 |
| 20050149364 | Ombrellaro | Jul 2005 | A1 |
| 20050152447 | Jouppi et al. | Jul 2005 | A1 |
| 20050152565 | Jouppi et al. | Jul 2005 | A1 |
| 20050154265 | Miro et al. | Jul 2005 | A1 |
| 20050168568 | Jouppi | Aug 2005 | A1 |
| 20050182322 | Grispo | Aug 2005 | A1 |
| 20050192721 | Jouppi | Sep 2005 | A1 |
| 20050204438 | Wang et al. | Sep 2005 | A1 |
| 20050212478 | Takenaka | Sep 2005 | A1 |
| 20050219356 | Smith et al. | Oct 2005 | A1 |
| 20050225448 | Schenker | Oct 2005 | A1 |
| 20050225634 | Brunetti et al. | Oct 2005 | A1 |
| 20050231156 | Yan | Oct 2005 | A1 |
| 20050231586 | Rodman et al. | Oct 2005 | A1 |
| 20050232647 | Takenaka | Oct 2005 | A1 |
| 20050234592 | Mcgee et al. | Oct 2005 | A1 |
| 20050264649 | Chang et al. | Dec 2005 | A1 |
| 20050267826 | Levy et al. | Dec 2005 | A1 |
| 20050278446 | Bryant | Dec 2005 | A1 |
| 20050283414 | Fernandes et al. | Dec 2005 | A1 |
| 20050286759 | Zitnick et al. | Dec 2005 | A1 |
| 20060004582 | Claudatos et al. | Jan 2006 | A1 |
| 20060007943 | Fellman | Jan 2006 | A1 |
| 20060010028 | Sorensen | Jan 2006 | A1 |
| 20060013263 | Fellman | Jan 2006 | A1 |
| 20060013469 | Wang et al. | Jan 2006 | A1 |
| 20060013488 | Inoue | Jan 2006 | A1 |
| 20060014388 | Lur et al. | Jan 2006 | A1 |
| 20060020694 | Nag et al. | Jan 2006 | A1 |
| 20060029065 | Fellman | Feb 2006 | A1 |
| 20060047365 | Ghodoussi et al. | Mar 2006 | A1 |
| 20060048286 | Donato | Mar 2006 | A1 |
| 20060052676 | Wang et al. | Mar 2006 | A1 |
| 20060052684 | Takahashi et al. | Mar 2006 | A1 |
| 20060056655 | Wen | Mar 2006 | A1 |
| 20060056837 | Vapaakoski | Mar 2006 | A1 |
| 20060058921 | Okamoto | Mar 2006 | A1 |
| 20060064212 | Thorne | Mar 2006 | A1 |
| 20060066609 | Iodice et al. | Mar 2006 | A1 |
| 20060071797 | Rosenfeld et al. | Apr 2006 | A1 |
| 20060074525 | Close et al. | Apr 2006 | A1 |
| 20060074719 | Horner | Apr 2006 | A1 |
| 20060082642 | Wang et al. | Apr 2006 | A1 |
| 20060087746 | Lipow | Apr 2006 | A1 |
| 20060095158 | Lee et al. | May 2006 | A1 |
| 20060095170 | Yang et al. | May 2006 | A1 |
| 20060098573 | Beer et al. | May 2006 | A1 |
| 20060103659 | Karandikar et al. | May 2006 | A1 |
| 20060104279 | Fellman et al. | May 2006 | A1 |
| 20060106493 | Niemeyer et al. | May 2006 | A1 |
| 20060122482 | Mariotti et al. | Jun 2006 | A1 |
| 20060125356 | Meek et al. | Jun 2006 | A1 |
| 20060142983 | Sorensen et al. | Jun 2006 | A1 |
| 20060149418 | Anvari | Jul 2006 | A1 |
| 20060161136 | Anderson et al. | Jul 2006 | A1 |
| 20060161303 | Wang et al. | Jul 2006 | A1 |
| 20060164546 | Machi | Jul 2006 | A1 |
| 20060171515 | Hintermeister et al. | Aug 2006 | A1 |
| 20060173708 | Vining et al. | Aug 2006 | A1 |
| 20060173712 | Joubert | Aug 2006 | A1 |
| 20060178559 | Kumar et al. | Aug 2006 | A1 |
| 20060178776 | Feingold et al. | Aug 2006 | A1 |
| 20060178777 | Park et al. | Aug 2006 | A1 |
| 20060184274 | Sakai et al. | Aug 2006 | A1 |
| 20060189393 | Edery | Aug 2006 | A1 |
| 20060195569 | Barker | Aug 2006 | A1 |
| 20060200518 | Sinclair et al. | Sep 2006 | A1 |
| 20060224781 | Tsao et al. | Oct 2006 | A1 |
| 20060247045 | Jeong et al. | Nov 2006 | A1 |
| 20060248210 | Kenoyer | Nov 2006 | A1 |
| 20060259193 | Wang et al. | Nov 2006 | A1 |
| 20060268704 | Ansari et al. | Nov 2006 | A1 |
| 20060271238 | Choi et al. | Nov 2006 | A1 |
| 20060271400 | Clements et al. | Nov 2006 | A1 |
| 20060293788 | Pogodin | Dec 2006 | A1 |
| 20070008321 | Gallagher et al. | Jan 2007 | A1 |
| 20070016328 | Ziegler et al. | Jan 2007 | A1 |
| 20070021871 | Wang et al. | Jan 2007 | A1 |
| 20070025711 | Marcus | Feb 2007 | A1 |
| 20070046237 | Lakshmanan et al. | Mar 2007 | A1 |
| 20070050937 | Song et al. | Mar 2007 | A1 |
| 20070061041 | Zweig | Mar 2007 | A1 |
| 20070064092 | Sandbeg et al. | Mar 2007 | A1 |
| 20070070069 | Samarasekera et al. | Mar 2007 | A1 |
| 20070078566 | Wang et al. | Apr 2007 | A1 |
| 20070093279 | Janik | Apr 2007 | A1 |
| 20070109324 | Lin | May 2007 | A1 |
| 20070112700 | Den haan et al. | May 2007 | A1 |
| 20070116152 | Thesling | May 2007 | A1 |
| 20070117516 | Saidi et al. | May 2007 | A1 |
| 20070120965 | Sandberg et al. | May 2007 | A1 |
| 20070122783 | Habashi | May 2007 | A1 |
| 20070129849 | Zini et al. | Jun 2007 | A1 |
| 20070133407 | Choi et al. | Jun 2007 | A1 |
| 20070135967 | Jung et al. | Jun 2007 | A1 |
| 20070142964 | Abramson | Jun 2007 | A1 |
| 20070156286 | Yamauchi | Jul 2007 | A1 |
| 20070165106 | Groves et al. | Jul 2007 | A1 |
| 20070170886 | Plishner | Jul 2007 | A1 |
| 20070171275 | Kenoyer | Jul 2007 | A1 |
| 20070176060 | White et al. | Aug 2007 | A1 |
| 20070182811 | Rockefeller et al. | Aug 2007 | A1 |
| 20070192910 | Vu et al. | Aug 2007 | A1 |
| 20070197896 | Moll et al. | Aug 2007 | A1 |
| 20070198128 | Ziegler et al. | Aug 2007 | A1 |
| 20070198130 | Wang et al. | Aug 2007 | A1 |
| 20070199108 | Angle et al. | Aug 2007 | A1 |
| 20070216347 | Kaneko et al. | Sep 2007 | A1 |
| 20070226949 | Hahm et al. | Oct 2007 | A1 |
| 20070234492 | Svendsen et al. | Oct 2007 | A1 |
| 20070238963 | Kaminaga et al. | Oct 2007 | A1 |
| 20070250212 | Halloran et al. | Oct 2007 | A1 |
| 20070255115 | Anglin et al. | Nov 2007 | A1 |
| 20070255155 | Drew et al. | Nov 2007 | A1 |
| 20070255706 | Iketani et al. | Nov 2007 | A1 |
| 20070262884 | Goncalves et al. | Nov 2007 | A1 |
| 20070271122 | Zaleski | Nov 2007 | A1 |
| 20070273751 | Sachau | Nov 2007 | A1 |
| 20070279483 | Beers et al. | Dec 2007 | A1 |
| 20070290040 | Wurman et al. | Dec 2007 | A1 |
| 20070291109 | Wang et al. | Dec 2007 | A1 |
| 20070291128 | Wang et al. | Dec 2007 | A1 |
| 20070299316 | Haslehurst et al. | Dec 2007 | A1 |
| 20080004904 | Tran | Jan 2008 | A1 |
| 20080009969 | Bruemmer et al. | Jan 2008 | A1 |
| 20080011904 | Cepollina et al. | Jan 2008 | A1 |
| 20080021834 | Holla et al. | Jan 2008 | A1 |
| 20080027591 | Lenser et al. | Jan 2008 | A1 |
| 20080033641 | Medalia | Feb 2008 | A1 |
| 20080045804 | Williams | Feb 2008 | A1 |
| 20080051985 | D'andrea et al. | Feb 2008 | A1 |
| 20080056933 | Moore et al. | Mar 2008 | A1 |
| 20080059238 | Park et al. | Mar 2008 | A1 |
| 20080065268 | Wang et al. | Mar 2008 | A1 |
| 20080082211 | Wang et al. | Apr 2008 | A1 |
| 20080086236 | Saito et al. | Apr 2008 | A1 |
| 20080086241 | Phillips et al. | Apr 2008 | A1 |
| 20080091340 | Milstein et al. | Apr 2008 | A1 |
| 20080126132 | Warner et al. | May 2008 | A1 |
| 20080133052 | Jones et al. | Jun 2008 | A1 |
| 20080161969 | Lee et al. | Jul 2008 | A1 |
| 20080174570 | Jobs et al. | Jul 2008 | A1 |
| 20080201016 | Finlay | Aug 2008 | A1 |
| 20080201017 | Wang et al. | Aug 2008 | A1 |
| 20080215987 | Alexander et al. | Sep 2008 | A1 |
| 20080229531 | Takida | Sep 2008 | A1 |
| 20080232763 | Brady | Sep 2008 | A1 |
| 20080242220 | Wilson et al. | Oct 2008 | A1 |
| 20080253629 | Kazuno et al. | Oct 2008 | A1 |
| 20080255703 | Wang et al. | Oct 2008 | A1 |
| 20080263451 | Portele et al. | Oct 2008 | A1 |
| 20080263628 | Norman et al. | Oct 2008 | A1 |
| 20080267069 | Thielman et al. | Oct 2008 | A1 |
| 20080267578 | Zhang et al. | Oct 2008 | A1 |
| 20080269949 | Norman et al. | Oct 2008 | A1 |
| 20080281467 | Pinter | Nov 2008 | A1 |
| 20080285886 | Allen | Nov 2008 | A1 |
| 20080306375 | Sayler et al. | Dec 2008 | A1 |
| 20080316368 | Fritsch et al. | Dec 2008 | A1 |
| 20090021572 | Garudadri et al. | Jan 2009 | A1 |
| 20090022131 | Rusanen et al. | Jan 2009 | A1 |
| 20090022377 | Matsue et al. | Jan 2009 | A1 |
| 20090030552 | Nakadai et al. | Jan 2009 | A1 |
| 20090044334 | Parsell et al. | Feb 2009 | A1 |
| 20090049640 | Lee et al. | Feb 2009 | A1 |
| 20090051756 | Trachtenberg et al. | Feb 2009 | A1 |
| 20090055019 | Stiehl et al. | Feb 2009 | A1 |
| 20090055023 | Walters et al. | Feb 2009 | A1 |
| 20090070135 | Parida et al. | Mar 2009 | A1 |
| 20090079812 | Crenshaw et al. | Mar 2009 | A1 |
| 20090086013 | Thapa | Apr 2009 | A1 |
| 20090089085 | Schoenberg | Apr 2009 | A1 |
| 20090102919 | Zamierowski et al. | Apr 2009 | A1 |
| 20090105882 | Wang et al. | Apr 2009 | A1 |
| 20090106679 | Anzures et al. | Apr 2009 | A1 |
| 20090122699 | Alperovitch et al. | May 2009 | A1 |
| 20090125147 | Wang et al. | May 2009 | A1 |
| 20090125584 | Agrawala et al. | May 2009 | A1 |
| 20090141146 | Guidash | Jun 2009 | A1 |
| 20090144425 | Marr et al. | Jun 2009 | A1 |
| 20090146882 | Halivaara et al. | Jun 2009 | A1 |
| 20090164255 | Menschik et al. | Jun 2009 | A1 |
| 20090164657 | Li et al. | Jun 2009 | A1 |
| 20090171170 | Li et al. | Jul 2009 | A1 |
| 20090177323 | Ziegler et al. | Jul 2009 | A1 |
| 20090177641 | Raghavan | Jul 2009 | A1 |
| 20090201372 | O'doherty et al. | Aug 2009 | A1 |
| 20090210090 | Takemitsu et al. | Aug 2009 | A1 |
| 20090234499 | Nielsen et al. | Sep 2009 | A1 |
| 20090237317 | Rofougaran | Sep 2009 | A1 |
| 20090240371 | Wang et al. | Sep 2009 | A1 |
| 20090248200 | Root | Oct 2009 | A1 |
| 20090259339 | Wright et al. | Oct 2009 | A1 |
| 20090278912 | Carter | Nov 2009 | A1 |
| 20090323648 | Park et al. | Dec 2009 | A1 |
| 20100010672 | Wang et al. | Jan 2010 | A1 |
| 20100010673 | Wang et al. | Jan 2010 | A1 |
| 20100017046 | Cheung et al. | Jan 2010 | A1 |
| 20100019715 | Roe et al. | Jan 2010 | A1 |
| 20100026239 | Li et al. | Feb 2010 | A1 |
| 20100030378 | Choi et al. | Feb 2010 | A1 |
| 20100030578 | Siddique et al. | Feb 2010 | A1 |
| 20100037418 | Hussey et al. | Feb 2010 | A1 |
| 20100041998 | Postel | Feb 2010 | A1 |
| 20100051596 | Diedrick et al. | Mar 2010 | A1 |
| 20100054566 | Toda | Mar 2010 | A1 |
| 20100063636 | Matsumoto et al. | Mar 2010 | A1 |
| 20100063848 | Kremer et al. | Mar 2010 | A1 |
| 20100066804 | Shoemake et al. | Mar 2010 | A1 |
| 20100070079 | Mangaser et al. | Mar 2010 | A1 |
| 20100073455 | Iwabuchi et al. | Mar 2010 | A1 |
| 20100073490 | Wang et al. | Mar 2010 | A1 |
| 20100076600 | Cross et al. | Mar 2010 | A1 |
| 20100085874 | Noy et al. | Apr 2010 | A1 |
| 20100088232 | Gale | Apr 2010 | A1 |
| 20100115418 | Wang et al. | May 2010 | A1 |
| 20100116566 | Ohm et al. | May 2010 | A1 |
| 20100118112 | Nimri et al. | May 2010 | A1 |
| 20100128104 | Fabregat et al. | May 2010 | A1 |
| 20100131103 | Herzog et al. | May 2010 | A1 |
| 20100145479 | Griffiths | Jun 2010 | A1 |
| 20100153317 | Lee | Jun 2010 | A1 |
| 20100157825 | Anderlind et al. | Jun 2010 | A1 |
| 20100171826 | Hamilton et al. | Jul 2010 | A1 |
| 20100191375 | Wright et al. | Jul 2010 | A1 |
| 20100217438 | Kawaguchi et al. | Aug 2010 | A1 |
| 20100228249 | Mohr et al. | Sep 2010 | A1 |
| 20100238323 | Englund | Sep 2010 | A1 |
| 20100243344 | Wyrobek et al. | Sep 2010 | A1 |
| 20100253775 | Yamaguchi et al. | Oct 2010 | A1 |
| 20100268383 | Wang et al. | Oct 2010 | A1 |
| 20100278086 | Pochiraju et al. | Nov 2010 | A1 |
| 20100286905 | Goncalves | Nov 2010 | A1 |
| 20100287006 | Cannon et al. | Nov 2010 | A1 |
| 20100299391 | Demarta et al. | Nov 2010 | A1 |
| 20100301679 | Murray et al. | Dec 2010 | A1 |
| 20100315352 | Hamamura et al. | Dec 2010 | A1 |
| 20100323783 | Nonaka et al. | Dec 2010 | A1 |
| 20100331052 | Watanabe | Dec 2010 | A1 |
| 20110022705 | Yellamraju et al. | Jan 2011 | A1 |
| 20110032373 | Forutanpour et al. | Feb 2011 | A1 |
| 20110050841 | Wang et al. | Mar 2011 | A1 |
| 20110071672 | Sanders et al. | Mar 2011 | A1 |
| 20110071675 | Wells et al. | Mar 2011 | A1 |
| 20110071702 | Wang et al. | Mar 2011 | A1 |
| 20110072114 | Hoffert et al. | Mar 2011 | A1 |
| 20110077852 | Ragavan et al. | Mar 2011 | A1 |
| 20110107374 | Roberts et al. | May 2011 | A1 |
| 20110153198 | Kokkas et al. | Jun 2011 | A1 |
| 20110167151 | Nishiyama | Jul 2011 | A1 |
| 20110169832 | Brown et al. | Jul 2011 | A1 |
| 20110172822 | Ziegler et al. | Jul 2011 | A1 |
| 20110187875 | Sanchez et al. | Aug 2011 | A1 |
| 20110190930 | Hanrahan et al. | Aug 2011 | A1 |
| 20110193949 | Nambakam et al. | Aug 2011 | A1 |
| 20110195701 | Cook et al. | Aug 2011 | A1 |
| 20110213210 | Temby et al. | Sep 2011 | A1 |
| 20110218674 | Stuart et al. | Sep 2011 | A1 |
| 20110228112 | Kaheel et al. | Sep 2011 | A1 |
| 20110231050 | Goulding | Sep 2011 | A1 |
| 20110231796 | Vigil | Sep 2011 | A1 |
| 20110245973 | Wang et al. | Oct 2011 | A1 |
| 20110246015 | Cummings et al. | Oct 2011 | A1 |
| 20110246929 | Jones et al. | Oct 2011 | A1 |
| 20110249079 | Santamaria et al. | Oct 2011 | A1 |
| 20110280551 | Sammon | Nov 2011 | A1 |
| 20110288417 | Pinter et al. | Nov 2011 | A1 |
| 20110288682 | Pinter et al. | Nov 2011 | A1 |
| 20110292193 | Wang et al. | Dec 2011 | A1 |
| 20110301759 | Wang et al. | Dec 2011 | A1 |
| 20110306400 | Nguyen | Dec 2011 | A1 |
| 20120010518 | Sarel | Jan 2012 | A1 |
| 20120023506 | Maeckel et al. | Jan 2012 | A1 |
| 20120036484 | Zhang et al. | Feb 2012 | A1 |
| 20120059946 | Wang | Mar 2012 | A1 |
| 20120072023 | Ota | Mar 2012 | A1 |
| 20120072024 | Wang et al. | Mar 2012 | A1 |
| 20120092157 | Tran | Apr 2012 | A1 |
| 20120095352 | Tran | Apr 2012 | A1 |
| 20120113856 | Krishnaswamy | May 2012 | A1 |
| 20120143906 | Sibbold et al. | Jun 2012 | A1 |
| 20120154509 | Erb | Jun 2012 | A1 |
| 20120162472 | Amling et al. | Jun 2012 | A1 |
| 20120176525 | Garin et al. | Jul 2012 | A1 |
| 20120182392 | Kearns et al. | Jul 2012 | A1 |
| 20120185095 | Rosenstein et al. | Jul 2012 | A1 |
| 20120191246 | Roe et al. | Jul 2012 | A1 |
| 20120191464 | Stuart et al. | Jul 2012 | A1 |
| 20120197464 | Wang et al. | Aug 2012 | A1 |
| 20120203731 | Nelson et al. | Aug 2012 | A1 |
| 20120215380 | Fouillade et al. | Aug 2012 | A1 |
| 20120215830 | Anguiano | Aug 2012 | A1 |
| 20120218374 | Matula et al. | Aug 2012 | A1 |
| 20120220372 | Cheung et al. | Aug 2012 | A1 |
| 20120242865 | Vartanian et al. | Sep 2012 | A1 |
| 20120245957 | Mathison | Sep 2012 | A1 |
| 20120291809 | Kuhe et al. | Nov 2012 | A1 |
| 20120316676 | Fouillade et al. | Dec 2012 | A1 |
| 20130002794 | Hines et al. | Jan 2013 | A1 |
| 20130035790 | Olivier et al. | Feb 2013 | A1 |
| 20130044180 | Shintani | Feb 2013 | A1 |
| 20130065604 | Werner et al. | Mar 2013 | A1 |
| 20130097264 | Bangalore et al. | Apr 2013 | A1 |
| 20130100269 | Tashiro et al. | Apr 2013 | A1 |
| 20130113871 | Ballantyne et al. | May 2013 | A1 |
| 20130141519 | Sayeed et al. | Jun 2013 | A1 |
| 20130158720 | Zywicki et al. | Jun 2013 | A1 |
| 20130166137 | Ahn et al. | Jun 2013 | A1 |
| 20130179067 | Trowbridge et al. | Jul 2013 | A1 |
| 20130198321 | Martin et al. | Aug 2013 | A1 |
| 20130246084 | Parmanto et al. | Sep 2013 | A1 |
| 20130250938 | Anandakumar et al. | Sep 2013 | A1 |
| 20130275922 | Pinter et al. | Oct 2013 | A1 |
| 20130304301 | Rakshit | Nov 2013 | A1 |
| 20130325244 | Wang et al. | Dec 2013 | A1 |
| 20140009561 | Sutherland et al. | Jan 2014 | A1 |
| 20140015914 | Delaunay | Jan 2014 | A1 |
| 20140047022 | Chan et al. | Feb 2014 | A1 |
| 20140085543 | Hartley et al. | Mar 2014 | A1 |
| 20140128103 | Joao et al. | May 2014 | A1 |
| 20140135990 | Stuart et al. | May 2014 | A1 |
| 20140139616 | Pinter et al. | May 2014 | A1 |
| 20140155755 | Pinter et al. | Jun 2014 | A1 |
| 20140207286 | Wang et al. | Jul 2014 | A1 |
| 20140254896 | Zhou et al. | Sep 2014 | A1 |
| 20140267549 | Pinter et al. | Sep 2014 | A1 |
| 20150055838 | Taylor et al. | Feb 2015 | A1 |
| 20150077502 | Jordan et al. | Mar 2015 | A1 |
| 20150081338 | Lai et al. | Mar 2015 | A1 |
| 20150088310 | Pinter et al. | Mar 2015 | A1 |
| 20150296177 | Pinter et al. | Oct 2015 | A1 |
| 20150314449 | Wang et al. | Nov 2015 | A1 |
| 20160229058 | Pinter et al. | Aug 2016 | A1 |
| 20160283685 | Jordan et al. | Sep 2016 | A1 |
| 20160358276 | Stephenson | Dec 2016 | A1 |
| 20170023944 | Wang et al. | Jan 2017 | A1 |
| 20170106738 | Gillett | Apr 2017 | A1 |
| 20170127019 | Pinter et al. | May 2017 | A1 |
| 20170144299 | Lafaye et al. | May 2017 | A1 |
| 20180099412 | Pinter et al. | Apr 2018 | A1 |
| 20180263703 | Pinter et al. | Sep 2018 | A1 |
| 20190066839 | Jordan et al. | Feb 2019 | A1 |
| 20200009736 | Pinter et al. | Jan 2020 | A1 |
| 20200128208 | Pinter et al. | Apr 2020 | A1 |
| Number | Date | Country |
|---|---|---|
| 1216200 | May 2000 | AU |
| 2289697 | Nov 1998 | CA |
| 1404695 | Mar 2003 | CN |
| 1554193 | Dec 2004 | CN |
| 1554985 | Dec 2004 | CN |
| 1561923 | Jan 2005 | CN |
| 1743144 | Mar 2006 | CN |
| 101049017 | Oct 2007 | CN |
| 101106939 | Jan 2008 | CN |
| 101151614 | Mar 2008 | CN |
| 100407729 | Jul 2008 | CN |
| 101390098 | Mar 2009 | CN |
| 101507260 | Aug 2009 | CN |
| 101730894 | Jun 2010 | CN |
| 101866396 | Oct 2010 | CN |
| 101978365 | Feb 2011 | CN |
| 102203759 | Sep 2011 | CN |
| 101106939 | Nov 2011 | CN |
| 466492 | Jan 1992 | EP |
| 488673 | Jun 1992 | EP |
| 0981905 | Jan 2002 | EP |
| 1262142 | Dec 2002 | EP |
| 1304872 | Apr 2003 | EP |
| 1536660 | Jun 2005 | EP |
| 1573406 | Sep 2005 | EP |
| 1594660 | Nov 2005 | EP |
| 1763243 | Mar 2007 | EP |
| 1791464 | Jun 2007 | EP |
| 1800476 | Jun 2007 | EP |
| 1819108 | Aug 2007 | EP |
| 1856644 | Nov 2007 | EP |
| 1536660 | Apr 2008 | EP |
| 1928310 | Jun 2008 | EP |
| 1232610 | Jan 2009 | EP |
| 2027716 | Feb 2009 | EP |
| 2145274 | Jan 2010 | EP |
| 2214111 | Aug 2010 | EP |
| 2263158 | Dec 2010 | EP |
| 2300930 | Mar 2011 | EP |
| 2342651 | Jul 2011 | EP |
| 1279081 | Jan 2012 | EP |
| 2431261 | Apr 2007 | GB |
| H07-194609 | Aug 1995 | JP |
| H07-213753 | Aug 1995 | JP |
| H07-248823 | Sep 1995 | JP |
| H07-257422 | Oct 1995 | JP |
| H08-84328 | Mar 1996 | JP |
| H08-166822 | Jun 1996 | JP |
| H08-320727 | Dec 1996 | JP |
| H09-267276 | Oct 1997 | JP |
| H10-79097 | Mar 1998 | JP |
| H10-288689 | Oct 1998 | JP |
| H11-220706 | Aug 1999 | JP |
| 2000-032319 | Jan 2000 | JP |
| 2000-049800 | Feb 2000 | JP |
| 2000-079587 | Mar 2000 | JP |
| 2000-196876 | Jul 2000 | JP |
| 2001-088124 | Apr 2001 | JP |
| 2001-125641 | May 2001 | JP |
| 2001-147718 | May 2001 | JP |
| 2001-179663 | Jul 2001 | JP |
| 2001-188124 | Jul 2001 | JP |
| 2001-198865 | Jul 2001 | JP |
| 2001-198868 | Jul 2001 | JP |
| 2001-199356 | Jul 2001 | JP |
| 2002-000574 | Jan 2002 | JP |
| 2002-046088 | Feb 2002 | JP |
| 2002-101333 | Apr 2002 | JP |
| 2002-112970 | Apr 2002 | JP |
| 2002-235423 | Aug 2002 | JP |
| 2002-305743 | Oct 2002 | JP |
| 2002-321180 | Nov 2002 | JP |
| 2002-355779 | Dec 2002 | JP |
| 2004-181229 | Jul 2004 | JP |
| 2004-524824 | Aug 2004 | JP |
| 2004-261941 | Sep 2004 | JP |
| 2004-289379 | Oct 2004 | JP |
| 2005-028066 | Feb 2005 | JP |
| 2005-059170 | Mar 2005 | JP |
| 2005-111083 | Apr 2005 | JP |
| 2006-035381 | Feb 2006 | JP |
| 2006-508806 | Mar 2006 | JP |
| 2006-109094 | Apr 2006 | JP |
| 2006-224294 | Aug 2006 | JP |
| 2006-246438 | Sep 2006 | JP |
| 2007-007040 | Jan 2007 | JP |
| 2007-081646 | Mar 2007 | JP |
| 2007-232208 | Sep 2007 | JP |
| 2007-316966 | Dec 2007 | JP |
| 2009-125133 | Jun 2009 | JP |
| 2010-064154 | Mar 2010 | JP |
| 2010-532109 | Sep 2010 | JP |
| 2010-246954 | Nov 2010 | JP |
| 10-2006-0037979 | May 2006 | KR |
| 10-2009-0012542 | Feb 2009 | KR |
| 10-2010-0019479 | Feb 2010 | KR |
| 10-2010-0139037 | Dec 2010 | KR |
| 10-2011-0102585 | Sep 2011 | KR |
| 9306690 | Apr 1993 | WO |
| 9742761 | Nov 1997 | WO |
| 199851078 | Nov 1998 | WO |
| 9967067 | Dec 1999 | WO |
| 2000025516 | May 2000 | WO |
| 2000033726 | Jun 2000 | WO |
| 0131861 | May 2001 | WO |
| 2003077745 | Sep 2003 | WO |
| 2004008738 | Jan 2004 | WO |
| 2004012018 | Feb 2004 | WO |
| 2004075456 | Sep 2004 | WO |
| 2006012797 | Feb 2006 | WO |
| 2006044847 | Apr 2006 | WO |
| 2006078611 | Jul 2006 | WO |
| 2007041295 | Apr 2007 | WO |
| 2007041038 | Jun 2007 | WO |
| 2008100272 | Aug 2008 | WO |
| 2008100272 | Oct 2008 | WO |
| 2009117274 | Sep 2009 | WO |
| 2009128997 | Oct 2009 | WO |
| 2009145958 | Dec 2009 | WO |
| 2010006205 | Jan 2010 | WO |
| 2010006211 | Jan 2010 | WO |
| 2010033666 | Mar 2010 | WO |
| 2010047881 | Apr 2010 | WO |
| 2010062798 | Jun 2010 | WO |
| 2010065257 | Jun 2010 | WO |
| 2010120407 | Oct 2010 | WO |
| 2011028589 | Mar 2011 | WO |
| 2011028589 | Apr 2011 | WO |
| 2011097130 | Aug 2011 | WO |
| 2011097132 | Aug 2011 | WO |
| 2011109336 | Sep 2011 | WO |
| 2011097132 | Dec 2011 | WO |
| 2011149902 | Dec 2011 | WO |
| 2012061932 | May 2012 | WO |
| 2013097264 | Jul 2013 | WO |
| 2013176762 | Nov 2013 | WO |
| Entry |
|---|
| Paulos et al., “Social Tele-Embodiment Understanding Presence”, Autonomous Robots, vol. 11, Issue 1, Kluwei Academic Publishers, Jul. 2001, pp. 87-95. |
| Paulos et al., “Ubiquitous Tele-Embodiment Applications and Implications”, International Journal of Human Computer Studies, vol. 46, No. 6, Jun. 1997, pp. 861-877. |
| Paulos, Eric John Canny, “Video of PRoP 2 at Richmond Field Station”, www.prop.org Printout of Home Page of Website and two-page Transcript of the audio portion of said PRoP Video, May 2001, 2 pages. |
| PictureTel Corporation, “PictureTel Live200 for Windows NT Product Guide”, 1997, 63 pages. |
| Pin et al., “A New Family of Omnidirectional and Holonomic Wheeled Platforms for Mobile Robots”, IEEE Transactions an Robotics and Automation, vol. 10, No. 4, Aug. 1994, pp. 480-489. |
| Piquepaille, Roland, “This Blog and its RSS Feed Are Moving”, Roland Piquepaille's Technology Trends, How new technologies are modifying our way of life, Oct. 31, 2004, 2 pages. |
| West et al., “Design of Ball Wheel Mechanisms for Omnidirectional Vehicles with Full Mobility and Invariant Kinematics”, Journal of Mechanical Design, vol. 119, Jun. 1997, pp. 153-161. |
| Yamasaki et al., “Applying Personal Robots and Active Interface to Video Conference Systems”, 6th International Conference on Human Computer Interaction, vol. B, 1995, pp. 243-248. |
| Yamauchi, Brian, “PackBot: A Versatile Platform for Military Robotics”, Proceedings of SPIE for Military Robotics, 2004, pp. 228-237. |
| Yong et al., “Robot Task Execution with Telepresence Using Virtual Reality Technology”, 1998 International Conference on Mechatronic Technology, Nov. 30-Dec. 2, 1998, pp. 1-8. |
| Zambroski, James, “CMU, Pitt Developing ‘Nursebot’”, available online at <http://www.cs.cmu.edu/˜nursebot/web/press/tribunereview.html>, Oct. 27, 2000, 3 pages. |
| Radvision, “Making Sense of Bandwidth the Netsense Way”, Network Congestion in Unmanaged Networks Bandwidth Estimation and Adaptation Techniques,White Paper, Radvision's Netsense Technology, 2010, 7 pages. |
| Reynolds et al., “Review of Robotic Telemedicine Utilization in Intensive Care Units (ICUs)”, 11th Annual ATA Symposium, Tampa, Florida, 2011, 1 page. |
| Roach, Adam, “Automatic Call Back Service in SIP”, Internet Engineering Task Force, Internet Draft, Category Informational, Mar. 2000, 8 pages. |
| Rovetta et al., “A New Telerobotic Application: Remote Laparoscopic Surgery Using Satellites and optical fiber Networks for Data Exchange”, International Journal of Robotics Research, vol. 15, No. 3, Jun. 1, 1996, pp. 267-279. |
| Roy et al., “Towards Personal Service Robots for the Elderly”, Workshop on Interactive Robots and Entertainment (WIRE 2000), available online at <http://www.ri.cmu.edu/pb_files/pub2/roy_nicholas_2000_1/roy_nicholas_2000_1.pdf>, vol. 25, Apr. 30-May 1, 2000, 7 pages. |
| Sachs et al., “Virtual Visit: Improving Communication for Those Who Need it Most”, Studies in Health Technology and Informatics, vol. 94, Medicine Meets Virtual Reality 11, 2003, pp. 302-308. |
| Salemi et al., “MILO: Personal Robot Platform”, IEEE/RSJ International Conference on Intelligent Robots and Systems, Aug. 2005, pp. 4089-4094. |
| Sandt et al., “Perceptions for a Transport Robot in Public Environments”, Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 1, Sep. 7-11, 1997, pp. 360-365. |
| Sawyer, Robert J., “Inventing the Future: 2000 Years of Discovery”, available online at <http://www.sfwriter.com/pritf.htm>, Jan. 2, 2000, 2 pages. |
| Schaeffer et al., “Care-O-BotTM: The Concept of a System for Assisting Elderiy or Disabled Persons in Home Environments”, Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society, vol. 4, 1998, pp. 2476-2481. |
| Schraft et al., “Care-O-bot™: The Concept of a System for Assisting Elderly or Disabled Persons in Home Environments”, IEEE Proceedings of the 24th Annual Conference of the Industrial Electronics Society, IECON '98, Aug. 31-Sep. 4, 1998, pp. 2476-2481. |
| Schultz et al., “Web Interfaces for Mobile Robots in Public Places”, IEEE Robotics & Automation Magazine, vol. 7, No. 1, Mar. 2000, pp. 48-56. |
| Shimoga et al., “Touch and Force Reflection for Telepresence Surgery”, Proceedings of the 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Nov. 1994, pp. 1049-1050. |
| Siegwart et al., “Interacting Mobile Robots on the Web”, Proceedings of the 1999 IEEE International Conference on Robotics and Automation, May 1999, pp. 1-7. |
| Simmons et al., “Xavier: An Autonomous Mobile Robot on the Web”, IEEE Robotics and Automation Magazine, 1999, pp. 43-48. |
| Stephenson, Gary, “Dr. Robot Tested at Hopkins”, available online at <http://www.hopkinsmedicine.org/press/2003/august/030805.htm>, Aug. 5, 2003, 2 pages. |
| Stoianovici et al., “Robotic Tools for Minimally Invasive Urologic Surgery”, Complications of Urologic Laparoscopic Surgery: Recognition, Management and Prevention, Dec. 2002, 17 pages. |
| Suplee, Curt, “Mastering the Robot”, The Washington Post, Washington Post Staff Writer, Sep. 17, 2000, 5 pages. |
| Tahboub et al., “Dynamics Analysis and Control of a Holonomic Vehicle With Continuously Variable Transmission”, Transactions of the ASME, Journal of Dynamic Systems, Measurement and Control, vol. 124, Mar. 2002, pp. 118-126. |
| Telepresence Research, Inc., “The Telepresence Mobile Robot System”, available online at <http://www.telepresence.com/telepresence-research/TELEROBOT/>, Feb. 20, 1995, 3 pages. |
| Tendick et al., “Human-Machine Interfaces for Minimally Invasive Surgery”, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 6, Oct. 30-Nov. 2, 1997, pp. 2771-2776. |
| Theodosiou et al., “MuLVAT: A Video Annotation Tool Based on XML-Dictionaries and Shot Clustering”, 19th International Conference, Artificial Neural Networks—ICANN 2009, Part II, Sep. 14-17, 2009, pp. 913-922. |
| Thrun et al., “Probabilistic Algorithms and the Interactive Museum Tour-Guide Robot Minerva”, International Journal of Robotics Research, vol. 19, 2000, pp. 1-35. |
| Time, Lists, “Office Coworker Robot”, Best Inventions of 2001, available online at <http://content.time.com/time/specials/packages/article/0,28804,1936165_1936255_1936640,00.html>, Nov. 19, 2001, 2 pages. |
| Tipsuwan et al., “Gain Adaptation of Networked Mobile Robot to Compensate QoS Deterioration”, 28th Annual Conference of the Industrial Electronics Society, vol. 4, Nov. 5-8, 2002, pp. 3146-3151. |
| Tsui et al., “Exploring Use Cases for Telepresence Robots”, 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI), Mar. 2011, pp. 11-18. |
| Tyrrell et al., “Teleconsultation in Psychology: The Use of Videolinks for Interviewing and Assessing Elderly Patients”, Age and Ageing, vol. 30, No. 3, May 2001, pp. 191-195. |
| Tzafestas et al., “VR-Based Teleoperation of a Mobile Robotic Assistant: Progress Report”, Technical Report DEMO 2000/13, Institute of Informatics and Telecommunications, National Center for Scientific Research Demokritos, Nov. 2000, pp. 1-23. |
| UMASS Lowell Robotics Lab, “Robotics Lab @ UMASS Lowell”, Brochure, 2011, 2 pages. |
| Urquhart, Kim, “InTouch's Robotic Companion ‘Beams Up’ Healthcare Experts”, Medical Device Daily, vol. 7, No. 39, Feb. 27, 2003, pp. 1-7. |
| Video Middleware Cookbook, “H.350 Directory Services for Multimedia”, 2 pages. |
| Weaver et al., “Monitoring and Controlling Using the Internet and Java”, Proceedings of the 25th Annual Conference of the IEEE Industrial Electronics Society, vol. 3, 1999, pp. 1152-1158. |
| Weiss et al., “PEBBLES: A Personal Technology for Meeting Education, Social and Emotional Needs of Hospitalised Children”, Personal and Ubiquitous Computing, vol. 5, No. 3, Aug. 2001, pp. 157-168. |
| Wu, et al., “Eulerian Video Magnification for Revealing Subtle Changes in the World”, ACM Transactions on Graphics, vol. 31, 2012, 15 pages. |
| Jouppi, et al., “BiReality: Mutually-lmmersive Telepresence”, 2004, 8 pages. |
| Screenshot Showing Google Date for Lemaire Telehealth Manual, screenshot retrieved on Dec. 18, 2014, 1 page. |
| Appeal from the U.S. District Court for the Central District of California in case No. 11-cv-9185, Judge Percy Anderson, Joint Appendix, vol. I of IV, Jun. 24, 2013, pp. A1-A6357. |
| Appeal from the U.S. District Court for the Central District of California in case No. 11-cv-9185, Judge Percy Anderson, Joint Appendix, vol. II of IV, Jun. 24, 2013, pp. A6849-A10634. |
| Appeal from the U.S. District Court for the Central District of California in case No. 11-cv-9185, Judge Percy Anderson, Joint Appendix, vol. III of IV, Jun. 24, 2013, pp. A10654-A15517. |
| Appeal from the U.S. District Court for the Central District of California in case No. 11-cv-9185, Judge Percy Anderson, Joint Appendix, vol. IV of IV, Jun. 24, 2013, pp. A15677-A18127. |
| Appeal from the U.S. District Court for the Central District of California in No. 11-CV-9185, Judge Percy Anderson, May 39, 2014, pp. 1-48. |
| Civil Minutes-General: Case No. CV 11-9185PA (AJWx), InTouch Tech., Inc. v. VGO Commons, Inc., Sep. 10, 2012, 7 pages. |
| Defendant VGO Communications, Inc.'s Invalidity Contentions Pursuant to the Feb. 27, 2012 Civil Minute Order, May 2, 2012, 143 pages. |
| Defendant-Counterclaimant VGO Communications, Inc.'s Supplemental Invalidity Contentions Pursuant to the Feb. 27, 2012 Civil Minute Order, May 14, 2012, 228 pages. |
| “Google translation of: Innovations Report”, From research project to television star: Care-O-bot in ZDF series, available online at <http://www.innovations-report.de/specials/printa.php?id=5157>, Sep. 28, 2001, 2 pages. |
| “Magne Charge”, Smart Power for Electric Vehicles, Aug. 26, 1997, 2 pages. |
| “More Online Robots: Robots that Manipulate”, available online at <http://ford.ieor.berkeley.edu/ir/robots_a2.html>, Retrieved on Nov. 23, 2010, Aug. 2001, 2 pages. |
| “MPEG File Format Summary”, available online at <http://www.fileformat.info/format/mpeg/egff.htm>, Feb. 1, 2001, 8 pages. |
| “Nomad Scout Language Reference Manual”, Nomadic Technologies, Software Version 2.7, Part No. DOC00002, Jul. 12, 1999, 47 pages. |
| “Nomad Scout User's Manual”, Nomadic Technologies, Software Version 2. 7, Part No. DOC00004, Jul. 12, 1999, pp. 1-59. |
| Opening Brief for Plaintiff-Appellant InTouch Technologies, Inc., Appeal from the U.S. District Court for the Central District of California in Case No. 11-cv-9185, Judge Percy Anderson, Apr. 12, 2013, 187 pages. |
| PictureTel Adds New Features and Functionality to Its Award-Winning Live200 Desktop Videoconferencing System, PR Newswire Association, LLC, Gale, Cengage Learning, Jun. 13, 1997, 5 pages. |
| Reply Brief for Defendant-Appellee VGO Communications, Inc., Appeal from the U.S. District Court for the Central District of California, in Case No. 2:11-cv-9185, Judge Percy Anderson, May 28, 2013, 75 pages. |
| Reply Brief for Plaintiff-Appellant InTouch Technologies, Inc., Appeal from the U.S. District Court for the Central Districl of California in Case No. 11-cv-9185, Judge Percy Anderson, Jun. 14, 2013, 39 pages. |
| “Robart I, II, III”, Spawar, Systems Center Pacific, 1998, 8 pages. |
| Using your Infrared Cell Phone Camera, available online at <http://www.catsdomain.com/xray/about.htm>, Courtesy of Internet Wayback Machine, Jan. 30, 2010, 4 pages. |
| Ivanova, Natali, “Master's Thesis: Internet Based Interface for Control of a Mobile Robot”, Department of Numerical Analysis and Computer Science, 2003, 59 pages. |
| Jacobs et al., “Applying Telemedicine to Outpatient Physical Therapy”, AMIA, Annual Symposium Proceedings, 2002, 1 page. |
| Jenkins et al., “Telehealth Advancing Nursing Practice”, Nursing Outlook, vol. 49, No. 2, Mar. 2001, pp. 100-105. |
| Johanson, Mathias, “Supporting Video-Mediated Communication Over the Internet”, Depailment of Computer Engineering, Chalmers University of Technology, Gothenburg, Sweden, 2003, 222 pages. |
| ACM Digital Library Record, “Autonomous Robots vol. 11 Issue 1”, available online at <http://dl.acm.org/citation.cfm?id=591550&picked=prox&cfid=360891374&cftoken=35225929>, Jul. 2001, 2 pages. |
| Active Media, Inc., “Saphira Software Manual”, Saphira Version 5.3, 1997, 105 pages. |
| Activmedia Robotics, “Pioneer 2/PeopleBot TM”, Operations Manual, Version 9, Oct. 2001, 78 pages. |
| Adams, Chris, “Simulation of Adaptive Behavior (SAB'02)”, Mobile Robotics Research Group, The Seventh International Conference, retrieved on Jan. 22, 2014, available online at <http://www.dai.ed.ac.uk/groups/mrg/MRG.html>, Aug. 4-11, 2002, 1 page. |
| Ando et al., “A Multimedia Self-service Terminal with Conferencing Functions”, Robot and Human Communication, Tokyo, Proceedings of 4th IEEE International Workshop, Jul. 5-7, 1995, pp. 357-362. |
| Android Amusement Corp., “What Marketing Secret Renting Robots from Android Amusement Corp!”, (Advertisement), 1982, 1 page. |
| Apple Inc., “I Phone”, iPhone Series, XP002696350, Sep. 21, 2012, pp. 1-29. |
| Applebome, Peter, “Planning Domesticated Robots for Tomorrow's Household”, New York Times, available online at <http://www.theoldrobots.com/images17/dc17.jpg>, Mar. 4, 1982, pp. 21 and 23. |
| Bar-Cohen et al., “Virtual Reality Robotic Telesurgery Simulations Using MEMICA Haptic System”, Proceedings of SPIE's 8th Annual International Symposium on Smart Structures and Materials, Mar. 5-8, 2001, pp. 1-7. |
| Barrett, Rick, “Video Conferencing Business Soars as Companies Cut Travel; Some Travel Cuts Are Permanent”, available online at <http://www.ivci.com/international_videoconferencing_news_videoconferencing_news_19.html>, May 13, 2002, 2 pages. |
| Bartholomew, “Pharmacy Apothecary”, available online at <http://classes.bnf.fr/ema/grands/034.htm>, retrieved on Jul. 26, 2012, 2 pages. |
| Bauer et al., “Remote Telesurgical Mentoring: Feasibility and Efficacy”, IEEE, Proceedings of the 33rd Hawaii International Conference on System Sciences, 2000, pp. 1-9. |
| Bauer, Jeffrey C., “Service Robots in Health Care: The Evolution of Mechanical Solutions to Human Resource Problems”, Bon Secours Health System Inc., Technology Early Warning System(TEWS), Future of Service Robots in Health Care, Jun. 2003, pp. 1-10. |
| Bischoff, Rainer, “Design Concept and Realization of the Humanoid Service Robot HERMES”, Field and Service Robotics, Springer, 1998, pp. 485-492. |
| Blackwell, Gerry, “Video: A Wireless LAN Killer App?”, available online at <http://www.wi-fiplanet.com/columns/article.php/1010261/Video-A-Wireless-LAN-Killer>, Apr. 16, 2002, 4 pages. |
| Blaer et al., “TopBot: Automated Network Topology Detection With a Mobile Robot”, IEEE, Proceedings of the 2003 International Conference on Robotics 7 Automation, Taipei, Taiwan, Sep. 14-19, 2003, pp. 1582-1587. |
| Bradner, S., “The Internet Standards Process—Revision 3”, Network Working Group, Request for Comments: 2026, BCP: 9, Obsoletes: 1602, Category: Best Current Practice, Oct. 1996, pp. 1-36. |
| Brenner, Pablo, “A technical tutorial on the IEEE 802.11 protocol”, Breezecom Wireless Communications, 1997, pp. 1-24. |
| Breslow et al., “Effect of a Multiple-Site Intensive Care Unit Telemedicine Program on Clinical and Economic Outcome an Alternative Paradigm for Intensivist Starnng”, Critical Care Med, vol. 32, No. 1, Jan. 2004, pp. 31-38. |
| Brooks, Rodney, “A Robust Layered Control System for a Mobile Robot”, IEEE, Journal of Robotics and Automation, vol. 2, No. 1, Mar. 1986, pp. 14-23. |
| Brooks, Rodney Allen, “Remote Presence”, Abstracts from Flesh & Machines, How Robots Will Change US, Feb. 2002, pp. 131-147. |
| Celi et al., “The eICU: It's Not Just Telemedicine”, Critical Care Medicine vol. 29, No. 8 (Supplement), Aug. 2001, pp. 183-189. |
| Cheetham et al., “Interface Development for a Child's Video Conferencing Robot”, available online at <www.ryerson.ca/pebbles/publications/paper-iea200hfes-last.pdf>, 2000, 4 pages. |
| Christensen et al., “BeeSoft User's Guide and Reference”, Robots for the Real World™, Real World Interface, Inc ., Sep. 26, 1997, 203 pages. |
| Chu et al., “Detection of Target Mobile Signal Strength”, Technical Development, Motorola Inc, Jan. 1999, pp. 205-206. |
| Jouppi et al., “BiReality: Mutually-Immersive Telepresence”, Proceedings of the 12th Annual ACM International Conference on Multimedia, Oct. 10-16, 2004, pp. 860-867. |
| Jouppi et al., “First Steps Towards Mutually-lmmersive Mobile Telepresence”, Proceedings of the 2002 ACM Conference on Computer Supported Cooperative Work, Nov. 16-20, 2002, pp. 354-363. |
| Kanehiro et al., “Virtual Humanoid Robot Platform to Develop Controllers of Real Humanoid Robots without Porting”, IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 2, 2001, pp. 1093-1099. |
| Kaplan et al., “An Internet Accessible Telepresence”, Multimedia Systems Journal, vol. 5, 1996, 7 pages. |
| Keller et al., “Raven Interface Project”, The National Aviary's Teleconferencing Carnegie Mellon University Robot, Interaction and Visual Interface Design, School of Design, Carnegie Mellon University, 2001, 8 pages. |
| Khatib et al., “Robots in Human Environments”, Proceedings International Conference on Control, Automation, Robotics, and Vision ICRACV2000, 1999, 15 pages. |
| Knight et al., “Active Visual Alignment of a Mobile Stereo Camera Platform”, IEEE International Conference on Robotics and Automation, vol. 4, Apr. 24-28, 2000, pp. 3203-3208. |
| Koenen, Rob, “MPEG-4: a Powerful Standard for Use in Web and Television Environments”, (KPN Research), available online at <http://www.w3.org/Architecture/1998/06/Workshop/paper26>, Jul. 1, 1998, 4 pages. |
| Kurlowicz et al., “The Mini Mental State Examination (MMSE)”, Try This: Best Practices in Nursing Care to Older Adults, A series from the Hartford Institute of Geriatric Nursing, Issue No. 3, Jan. 1999, 2 pages. |
| Kuzuoka et al., “Can the GestureCam be a Surrogate?”, Proceedings of the Fourth European Conference on Computer-Supported Cooperative Work, 1995, pp. 181-196. |
| Lane, Earl, “Automated Aides”, Available online at <http://www.cs.cum.edu/nursebot/web/press/nd4380.htm>, Oct. 17, 2000, 4 pages. |
| Lee et al., “A Novel Method of Surgical Instruction: International Telementoring”, World Journal of Urology, vol. 16, No. 6, Dec. 1998, pp. 367-370. |
| Leifer et al., “VIPRR: A Virtually In Person Rehabilitation Robot”, Proceedings of 1997 International Conference on Rehabilitation Robotics, Apr. 14-15, 1997, 4 pages. |
| Lemaire, Edward, “Using Communication Technology to Enhance Rehabilitation Services: A Solution Oriented User Manual”, Institute for Rehabilitation Research and Development, Terry Fox Mobile Clinic, The Rehabilitation Centre, Ottawa, Ontario, Canada, Version 2.0, available online at <http://www.irrd.ca/telehealth/distfile/distman_v2_1.pdf>, 1998-2001, 104 pages. |
| Library of Congress, “008—Fixed-Length Data Elements (NR)”, MARC 21 Format for Classification Data, available online at <http://www.loc.gov/marc/classification/cd008.html>, Jan. 2000, pp. 1-14. |
| Lim et al., “Control to Realize Human-Like Walking of a Biped Humanoid Robot”, IEEE International Conference on Systems, Man, and Cybernetics, 2000, vol. 5, 2000, pp. 3271-3276. |
| Linebarger et al., “Concurrency Control Mechanisms for Closely Coupled Collaboration in Multithreaded Virtual Environments”, Department of Computer Science and Engineering, Lehigh University, vol. 13, 2004, 40 pages. |
| Long, William F., “Robot Navigation Technology”, available online at <http://www.atp.nist.gov/eao/sp950-1/helpmate.htm>, Mar. 1999, 3 pages. |
| Luna, Nancy, “Robot a New Face on Geriatric Care”, ocregister.com, Aug. 6, 2003, 3 pages. |
| Mack, Michael J., “Minimally Invasive and Robotic Surgery”, The Journal of the American Medical Association, vol. 285, No. 5, 2001, pp. 568-572. |
| Mair, G. M., “Telepresence—The Technology and its Economic and Social Implications”, Technology and Society, 1997. ‘Technology and Society at a Time of Sweeping Change’. Proceedings, 1997 International Symposium, Jun. 20-21, 1997, pp. 118-124. |
| Martin, Anya, “Brighter Days Ahead”, Assisted Living Today, vol. 9, Nov./Dec. 2002, pp. 19-22. |
| McCardle et al., “The Challenge of Utilizing New Technology in Design Education”, Loughborough University, IDATER 2000, 2000, pp. 122-127. |
| Meng et al., “E-Service Robot in Home Healthcare”, Proceedings of the 2000 IEEE/RSJ, International Conference on Intelligent Robots and Systems, 2000, pp. 832-837. |
| Metz, Cade, “HP Labs”, available online at <http://www.pcmag.eom/article2/0,2817,1130820,00.asp>, Jul. 1, 2003, 4 pages. |
| Michaud, Anne, “Introducing “Nursebot””, available online at <http://www.cs.cmu.edu/nursebot/web/press/globe 301/index.html>, 2001, 4 pages. |
| Microsoft Corporation, Inc., “Microsoft NetMeeting 3 Features excerpt from Official Microsoft NetMeeting 3.0 Book”, available online at <http://technet.microsoft.com/en-us/library/cc723477.aspx>, 2012, 6 pages. |
| Minsky, Marvin, “Telepresence”, OMNI Magazine, Jun. 1980, 6 pages. |
| Montemerlo, Reddy Whittaker, “Telepresence: Experiments in Next Generation Internet”, available online at <http://www.ri.cmu.edu/creative/archives.htm>, Oct. 20, 1998, 3 pages. |
| Murphy, Robin R., “Introduction to A1 Robotics”, A Bradford Book, The MIT Press, Cambridge, Massachusetts, London, England, 2000, 487 pages. |
| Nakajima et al., “A Multimedia Teleteaching System using an Electronic Whiteboard for Two Way Communication of Motion Videos and Chalkboards”, 2nd IEEE International Workshop on Robot and Human Communication, 1993, pp. 436-441. |
| Nakazato et al., “Group-Based Interface for Content-Based Image Retrieval”, Proceedings of the Working Conference on Advanced Visual Interfaces, 2002, pp. 187-194. |
| Nakazato et al., “ImageGrouper: A Group-Oriented User Interface for Content-Based Image Retrieval and Digital mage Arrangement”, Journal of Visual Languages & Computing, vol. 14, No. 4, Aug. 2003, pp. 45-46. |
| Nersc, “Berkeley Lab's RAGE Telepresence Robot Captures R&D100 Award”, Available online at <https://www.nersc.gov/news-publications/news/nersc-center-news/2002/berkeley-lab-s-rage-telepresence-robot-captures-r-and-d100-award/>, Jul. 2, 2002, 2 pages. |
| Nomadic Technologies Inc., “Nomad XR4000 Hardware Manual”, Release 1.0, Mar. 1999, 34 pages. |
| Noritsugu et al., “Application of Rubber Artificial Muscle Manipulator as a Rehabilitation Robot”, Mechatronics, IEEE/ASME Transactions, vol. 2, No. 4, Dec. 1997, pp. 259-267. |
| North, Michael, “Telemedicine: Sample Script and Specifications for a Demonstration of Simple Medical Diagnosis and Treatment Using Live Two-Way Video on a Computer Network”, Greenstar Corporation, 1998, 5 pages. |
| Ogata et al., “Development of Emotional Communication Robot: WAMOEBA-2R—Experimental Evaluation”, Proceedings of the 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 1, 2000, pp. 175-180. |
| Ogata et al., “Emotional Communication Robot: WAMOEBA-2R—Emotion Model and Evaluation Experiments”, Proceedings of the International Conference on Humanoid Robots, 2000, pp. 1-16. |
| Oh et al., “Autonomous Battery Recharging for Indoor Mobile Robots”, Proceedings of Australian Conference on Robotics and Automation, 2000, pp. 1-6. |
| Ojha, Anand K., “An application of Virtual Reality in Rehabilitation”, Proceedings of the 1994 IEEE Southeastcon 94. Creative Technology Transfer—A Global Affair, Apr. 1994, pp. 4-6. |
| Osborn, Jim, “Quality of Life Technology Center”, QoLT Research Overview: A National Science Foundation Engineering Research Center, Carnegie Mellon University of Pittsburgh, 2 pages. |
| Panusopone et al., “Performance comparison of MPEG-4 and H.263+ for streaming video applications”, Circuits Systems Signal Processing, vol. 20, No. 3, 2001, pp. 293-309. |
| Paulos et al., “A World Wide Web Telerobotic Remote Environment Browser”, Available online at <http://www.w3.org/Conferences/WWW4/Papers/326/>, 1995, 15 pages. |
| Paulos et al., “Designing Personal Tele-Embodiment”, Proceedings of IEEE International Conference on Robotics and Automation, vol. 4, May 16-20, 1998, pp. 3173-3178. |
| Paulos, Eric J., “Personal Tele-Embodiment”, Dissertation, Doctor of Philosophy in Computer Science in the Graduate Division of the University of California at Berkeley, 2001, 282 pages. |
| Weiss et al., “Telework and Video-Mediated Communication: Importance of Real-Time, Interactive Communication for Workers with Disabilities”, available online at <http://www.telbotics.com/research_3.htm>, retrieved on Nov. 23, 2010, 3 pages. |
| Zamrazil, Kristie, “Telemedicine in Texas: Public Policy Concerns”, Focus Report, House Research Organization, Texas House of Representatives, No. 76-22, May 5, 2000, pp. 1-16. |
| Zipperer, Lorri, “Robotic Dispensing System”, ISMP Medication Safety Alert!, vol. 4, No. 17, Aug. 25, 1999, 2 pages. |
| Zorn, Benjamin G., “Ubiquitous Telepresence”, Department of Computer Science, University of Colorado, 1996, 13 pages. |
| Cleary et al., “State of the Art in Surgical Robotics: Clinical Applications and Technology Challenges”, Feb. 24, 2002, pp. 1-26. |
| CMU Course 16X62, “Robot user's manual”, (describing the Nomad Scout), Carnegie Mellon University, Feb. 1, 2001, 11 pages. |
| CNN, “Floating ‘Droids’ to Roam Space Corridors of the Future”, Available online at <http://edition.cnn.com/2000/TECH/space/01/12/psa/>, Jan. 12, 2000, 3 pages. |
| cnn.com, “Paging Dr.Robot: Machine Helps Doctors with Patients”, Sep. 30, 2003, 3 pages. |
| Crowley, Susan L., “Hello to Our Future”, AARP Bulletin, Jan. 2000, 12 pages. |
| Dalton, Barnaby, “Techniques for Web Telerobotics”, PhD Thesis, University of Western Australia, 2001, 243 pages. |
| Dario et al., “A Robot Workstation for Diagnosis and Physical Therapy”, IEEE Catalog No. 88TH0234-5, 1989, pp. 67-72. |
| Davies, Brian, “Robotics in Minimally Invasive Surgery”, IEE Colloquium on Through the Keyhole: Microengineering n Minimally Invasive Surgery, 1995, pp. 1-2. |
| Davis, Erik, “Telefriend, Meet iRobot, The Smartest Webcam on Wheels”, Wired Magazine, Issue 8.09, Available online at <http://www.wired.com/wired/archive/8.09/irobot.html?pg=1&topic=&topic_set=>, Sep. 2000, 3 pages. |
| Dean et al., “1992 AAAI Robot Exhibition and Competition”, Articles, AI Magazine, vol. 14, No. 1, 1993, 15 pages. |
| Digiorgio, James, “Is Your Emergency Department of the Leading Edge?”, Chicago Hospital News, vol. 2, No. 12, 2005, 3 pages. |
| Dudenhoeffer et al., “Command and Control Architectures for Autonomous Micro-Robotic Forces”, FY00 Project Report, Idaho National Engineering and Environmental Laboratory Human Systems Engineering and Sciences Department, Idaho Falls, Apr. 2001, 43 pages. |
| Elhajj et al., “Real-Time Haptic Feedback in Internet-Based Telerobotic Operation”, IEEE International Conference on Electro/Information Technology, available online at <http://www.egr.msu.edu/˜ralab-web/cgi_bin/internet-teleoperation.php>, Jun. 2000, 10 pages. |
| Elhajj et al., “Supermedia in Internet-Based Telerobotic Operations”, Management of Multimedia on the InternetLecture Notes in Computer Science, Springer-Verlag Berlin Heidelberg, vol. 2216, Oct. 29-Nov. 1, 2001, pp. 359-372. |
| Elhajj et al., “Synchronization and Control of Supermedia Transmission via the Internet”, Proceedings of 2001 International Symposium on Intelligent Multimedia Video and Speech Processing, May 2-4, 2001, pp. 320-323. |
| Ellison et al., “Telerounding and Patient Satisfaction Following Surgery”, vol. 199, No. 4, Oct. 2004, pp. 523-530. |
| Evans et al., “The Trackless Robotic Courier”, PYXIS HelpMate®, 2007, 3 pages. |
| Fels et al., “Developing a Video-Mediated Communication System for Hospitalized Children”, Telemedicine Journal, vol. 5, No. 2, 1999, 30 pages. |
| Fetterman et al., “Videoconferencing Over the Internet”, 2001, 8 pages. |
| Fiorini et al., “Health Care Robotics: A Progress Report”, IEEE International Conference on Robotics and Automation, Apr. 20-25, 1997, pp. 1271-1276. |
| Fong, Terrence, “Collaborative Control: A Robot-Centric Model for Vehicle Teleoperation”, Doctoral Dissertation, Technical Report CMU-RI-TR-01-34, Robotics Institute, Carnegie Mellon University, Nov. 2001, 197 pages. |
| Fulbright et al., “SWAMI: An Autonomous Mobile Robot for Inspection of Nuclear Waste of Storage Facilities”, Autonomous Robots, vol. 2, 1995, pp. 225-235. |
| Gaidioz et al., “Synchronizing Network Probes to Avoid Measurement Intrusiveness with the Network Weather Service”, Proceedings of the Ninth International Symposium on High-Performance Distributed Computing, 2000, pp. 147-154. |
| Garner et al., “The Application of Telepresence in Medicine”, BT Technology Journal, vol. 15, No. 4, Oct. 1, 1997, pp. 181-187. |
| Ghiasi et al., “A Generic Web-based Teleoperations Architecture: Details and Experience”, Proceedings of SPIE, Telemanipulator and Telepresence Technologies VI, vol. 3840, No. 234, Sep. 19, 1999, 14 pages. |
| Goldberg et al., “Collaborative Teleoperation via the Internet”, IEEE International Conference on Robotics and Automation (ICRA), vol. 2, 2000, pp. 2019-2024. |
| Goldberg et al., “Desktop Teleoperation via the World Wide Web”, IEEE International Conference on Robotics and Automation, vol. 1, May 21-27, 1995, pp. 654-659. |
| Goldenberg et al., “Telemedicine in Otolaryngology”, American Journal of Otolaryngology, vol. 23,No. 1, 2002, pp. 35-43. |
| Goldman, Lea, “Machine Dreams”, available online at <http://www.forbes.com/global/2002/0527/043.html>, May 27, 2002, 5 pages. |
| Gostai, “Gostai Jazz: Robotic Telepresence”, available online at <http://www.robotshop.com/media/files/pdf/gostai-jazz-information-sheet.pdf>, 4 pages. |
| Gump, Michael D., “Robot Technology Improves VA Pharmacies”, U.S. Medicine Informational Central, Jul. 2001, 3 pages. |
| Al-Kassab et al., “A Review of Telemedicine”, Journal of Telemedicine and Telecare, vol. 5, Supplement 1, 1999, pp. 103-106. |
| Han et al., “Construction of an Omnidirectional Mobile Robot Platform Based on Active Dual-Wheel Caster Mechanisms and Development of a Control Simulator”, Journal of Intelligent and Robotic Systems, vol. 29, Issue 3, Nov. 2000, pp. 257-275. |
| Handley et al., “RFC 2327—SDP: Session Description Protocol”, available online at <http://www.faqs.org/rfcs/rfc2327.html>, Apr. 1998, 22 pages. |
| Hanebeck et al., “ROMAN: A Mobile Robotic Assistant for Indoor Service Applications”, IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 2, Sep. 7-11, 1997, pp. 518-525. |
| Harmo et al., “Moving Eye-Interactive Telepresence Over Internet With a Ball Shaped Mobile Robot”, available online at <http://automation.tkk.fi/files/tervetaas/MovingEye4.pdf>, 2000, 6 pages. |
| Haule et al., “Control Scheme for Delayed Teleoperation Tasks”, IEEE Pacific Rim Conference on Communications, Computers, and Signal Processing, May 17-19, 1995, pp. 157-160. |
| Hees, William P., “Communications Design for a Remote Presence Robot”, CSCI E-131b, Final Project, Jan. 14, 2002, 12 pages. |
| Herias et al., “Flexible Virtual and Remote Laboratory for Teaching Robotics”, Current Developments in Technology-Assisted Education, Jun. 2006, pp. 1959-1963. |
| Holmberg et al., “Development of a Holonomic Mobile Robot for Mobile Manipulation Tasks”, International Journal of Robotics Research, vol. 19, No. 11, Nov. 2000, pp. 1066-1074. |
| Ishiguro et al., “Integrating a Perceptual Information Infrastructure with Robotic Avatars: A Framework for Tele-Existence”, IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 2, 1999, pp. 1032-1038. |
| Ishihara et al., “Intelligent Microrobot DDS (Drug Delivery System) Measured and Controlled by Ultrasonics”, Intelligent Robots and Systems '91. Intelligence for Mechanical Systems, IEEE/RSJ International Workshop, vol. 2, Nov. 3-5, 1991, pp. 1145-1150. |
| ITU, “Call Completion Supplementary Services for H.323”, ITU-T, Telecommunication Standardization Sector of ITU, H.450.9, Series H: Audiovisual And Multimedia Systems, Nov. 2000, 63 pages. |
| ITU, “Call Intrusion Supplementary Service for H.323”, ITU-T, Telecommunication Standardization Sector of ITU, H.450.11, Series H: Audiovisual and Multimedia Systems, Mar. 2001, 59 pages. |
| ITU, “Packet-Based Multimedia Communications Systems”, ITU-T, Telecommunication Standardization Sector of ITU, H.323, Series H: Audiovisual and Multimedia Systems, Feb. 1998, 128 pages. |
| ITU, “Transmission of Non-Telephone Signals: A Far End Camera Control Protocol for Videoconferences Using H.224”, ITU-T, Telecommunication Standardization Sector of ITU, H.281, Nov. 1994, 12 pages. |
| Paulos, Eric John, “Personal Tele-Embodiment”, OskiCat Catalog Record, UCB Library Catalog, 2001, 3 pages. |
| Paulos et al., “Personal Tele-Embodiment”, Chapter 9 in Goldberg, et al., ed. “Beyond webcams”, MIT Press, Jan. 4, 2002, pp. 155-167. |
| Paulos, Eric John, “Personal Tele-Embodiment”, Introductory and cover pages from 2001 Dissertation including Contents table, together with e-mails relating thereto from UC Berkeley Libraries, as shelved at UC Berkeley Engineering Library (Northern Regional library Facility), May 8, 2002, 25 pages (including 4 pages of e-mails). |
| Paulos et al., “PRoP: Personal Roving Presence”, ACM:CHI Proceedings of CHI '98, 1998, 8 pages. |
| Number | Date | Country | |
|---|---|---|---|
| 20200356101 A1 | Nov 2020 | US |
| Number | Date | Country | |
|---|---|---|---|
| 61437433 | Jan 2011 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 15721067 | Sep 2017 | US |
| Child | 16817497 | US | |
| Parent | 15139073 | Apr 2016 | US |
| Child | 15721067 | US | |
| Parent | 13958413 | Aug 2013 | US |
| Child | 15139073 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 13360579 | Jan 2012 | US |
| Child | 13958413 | US |
