This disclosure relates to relates to a navigational control system for a robotic device.
Robotic engineers have long worked on developing an effective method of autonomous cleaning. This has led to the development of two separate and distinct schemes for autonomous robotic devices: (1) deterministic cleaning; and (2) random cleaning.
In deterministic cleaning, where the cleaning rate equals the coverage rate and is, therefore, a more efficient cleaning method than random-motion cleaning, the autonomous robotic device follows a defined path, e.g., a boustrophedon path that is calculated to facilitate complete cleaning coverage of a given area while eliminating redundant cleaning. Deterministic cleaning requires that the robotic device maintain precise position knowledge at all times, as well as its position history (where it has been), which, in turn, requires a sophisticated positioning system. A suitable positioning system—a positioning system suitably accurate for deterministic cleaning might rely on scanning laser ranging systems, ultrasonic transducers, a carrier phase differential GPS, or other sophisticated methods—is typically prohibitively expensive and labor intensive, requiring an involved pre-setup to accommodate the unique conditions of each area to be cleaned, e.g., room geometry, furniture locations. In addition, methods that rely on global positioning are typically incapacitated by failure of any part of the positioning system.
One illustrative example of a highly sophisticated (and relatively expensive) robotic device for deterministic cleaning is the RoboScrub device built by Denning Mobile Robotics and Windsor Industries. The RoboScrub device employs sonar and infrared detectors, bump sensors, and a high-precision laser navigation system to define the deterministic cleaning path. The navigation system employed with the RoboScrub device requires numerous large bar code targets to be set up in various strategic positions within the area to be cleaned, and effective operation of the navigation system requires that at least four of such targets be visible simultaneously. This target accessibility requirement effectively limits the use of the RoboScrub device to large uncluttered open areas.
Other representative deterministic robotic devices are described in U.S. Pat. No. 5,650,702 (Azumi), U.S. Pat. No. 5,548,511 (Bancroft), U.S. Pat. No. 5,537,017 (Feiten et al.), U.S. Pat. No. 5,353,224 (Lee et al.), U.S. Pat. No. 4,700,427 (Knepper), and U.S. Pat. No. 4,119,900 (Kreimnitz). These representative deterministic robotic devices are likewise relatively expensive, require labor intensive pre-setup, and/or are effectively limited to large, uncluttered areas of simple geometric configuration (square, rectangular rooms with minimal (or no) furniture).
Due to the limitations and difficulties inherent in purely deterministic cleaning systems, some robotic devices rely on pseudo-deterministic cleaning schemes such as dead reckoning. Dead reckoning consists of continually measuring the precise rotation of each drive wheel (e.g., using optical shaft encoders) to continually calculate the current position of the robotic device, based upon a known starting point and orientation. In addition to the disadvantages of having to start cleaning operations from a fixed position with the robotic device in a specified orientation, the drive wheels of dead reckoning robotic devices are almost always subject to some degree of slippage, which leads to errors in the calculation of current position. Accordingly, dead reckoning robotic devices are generally considered unreliable for cleaning operations of any great duration—resulting in intractable system neglect, i.e., areas of the surface to be cleaned are not cleaned. Other representative examples of pseudo-deterministic robotic devices are described in U.S. Pat. No. 6,255,793 (Peless et al.) and U.S. Pat. No. 5,109,566 (Kobayashi et al.).
A robotic device operating in random motion, under the control of one or more random-motion algorithms stored in the robotic device, represents the other basic approach to cleaning operations using autonomous robotic devices. The robotic device autonomously implement such random-motion algorithm(s) in response to internal events, e.g., signals generated by a sensor system, elapse of a time period (random or predetermined). In a typical room without obstacles, a robotic device operating under the control of a random-motion algorithm will provide acceptable cleaning coverage given enough cleaning time. Compared to a robotic device operating in a deterministic cleaning mode, a robotic device utilizing a random-motion algorithm must operate for a longer period of time to achieve acceptable cleaning coverage. To have a high confidence that a random-motion robotic device has cleaned 98% of an obstacle-free room, the random-motion robotic device must run approximately five times longer than a deterministic robotic device having similarly sized cleaning mechanisms and moving at approximately the same speed.
However, an area to be cleaned that includes one or more randomly-situated obstacles causes a marked increase in the running time for a random-motion robotic device to effect 98% cleaning coverage. Therefore, while a random motion robotic device is a relatively inexpensive means of cleaning a defined working area as contrasted to a deterministic robotic device, the random-motion robotic device requires a significantly higher cleaning time.
A need exists to provide a deterministic component to a random-motion robotic device to enhance the cleaning efficiency thereof to reduce the running time for the random-motion robotic cleaning to achieve a 98% cleaning coverage.
The present disclosure provides a debris sensor, and apparatus utilizing such a debris sensor, wherein the sensor is instantaneously responsive to debris strikes, and can be used to control, select or vary the operational mode of an autonomous or non-autonomous cleaning apparatus containing such a sensor.
In one aspect of the disclosure, an autonomous cleaning apparatus includes a chassis, a drive system disposed on the chassis and operable to enable movement of the cleaning apparatus, and a controller in communication with the drive system. The controller includes a processor operable to control the drive system to steer movement of the cleaning apparatus. The autonomous cleaning apparatus includes a cleaning head system disposed on the chassis and a sensor system in communication with the controller. The sensor system includes a debris sensor for generating a debris signal, a bump sensor for generating a bump signal, and an obstacle following sensor disposed on a side of the autonomous cleaning apparatus for generating an obstacle signal. The processor executes a prioritized arbitration scheme to identify and implement one or more dominant behavioral modes based upon at least one signal received from the sensor system.
Implementations of the disclosure may include one or more of the following features. In some implementations, the processor implements a spot cleaning mode in an area in which the cleaning apparatus was operating, substantially immediately in response to receiving a debris signal generated by the debris sensor. The spot cleaning mode may comprise maneuvering the autonomous cleaning apparatus according to a self-bounded area algorithm. The self-bounded area algorithm may include a spiraling algorithm at a reduced drive speed. In some implementations, the processor implements a high power cleaning mode in response to the debris signal. The high power mode includes elevating power delivery to the cleaning head system.
In some implementations, the debris sensor includes a piezoelectric sensor located proximate to a cleaning pathway and responsive to a debris impact thereon to generate a debris signal indicative of such impact. The debris sensor may include a plate, an elastomer pad supporting the plate, and a piezoelectric material and an electrode both secured to the plate. The electrode is in communication with the controller. In some examples, the debris sensor includes a piezoelectric film.
In some implementations, the sensor system includes right and left debris sensors in communication with the controller and disposed proximate a cleaning pathway of the cleaning head system for generating respective debris signals. The processor directs the drive system to turn right in response to the debris signal generated by the right debris sensor and to turn left in response to the debris signal generated by the left debris sensor. The right and left debris sensors may be disposed opposite each other and equidistantly from a center axis defined by the cleaning pathway.
The bump sensor may include a displaceable bumper attached to the chassis and at least one break-beam sensor disposed on the displaceable bumper. The break-beam sensor is activated upon displacement of the bumper toward the chassis.
The obstacle following sensor may include an emitter emitting an emission signal laterally and a detector configured to detect the emission reflected off an obstacle adjacent the cleaning apparatus. The emitter and the detector are configured to establish a focal point. The obstacle following sensor may be disposed on a dominant side of the autonomous cleaning apparatus.
In some implementations, the sensor system includes a cliff sensor for generating a cliff signal upon detection of a cliff. The cliff sensor includes an emitter emitting an emission signal downwardly and a detector configured to detect the emission reflected off a surface being traversed by the cleaning apparatus. The emitter and the detector are configured to establish a focal point below the cleaning apparatus. In some examples, the sensor system includes a wheel drop sensor and/or a stall sensor.
In another aspect of the disclosure, an autonomous cleaning apparatus includes a chassis a drive system disposed on the chassis and operable to enable movement of the cleaning apparatus, and a controller in communication with the drive system. The controller includes a processor operable to control the drive system to steer movement of the cleaning apparatus. The autonomous cleaning apparatus includes a cleaning head system disposed on the chassis and a sensor system in communication with the controller. The sensor system includes a debris sensor for generating a debris signal, a bump sensor for generating a bump signal, and an obstacle following sensor disposed on a side of the autonomous cleaning apparatus for generating an obstacle signal. The processor executes a prioritized arbitration scheme to identify and implement one or more dominant behavioral modes based upon at least one signal received from the sensor system. The processor controls one or more operational conditions of the autonomous cleaning apparatus based upon the debris signal. The processor controls the drive system to execute a pattern of movements to steer the autonomous cleaning apparatus toward a debris area corresponding to the debris signal generated by the debris sensor.
In yet another aspect of the disclosure, an autonomous cleaning apparatus includes a drive system operable to enable movement of the cleaning apparatus, a controller in communication with the drive system, and a debris sensor for generating a debris signal indicating that the cleaning apparatus has encountered debris. The controller includes a processor operable to control the drive system to provide at least one pattern of movement of the cleaning apparatus. The debris sensor is located along a cleaning passageway of the cleaning apparatus and responsive to debris passing through the cleaning passageway to generate a signal indicative of such passing. The processor is responsive to the debris signal to select a pattern of movement of the cleaning apparatus. The pattern of movement includes steering the cleaning apparatus toward an area containing debris. In some implementations, the pattern of movement includes spot coverage of an area containing debris.
One aspect of the disclosure is an autonomous cleaning apparatus including a drive system operable to enable movement of the cleaning apparatus, a controller in communication with the drive system, the controller including a processor operable to control the drive system to provide at least one pattern of movement of the cleaning apparatus; and a debris sensor for generating a debris signal indicating that the cleaning apparatus has encountered debris; wherein the processor is responsive to the debris signal to select an operative mode from among predetermined operative modes of the cleaning apparatus.
The selection of operative mode could include selecting a pattern of movement of the cleaning apparatus. The pattern of movement can include spot coverage of an area containing debris, or steering the cleaning apparatus toward an area containing debris. The debris sensor could include spaced-apart first and second debris sensing elements respectively operable to generate first and second debris signals; and the processor can be responsive to the respective first and second debris signals to select a pattern of movement, such as steering toward a side (e.g., left or right side) with more debris.
The debris sensor can include a piezoelectric sensor element located proximate to a cleaning pathway of the cleaning apparatus and responsive to a debris strike to generate a signal indicative of such strike.
The debris sensor can also be incorporated into a non-autonomous cleaning apparatus. This aspect of the invention can include a piezoelectric sensor located proximate to a cleaning pathway and responsive to a debris strike to generate a debris signal indicative of such strike; and a processor responsive to the debris signal to change an operative mode of the cleaning apparatus. The change in operative mode could include illuminating a user-perceptible indicator light, changing a power setting (e.g., higher power setting when more debris is encountered), or slowing or reducing a movement speed of the apparatus.
A further aspect of the disclosure is a debris sensor, including a piezoelectric element located proximate to or within a cleaning pathway of the cleaning apparatus and responsive to a debris strike to generate a first signal indicative of such strike, and a processor operable to process the first signal to generate a second signal representative of a characteristic of debris being encountered by the cleaning apparatus. That characteristic could be, for example, a quantity or volumetric parameter of the debris, or a vector from a present location of the cleaning apparatus to an area containing debris.
Another aspect of the disclosure takes advantage of the motion of an autonomous cleaning device across a floor or other surface, processing the debris signal in conjunction with knowledge of the cleaning device's movement to calculate a debris gradient. The debris gradient is representative of changes in debris strikes count as the autonomous cleaning apparatus moves along a surface. By examining the sign of the gradient (positive or negative, associated with increasing or decreasing debris), an autonomous cleaning device controller can continuously adjust the path or pattern of movement of the device to clean a debris field most effectively.
Another aspect of the disclosure includes a navigational control system that enhances the cleaning efficiency of a robotic device by adding a deterministic component (in the form of a conduct prescribed by a navigation control algorithm) to the random motion of the robotic device generated by predetermined behavioral modes stored in the robotic device.
Yet another aspect of the disclosure includes a navigational control unit operating under a navigation control algorithm that includes a predetermined triggering event that defines when the prescribed conduct will be implemented by the robotic device.
These and other aspects of the disclosure are achieved by means of a navigational control system for deterministically altering movement activity of a robotic device operating in a defined working area, comprising a transmitting subsystem integrated in combination with the robotic device, the transmitting subsystem comprising means for emitting a number of directed beams, each directed beam having a predetermined emission pattern, and a receiving subsystem functioning as a base station that includes a navigation control algorithm that defines a predetermined triggering event for the navigational control system and a set of detection units positioned within the defined working area, the detection units being positioned in a known aspectual relationship with respect to one another, the set of detection units being configured and operative to detect one or more of the directed beams emitted by the transmitting system; and wherein the receiving subsystem is configured and operative to process the one or more detected directed beams under the control of the navigational control algorithm to determine whether the predetermined triggering event has occurred, and, if the predetermined triggering event has occurred transmit a control signal to the robotic device, wherein reception of the control signal by the robotic device causes the robotic device to implement a prescribed conduct that deterministically alters the movement activity of the robotic device.
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.
While the debris sensor of the present disclosure can be incorporated into a wide range of autonomous cleaning devices (and indeed, into non-autonomous cleaning devices as shown by way of example in
The hardware and behavioral modes (coverage behaviors for cleaning operations; escape behaviors for transitory movement patterns; and safety behaviors for emergency conditions) of the robotic device 100, which is manufactured, distributed, and/or marketed by the iRobot Corporation of Burlington, Mass. under the ROOMBA trademark, are briefly described in the following paragraphs to facilitate a more complete understanding of the navigational control system 10 of the present invention. Further details regarding the hardware and behavioral modes of the robotic device 100 can be found in commonly-owned, co-pending U.S. nonprovisional patent application Ser. No. 10/167,851, filed 12 Jun. 2002, entitled METHOD AND SYSTEM FOR MULTI-MODE COVERAGE FOR AN AUTONOMOUS ROBOT, and U.S. nonprovisional patent application Ser. No. 10/320,729, filed 16 Dec. 2002, entitled AUTONOMOUS FLOOR-CLEANING DEVICE.
In the following description of the robotic device 100, use of the terminology “forward”/“fore” refers to the primary direction of motion (forward) of the robotic device (see arrow identified by reference character “FM” in
Robotic Device
The robotic device 100 has a generally cylindrical housing infrastructure that includes a chassis 102 and an outer shell 104 secured to the chassis 102 that define a structural envelope of minimal height (to facilitate movement under furniture). The hardware comprising the robotic device 100 can be generally categorized as the functional elements of a power system, a motive power system, a sensor system, a control module, a side brush assembly, or a self-adjusting cleaning head system, respectively, all of which are integrated in combination with the housing infrastructure. In addition to such categorized hardware, the robotic device 100 further includes a forward bumper 106 having a generally arcuate configuration and a nose-wheel assembly 108.
The forward bumper 106 (illustrated as a single component; alternatively, a two-segment component) is integrated in movable combination with the chassis 102 (by means of displaceable support members pairs) to extend outwardly therefrom. Whenever the robotic device 100 impacts an obstacle (e.g., wall, furniture) during movement thereof, the bumper 106 is displaced (compressed) towards the chassis 102 and returns to its extended (operating) position when contact with the obstacle is terminated.
The nose-wheel assembly 108 is mounted in biased combination with the chassis 102 so that the nose-wheel subassembly 108 is in a retracted position (due to the weight of the robotic device 100) during cleaning operations wherein it rotates freely over the surface being cleaned. When the nose-wheel subassembly 108 encounters a drop-off during operation (e.g., descending stairs, split-level floors), the nose-wheel assembly 108 is biased to an extended position.
The hardware of the power system, which provides the energy to power the electrically-operated hardware of the robotic device 100, comprises a rechargeable battery pack 110 (and associated conduction lines, not shown) that is integrated in combination with the chassis 102.
The motive power system provides the means that propels the robotic device 100 and operates the cleaning mechanisms, e.g., side brush assembly and the self-adjusting cleaning head system, during movement of the robotic device 100. The motive power system comprises left and right main drive wheel assemblies 112L, 112R, their associated independent electric motors 114L, 114R, and electric motors 116, 118 for operation of the side brush assembly and the self-adjusting cleaning head subsystem, respectively.
The main drive wheel assemblies 112L, 112R are independently mounted in biased combination with the chassis 102 (for pivotal motion with respect thereto) at opposed ends of the transverse diameter (with respect to the fore-aft axis FAX) of the robotic device 100 and are in a retracted position (due to the weight of the robotic device 100) during operation thereof wherein the axes of rotation are approximately coplanar with the bottom of the chassis 102. If the robotic device 100 is removed from the surface being cleaned, the main wheel assemblies 112L, 112R are pivotally-biased to an extended position wherein their axes of rotation are below the bottom plane of the chassis 102 (in this extended position the rechargeable battery pack 110 is automatically turned off by the control module executing one of the safety behavioral modes).
The electric motors 114L, 114R are mechanically coupled to the main drive wheel assemblies 112L, 112R, respectively, and independently operated by control signals generated by the control module as a response to the implementation of a behavioral mode. Independent operation of the electric motors 114L, 114R allows the main wheel assemblies 112L, 112R to be: (1) rotated at the same speed in the same direction to propel the robotic device 100 in a straight line, forward or aft; (2) differentially rotated (including the condition wherein one wheel assembly is not rotated) to effect a variety of right and/or left turning patterns (over a spectrum of sharp to shallow turns) for the robotic device 100; and (3) rotated at the same speed in opposite directions to cause the robotic device 100 to turn in place, i.e., “spin on a dime”, to provide an extensive repertoire of movement capability for the robotic device 100.
The sensor system comprises a variety of different sensor units that are operative to generate signals that control the behavioral mode operations of the robotic device 100. The described robotic device 100 includes obstacle detection units 120, cliff detection units 122, wheel drop sensors 124, an obstacle-following unit 126, a virtual wall omnidirectional detector 128, stall-sensor units 130, and main wheel encoder units 132, and left and right debris sensors 125L,125R.
For the described embodiment, the obstacle (“bump”) detection units 120 are IR break beam sensors mounted in combination with the displaceable support member pairs of the forward bumper 106. These detection units 120 are operative to generate one or more signals indicating relative displacement between one or more support member pairs whenever the robotic device 100 impacts an obstacle such that the forward bumper 106 is compressed. These signals are processed by the control module to determine an approximate point of contact with the obstacle relative to the fore-aft axis FAX of the robotic device 100 (and the behavioral mode(s) to be implemented).
The cliff detection units 122 are mounted in combination with the forward bumper 106. Each cliff detection unit 122 comprises an IR emitter—detector pair configured and operative to establish a focal point such that radiation emitted downwardly by the emitter is reflected from the surface being traversed and detected by the detector. If reflected radiation is not detected by the detector, i.e., a drop-off is encountered, the cliff detection unit 122 transmits a signal to the control module (which causes one or more behavioral modes to be implemented).
A wheel drop sensor 124 such as a contact switch is integrated in combination with each of the main drive wheel assemblies 112L, 112R and the nose wheel assembly 108 and is operative to generate a signal whenever any of the wheel assemblies is in an extended position, i.e., not in contact with the surface being traversed, (which causes the control module to implement one or more behavioral modes).
The obstacle-following unit 126 for the described embodiment is an IR emitter-detector pair mounted on the ‘dominant’ side (right hand side of
A virtual wall detection system for use in conjunction with the described embodiment of the robotic device 100 comprises an omnidirectional detector 128 mounted atop the outer shell 104 and a stand-alone transmitting unit (not shown) that transmits an axially-directed confinement beam. The stand-alone transmitting unit is positioned so that the emitted confinement beam blocks an accessway to a defined working area, thereby restricting the robotic device 100 to operations within the defined working area (e.g., in a doorway to confine the robotic device 100 within a specific room to be cleaned). Upon detection of the confinement beam, the omnidirectional detector 128 transmits a signal to the control module (which causes one or more behavioral modes to be implemented to move the robotic device 100 away from the confinement beam generated by the stand-alone transmitting unit).
A stall sensor unit 130 is integrated in combination with each electric motor 114L, 114R, 116, 118 and operative to transmit a signal to the control module when a change in current is detected in the associated electric motor (which is indicative of a dysfunctional condition in the corresponding driven hardware). The control module is operative in response to such a signal to implement one or more behavioral modes.
An IR encoder unit 132 (see
The control module comprises the microprocessing unit 135 illustrated in
The microcontroller is operative to execute instruction sets for processing sensor signals, implementing specific behavioral modes based upon such processed signals, and generating control (instruction) signals for the controllable hardware based upon implemented behavioral modes for the robotic device 100. The cleaning coverage and control programs for the robotic device 100 are stored in the ROM of the microprocessing unit 135, which includes the behavioral modes, sensor processing algorithms, control signal generation algorithms and a prioritization algorithm for determining which behavioral mode or modes are to be given control of the robotic device 100. The RAM of the microprocessing unit 135 is used to store the active state of the robotic device 100, including the ID of the behavioral mode(s) under which the robotic device 100 is currently being operated and the hardware commands associated therewith.
Referring again to
The self-adjusting cleaning head system 145 for the described robotic device 100 comprises a dual-stage brush assembly and a vacuum assembly, each of which is independently powered by an electric motor (reference numeral 118 in
The dual-stage brush assembly and the inlet of the vacuum assembly are integrated in combination with a deck structure, which is pivotally mounted in combination with the chassis 102 and operatively integrated with the motor of the dual-stage brush assembly. In response to a predetermined reduction in rotational speed of the brush assembly motor, the brush assembly motor provides the motive force to pivot the deck structure with respect to the chassis 102. The pivoting deck structure provides the self adjusting capability for the cleaning head assembly 145, which allows the robotic device 100 to readily transition between disparate surfaces during cleaning operations, e.g., carpeted surface to bare surface or vice versa, without hanging up.
The dual-stage brush assembly comprises asymmetric, counter-rotating brushes that are positioned (forward of the inlet of the vacuum assembly), configured and operative to direct particulate debris into a removable dust cartridge (not shown). The positioning, configuration, and operation of the brush assembly concomitantly directs particulate debris towards the inlet of the vacuum assembly such that particulates that are not swept up by the dual-stage brush assembly can be subsequently ingested by the vacuum assembly as a result of movement of the robotic device 100.
Operation of the vacuum assembly independently of the self-adjustable brush assembly allows the vacuum assembly to generate and maintain a higher vacuum force using a battery-power source than would be possible if the vacuum assembly were operated in dependence with the brush assembly.
Referring now to
More particularly, in the arrangement shown in
As shown in
The operation of the piezoelectric debris sensors, as well as signal processing and selection of behavioral modes based on the debris signals they generate, will be discussed below following a brief discussion of general aspects of behavioral modes for the cleaning device.
Behavioral Modes
The robotic device 100 uses a variety of behavioral modes to effectively clean a defined working area where behavioral modes are layers of control systems that can be operated in parallel. The microprocessor unit 135 is operative to execute a prioritized arbitration scheme to identify and implement one or more dominant behavioral modes for any given scenario based upon inputs from the sensor system.
The behavioral modes for the described robotic device 100 can be characterized as: (1) coverage behavioral modes; (2) escape behavioral modes; and (3) safety behavioral modes. Coverage behavioral modes are primarily designed to allow the robotic device 100 to perform its cleaning operations in an efficient and effective manner and the escape and safety behavioral modes are priority behavioral modes implemented when a signal from the sensor system indicates that normal operation of the robotic device 100 is impaired, e.g., obstacle encountered, or is likely to be impaired, e.g., drop-off detected.
Representative and illustrative coverage behavioral (cleaning) modes for the robotic device 100 include: (1) a Spot Coverage pattern; (2) an Obstacle-Following (or Edge-Cleaning) Coverage pattern, and (3) a Room Coverage pattern. The Spot Coverage pattern causes the robotic device 100 to clean a limited area within the defined working area, e.g., a high-traffic area. In a preferred embodiment the Spot Coverage pattern is implemented by means of a spiral algorithm (but other types of self-bounded area algorithms, e.g., polygonal, can be used). The spiral algorithm, which causes outward spiraling (preferred) or inward spiraling movement of the robotic device 100, is implemented by control signals from the microprocessing unit 135 to the main wheel assemblies 112L, 112R to change the turn radius/radii thereof as a function of time (thereby increasing/decreasing the spiral movement pattern of the robotic device 100).
The robotic device 100 is operated in the Spot Coverage pattern for a predetermined or random period of time, for a predetermined or random distance (e.g., a maximum spiral distance) and/or until the occurrence of a specified event, e.g., activation of one or more of the obstacle detection units 120 (collectively a transition condition). Once a transition condition occurs, the robotic device 100 can implement or transition to a different behavioral mode, e.g., a Straight Line behavioral mode (in a preferred embodiment of the robotic device 100, the Straight Line behavioral mode is a low priority, default behavior that propels the robot in an approximately straight line at a preset velocity of approximately 0.306 m/s) or a Bounce behavioral mode in combination with a Straight Line behavioral mode.
If the transition condition is the result of the robotic device 100 encountering an obstacle, the robotic device 100 can take other actions in lieu of transitioning to a different behavioral mode. The robotic device 100 can momentarily implement a behavioral mode to avoid or escape the obstacle and resume operation under control of the spiral algorithm (i.e., continue spiraling in the same direction). Alternatively, the robotic device 100 can momentarily implement a behavioral mode to avoid or escape the obstacle and resume operation under control of the spiral algorithm (but in the opposite direction—reflective spiraling).
The Obstacle-Following Coverage pattern causes the robotic device 100 to clean the perimeter of the defined working area, e.g., a room bounded by walls, and/or the perimeter of an obstacle (e.g., furniture) within the defined working area. Preferably the robotic device 100 utilizes obstacle-following unit 126 to continuously maintain its position with respect to an obstacle, e.g., wall, furniture, so that the motion of the robotic device 100 causes it to travel adjacent to and concomitantly clean along the perimeter of the obstacle. Different embodiments of the obstacle-following unit 126 can be used to implement the Obstacle-Following behavioral pattern.
In a first embodiment, the obstacle-following unit 126 is operated to detect the presence or absence of the obstacle. In an alternative embodiment, the obstacle-following unit 126 is operated to detect an obstacle and then maintain a predetermined distance between the obstacle and the robotic device 100. In the first embodiment, the microprocessing unit 135 is operative, in response to signals from the obstacle-following unit, to implement small CW or CCW turns to maintain its position with respect to the obstacle. The robotic device 100 implements a small CW when the robotic device 100 transitions from obstacle detection to non-detection (reflection to non-reflection) or to implement a small CCW turn when the robotic device 100 transitions from non-detection to detection (non-reflection to reflection). Similar turning behaviors are implemented by the robotic device 100 to maintain the predetermined distance from the obstacle.
The robotic device 100 is operated in the Obstacle-Following behavioral mode for a predetermined or random period of time, for a predetermined or random distance (e.g., a maximum or minimum distance) and/or until the occurrence of a specified event, e.g., activation of one or more of the obstacle detection units 120 a predetermined number of times (collectively a transition condition). In certain embodiments, the microprocessor 135 will cause the robotic device to implement an Align behavioral mode upon activation of the obstacle-detection units 120 in the Obstacle-Following behavioral mode wherein the implements a minimum angle CCW turn to align the robotic device 100 with the obstacle.
The Room Coverage pattern can be used by the robotic device 100 to clean any defined working area that is bounded by walls, stairs, obstacles or other barriers (e.g., a virtual wall unit). A preferred embodiment for the Room Coverage pattern comprises the Random-Bounce behavioral mode in combination with the Straight Line behavioral mode. Initially, the robotic device 100 travels under control of the Straight-Line behavioral mode, i.e., straight-line algorithm (main drive wheel assemblies 112L, 112R operating at the same rotational speed in the same direction) until an obstacle is encountered. Upon activation of one or more of the obstacle detection units 120, the microprocessing unit 135 is operative to compute an acceptable range of new directions based upon the obstacle detection unit(s) 126 activated. The microprocessing unit 135 selects a new heading from within the acceptable range and implements a CW or CCW turn to achieve the new heading with minimal movement. In some embodiments, the new turn heading may be followed by forward movement to increase the cleaning efficiency of the robotic device 100. The new heading may be randomly selected across the acceptable range of headings, or based upon some statistical selection scheme, e.g., Gaussian distribution. In other embodiments of the Room Coverage behavioral mode, the microprocessing unit 135 can be programmed to change headings randomly or at predetermined times, without input from the sensor system.
The robotic device 100 is operated in the Room Coverage behavioral mode for a predetermined or random period of time, for a predetermined or random distance (e.g., a maximum or minimum distance) and/or until the occurrence of a specified event, e.g., activation of the obstacle-detection units 120 a predetermined number of times (collectively a transition condition).
A preferred embodiment of the robotic device 100 includes four escape behavioral modes: a Turn behavioral mode, an Edge behavioral mode, a Wheel Drop behavioral mode, and a Slow behavioral mode. One skilled in the art will appreciate that other behavioral modes can be utilized by the robotic device 100. One or more of these behavioral modes may be implemented, for example, in response to a current rise in one of the electric motors 116, 118 of the side brush assembly 140 or dual-stage brush assembly above a low or high stall threshold, forward bumper 106 in compressed position for determined time period, detection of a wheel-drop event.
In the Turn behavioral mode, the robotic device 100 turns in place in a random direction, starting at higher velocity (e.g., twice normal turning velocity) and decreasing to a lower velocity (one-half normal turning velocity), i.e., small panic turns and large panic turns, respectively. Low panic turns are preferably in the range of 45° to 90°, large panic turns are preferably in the range of 90° to 270°. The Turn behavioral mode prevents the robotic device 100 from becoming stuck on room impediments, e.g., high spot in carpet, ramped lamp base, from becoming stuck under room impediments, e.g., under a sofa, or from becoming trapped in a confined area.
In the Edge behavioral mode follows the edge of an obstacle unit it has turned through a predetermined number of degrees, e.g., 60°, without activation of any of the obstacle detection units 120, or until the robotic device has turned through a predetermined number of degrees, e.g., 170°, since initiation of the Edge behavioral mode. The Edge behavioral mode allows the robotic device 100 to move through the smallest possible openings to escape from confined areas.
In the Wheel Drop behavioral mode, the microprocessor 135 reverses the direction of the main wheel drive assemblies 112L, 112R momentarily, then stops them. If the activated wheel drop sensor 124 deactivates within a predetermined time, the microprocessor 135 then reimplements the behavioral mode that was being executed prior to the activation of the wheel drop sensor 124.
In response to certain events, e.g., activation of a wheel drop sensor 124 or a cliff detector 122, the Slow behavioral mode is implemented to slowed down the robotic device 100 for a predetermined distance and then ramped back up to its normal operating speed.
When a safety condition is detected by the sensor subsystem, e.g., a series of brush or wheel stalls that cause the corresponding electric motors to be temporarily cycled off, wheel drop sensor 124 or a cliff detection sensor 122 activated for greater that a predetermined period of time, the robotic device 100 is generally cycled to an off state. In addition, an audible alarm may be generated.
The foregoing description of behavioral modes for the robotic device 100 are intended to be representative of the types of operating modes that can be implemented by the robotic device 100. One skilled in the art will appreciate that the behavioral modes described above can be implemented in other combinations and/or circumstances.
Debris Sensor
As shown in
When employed in an autonomous, robot cleaning device, the debris signal from the debris sensor can be used to select a behavioral mode (such as entering into a spot cleaning mode), change an operational condition (such as speed, power or other), steer in the direction of debris (particularly when spaced-apart left and right debris sensors are used to create a differential signal), or take other actions.
A debris sensor according to the present invention can also be incorporated into a non-autonomous cleaning device. When employed in a non-autonomous cleaning device such as, for example, an otherwise relatively conventional vacuum cleaner 700 like that shown in
The algorithmic aspects of the operation of the debris sensor subsystem are summarized in
A further practice of the invention takes advantage of the motion of an autonomous cleaning device across a floor or other surface, processing the debris signal in conjunction with knowledge of the cleaning device's movement to calculate a debris gradient (812 in
Piezoelectric Sensor: As noted above, a piezoelectric transducer element can be used in the debris sensor subsystem of the invention. Piezoelectric sensors provide instantaneous response to debris strikes and are relatively immune to accretion that would degrade the performance of an optical debris sensor typical of the prior art.
An example of a piezoelectric transducer 125PS is shown in
In the example shown in
The exemplary mounting configuration shown in
In operation, debris thrown up by the cleaning brush assembly (e.g., brush 94 of
As is well known, a piezoelectric sensor converts mechanical energy (e.g., the kinetic energy of a debris strike and vibration of the brass disk) into electrical energy—in this case, generating an electrical pulse each time it is struck by debris—and it is this electrical pulse that can be processed and transmitted to a system controller (e.g., controller 135 of
Filtering: However; since the sensor element 125PS is in direct or indirect contact with the cleaning device chassis or body through its mounting system (see
Accordingly, as described below, an electronic filter is used to greatly attenuate the lower frequency signal components to improve signal-to-noise performance. Examples of the architecture and circuitry of such filtering and signal processing elements will next be described in connection with
Signal Processing
Moreover, as noted above, two debris sensor circuit modules (i.e., left and right channels like 125L and 125R of
Thus,
As shown in
The Acoustic Vibration Filter/RFI Filter block 502 can be designed to provide significant attenuation (in one embodiment, better than −45 dB Volts), and to block most of the lower frequency, slow rate of change mechanical vibration signals, while permitting higher frequency, fast rate of change debris-strike signals to pass. However, even though these higher frequency signals get through the filter, they are attenuated, and thus require amplification by the Signal Amplifier block 504.
In addition to amplifying the desired higher frequency debris strike signals, the very small residual mechanical noise signals that do pass through the filter also get amplified, along with electrical noise generated by the amplifier itself, and any radio frequency interference (RFI) components generated by the motors and radiated through the air, or picked up by the sensor and its conducting wires. The signal amplifier's high frequency response is designed to minimize the amplification of very high frequency RFI. This constant background noise signal, which has much lower frequency components than the desired debris strike signals, is fed into the Reference Level Generator block 506. The purpose of module 506 is to create a reference signal that follows the instantaneous peak value, or envelope, of the noise signal. It can be seen in
Referring again to
The Comparator 510 compares the instantaneous voltage amplitude value of the signal from the Attenuator 508 to the signal from the Reference Level Generator 506. Normally, when the cleaning device operating is running and debris are not striking the sensor element, the instantaneous voltage coming out of the Reference Level Generator 506 will be higher than the voltage coming out of the Attenuator block 508. This causes the Comparator block 510 to output a high logic level signal (logic one), which is then inverted by the Pulse Stretcher block 512 to create a low logic level (logic zero).
However, when debris strikes the sensor, the voltage from the Attenuator 508 exceeds the voltage from the Reference Level Generator 506 (since this circuit cannot track the high frequency, fast rate of change signal component from the Amplifier 504) and the signal produced by a debris strike is higher in voltage amplitude than the constant background mechanical noise signal which is more severely attenuated by the Acoustic Vibration Filter 502. This causes the comparator to momentarily change state to a logic level zero. The Pulse Stretcher block 512 extends this very brief (typically under 10-microsecond) event to a constant 1 millisecond (+0.3 mS, −0 mS) event, so as to provide the system controller (e.g., controller 135 of
When the system controller “sees” this 1-millisecond logic zero pulse, it interprets the event as a debris strike.
Referring now to the RFI Filter portion of the Acoustic Vibration Filter/RFI Filter block 502, this filter serves to attenuate the very high frequency radiated electrical noise (RFI), which is generated by the motors and motor driver circuits.
In summary, the illustrated circuitry connected to the sensor element uses both amplitude and frequency information to discriminate a debris strike (representative of the cleaning device picking up debris) from the normal background mechanical noise also picked up by the sensor element, and the radiated radio frequency electrical noise produced by the motors and motor driver circuits. The normal, though undesirable, constant background noise is used to establish a dynamic reference that prevents false debris-strike indications while maintaining a good signal-to-noise ratio.
In practice, the mechanical mounting system for the sensor element (see
Signal Processing Circuitry:
The ground referenced, composite signal from the piezoelectric sensor disk (see piezoelectric disk 402 of
U1A, U1B and their associated components form a two stage, ac-coupled, non-inverting amplifier with a theoretical AC gain of 441. C9 and C10 serve to minimize gain at low frequencies while C7 and C8 work to roll the gain off at RFI frequencies. The net theoretical frequency response from the filter input to the amplifier output is a single pole high pass response with ˜3 dB at 32.5 kHz, ˜100 dB/Decade, and a 2-pole low pass response with break frequencies at 100 kHz, ˜32 dB/Decade, and 5.4 MHz, ˜100 dB/Decade, together forming a band-pass filter.
The output from the amplifier is split, with one output going into R14, and the other to the non-inverting input of U1C. The signal going into R14 is attenuated by the R14-R15 voltage divider, and then fed into the inverting input of comparator U2A. The other signal branch from the output of U1B is fed into the non-inverting input of amplifier U1C. U1C along with U1D and the components therebetween (as shown in
Normally, when debris is not striking the sensor, the output of the peak detector will be greater in amplitude than the output of the attenuator network. When debris strikes the sensor, a high frequency pulse is created that has a higher amplitude coming out of the front-end high pass filter going into U1A than the lower frequency mechanical noise signal component. This signal will be larger in amplitude, even after coming out of the R14-R15 attenuator network, than the signal coming out of the peak detector, because the peak detector cannot track high-speed pulses due to the component values in the R13, C11, R12 network. The comparator then changes state from high to low for as long as the amplitude of the debris-strike pulse stays above the dynamic, noise generated, reference-level signal coming out of the peak detector. Since this comparator output pulse can be too short for the system controller to see, a pulse stretcher is used.
The pulse stretcher is a one-shot monostable design with a lockout mechanism to prevent re-triggering until the end of the timeout period. The output from U2A is fed into the junction of C13 and Q1. C13 couples the signal into the monostable formed by U2C and its associated components. Q1 functions as the lockout by holding the output of U2A low until the monostable times out. The timeout period is set by the time constant formed by R22, C12 and R18, and the reference level set by the R20-R21 voltage divider. This time can adjusted for 1 mS, +0.3 mS, −0.00 mS as dictated by the requirements of the software used by the controller/processor.
Power for the debris sensor circuit is provided by U3 and associated components. U3 is a low power linear regulator that provides a 5-volt output. The unregulated voltage from the robot's onboard battery provides the power input
When required, circuit adjustments can be set by R14 and R12. These adjustments will allow the circuit response to be tuned in a short period of time
In a production device of this kind, it is expected that power into, and signal out of the debris sensor circuit printed circuit board (PCB) will be transferred to the main board via shielded cable. Alternatively, noise filters may be substituted for the use of shielded cable, reducing the cost of wiring. The cable shield drain wire can be grounded at the sensor circuit PCB side only. The shield is not to carry any ground current. A separate conductor inside the cable will carry power ground. To reduce noise, the production sensor PCB should have all components on the topside with solid ground plane on the bottom side. The sensor PCB should be housed in a mounting assembly that has a grounded metal shield that covers the topside of the board to shield the components from radiated noise pick up from the robot's motors. The piezoelectric sensor disk can be mounted under the sensor circuit PCB on a suitable mechanical mounting system, such as that shown in
The debris sensor is not subject to degradation by accretion of debris, but is capable of instantaneously sensing and responding to debris strikes, and thus immediately responsive to debris on a floor or other surface to be cleaned, with reduced sensitivity to variations in airflow, instantaneous power, or other operational conditions of the cleaning device.
When employed as described herein, the debris sensor and/or control system enables an autonomous cleaning device to control its operation or select from among operational modes, patterns of movement or behaviors responsive to detected debris, for example, by steering the device toward “dirtier” areas based on signals generated by the debris sensor.
The debris sensor can also be employed in non-autonomous cleaning devices to control, select or vary operational modes of either an autonomous or non-autonomous cleaning apparatus.
In addition, the disclosed signal processing architecture and circuitry is particularly useful in conjunction with a piezoelectric debris sensor to provide high signal to noise ratios.
A wide range of modifications and variations of the present invention are possible and within the scope of the disclosure. The debris sensor can also be employed for purposes, and in devices, other than those described herein.
Navigational Control System
Broadly described, the navigational control system 10 according to the present invention is operative, under the direction of the navigation control algorithm, to monitor the movement activity of the robotic device 100 within the defined working area. In one preferred embodiment, the monitored movement activity is defined in terms of the “position history” of the robotic device 100 as described in further detail below. In another preferred embodiment, the monitored movement activity is defined in terms of the “instantaneous position” of the robotic device 100 as defined in further detail below.
The predetermined triggering event is a specific occurrence or condition in the movement activity of the robotic device 100. Upon the realization of the predetermined triggering event, the navigational control system 10 is operative to generate and communicate a control signal to the robotic device 100. In response to the control signal, the robotic device 100 is operative to implement or execute a conduct prescribed by the control signal, i.e., the prescribed conduct. This prescribed conduct represents a deterministic component of the movement activity of the robotic device 100.
In the preferred embodiment of the navigational control system 10 based upon position history, the system 10 is configured and operative to create a “tessellation” of any defined working area where the robotic device 100 is to be operated, e.g., a room to be cleaned. Tessellate is used herein in the sense that the defined working area is segmented into a set of individual cells, which may or may not be of equal size. For example,
Preferably, the navigational control system 10 is operative to define the size the individual cells C so that the length and width dimensions of an individual cell C are no larger than one-half the width (W) of the cleaning head system 145 of the robotic device 100 (see
The navigational control system 10 is operative to generate a position history of the robotic device 100 within the defined working area in terms of such individual cells C (to minimize the memory requirements for storage of position history). The position history comprises a set of discrete, instantaneous positions (in terms of individual cells C) of the robotic device 100 over a time interval where the time interval is a variable that depends upon the “triggering condition” of the navigation control algorithm implemented by the navigational control system 10.
Each discrete instantaneous position of the robotic device 100 is determined by operating the transmitting subsystem 12 to emit a set of directional beams and operating the receiving subsystem 20 to detect one or more of such directional beams and process a signal parameter of the detected beam(s) to determine an absolute bearing parameter and a distance parameter between the transmitting subsystem 12 and the receiving subsystem 20 at a point in time. Each pair of bearing, distance parameters establishes a discrete instantaneous position for the robotic device 100. For the preferred ‘position history’ embodiment, the navigational control system 10 is operative to correlate each discrete instantaneous position to one individual cell C of the grid map. A set of bearing and position pairs, i.e., a set of instantaneous positions, over a time interval defines a set of cells C, which are identified in the receiving subsystem 20 as the position history of the robotic device 100 for the time interval.
In the preferred embodiment of the navigational control system 10 based upon the instantaneous position, the system 10 processes each discrete instantaneous position as it is established, under the control of the navigation control algorithm, to determine whether such discrete instantaneous position is the predetermined triggering event defined by the navigation control algorithm.
In an advanced embodiment of the navigational control system 10, the system 10 is additionally configured and operative to determine a travel vector (indicating the direction of motion of the robotic device 100 within an individual cell C or at the discrete instantaneous position) at each point in time. These travel vectors may be stored in memory in conjunction with the corresponding cells C as a component of the position history of the robotic device 100.
The navigational control system 10 according to the present invention is further operative, under direction of the navigational control algorithm, to generate and communicate a control signal to the robotic device 100 whenever the navigational control system 100 realizes the predetermined triggering event. In response to any such control signal, the robotic device 100 is configured and operative to initiate a prescribed conduct. The prescribed conduct comprises the deterministic component added to the random motion movement activity of the robotic device 100 by means of the navigational control system 10 according to the present invention.
In one preferred embodiment of the invention, the prescribed conduct of the robotic device 100 comprises one or more basic maneuvers such as CW and CCW turns, forward or aft (straight line) movement, slow down, speed up, and stop. The CW and/or CCW turns can be implemented using the turning techniques of the robotic device 100 described above, and the turn angles can be, for example, over a 360° spectrum at predetermined intervals, e.g., 5° or 10°. Alternatively, or in addition to, the CW and/or CCW turns can be to a specified azimuthal heading (referenced to the base station as the origin) where the navigational control system 10 is configured and operative so that the travel vector is a determinable variable. Of these basic maneuvers, forward (straight line) movement is typically the default maneuver that the robotic device 100 automatically reverts to (implements) once one or more of the other basic maneuvers has been completed.
In another preferred embodiment of the invention, the prescribed conduct of the robotic device 100 comprises one or more of the behavioral modes described herein. In yet a further preferred embodiment of the invention, the prescribed conduct of the robotic device 100 comprises a combination of the basic maneuvers and the behavioral modes described herein.
The transmitting subsystem 12 is operative to transmit a number of directed beams having a predetermined emission pattern along a specific propagation axis. Preferably, the directed beams are planar, i.e., substantially parallel to the surface of the defined working area.
In preferred embodiments of the navigational control system 10 according to the present invention, the transmitting subsystem 12 is integrated in combination with the robotic device 100. The transmitting subsystem 12 is configured and operative to functionally emulate an omnidirectional transmission source with respect to the defined working area, i.e., by emitting a plurality of directed beams that cover the defined working area. For these preferred embodiments, the robotic device 100 further includes a receiver unit 16 (see
In one preferred embodiment, the transmitting subsystem 12 comprises a conventional mechanical sweeping transmitter, e.g., a laser, that is integrated in combination with a high point of the housing infrastructure of the robotic device 100 so that none of the structural features of the robotic device 100 interfere with the operation thereof. The mechanical sweeping transmitter is configured and operative to emit the plurality of directed beams while concomitantly redirecting (mechanically sweeping) the transmitting element so that each directed beam has a different propagation axis. Other features and characteristics of the mechanical sweeping transmitter are described below in terms of individual transmitting units 14N for ease of description.
Another preferred embodiment of the transmitting subsystem 12 comprises a set of transmitting units 14N, where N is an integer defining the number of individual transmitting units comprising the set for the navigational control system 10, that are integrated in combination with the robotic device 100 about the periphery of its housing infrastructure. Each transmitting unit 14N is configured and operative to emit a directed beam having a predetermined emission pattern along a specific propagation axis. Preferably, the transmitting subsystem 12 is configured and operative so that the emitted directed beams are planar.
In a basic embodiment of the transmitting subsystem 12, the transmitting units 14N are fungible/interchangeable, each operating to emit a directed beam at a common operating frequency. Preferably, the common operating frequency for the transmitting units 14N lies in the infrared range, i.e., about 750 nm to about 1.4×104 nm, preferably about 880 nm to about 980 nm, although one skilled in the art will appreciate that other wavelengths, e.g., in the radio frequency range, microwave frequency range, can be used in the practice of the navigational control system 10 of the present invention.
Preferably, the common operating frequency directed beams emitted by the transmitting units 14N are periodically modulated, e.g., at 10 KHz for 50 msec, off for 300 msec. Modulation of the directed beams facilitates detection thereof by the receiving subsystem 20, i.e., the receiving subsystem 20 is able to readily discriminate between modulated directed beams emitted by the transmitting subsystem 12 and any other electromagnetic radiation sources that may be active in the defined working area, e.g., television remote control units, wireless computer keyboards, microwaves, ambient radiation such as sunlight. For the basic embodiment, it is also preferable that the transmitting units 14N be sequentially operated so that any transmitting unit 14N is cycled on for a predetermined period of time and then cycled off, the next (adjacent) transmitting unit 14N is then cycled on for the predetermined period of time and cycled off, and so forth. Operating the transmitting subsystem 12 in the foregoing manner, i.e., modulation of the directed beam, cycling transmitting units 14N on/off sequentially, minimizes the power requirements of the transmitting subsystem 12 and reduces spurious noise/collateral energy that could adversely impact the functioning of the navigational control system 10.
Ordinarily, a navigational control system 10 employing the basic embodiment of the transmitting subsystem 12, i.e., all transmitting units 14N are interchangeable-emitting directed beams at a common operating frequency, cannot be used to determine travel vectors for the robotic device 100 because the receiving subsystem 20 cannot differentiate between directed beams emitted by the transmitting units 14N and therefore cannot identify any particular transmitting unit 14N. However, the inventors have developed two innovative ways of transmitting and processing directed beams emitted by a transmitting subsystem 12 comprised of interchangeable transmitting units 14N so that the receiving subsystem 20 can individually identify a specific interchangeable transmitting unit 14N, and, based upon such identification, establish a travel vector for the robotic device 100.
Accordingly, in one enhanced version of the basic embodiment of the transmitting subsystem 12, interchangeable transmitting units 14N are operated in a predetermined manner that allows the receiving subsystem 20 to process detected directed beams to identify the directed beam having the highest signal strength, which, in turn, allows the receiving subsystem 20 to identify the interchangeable transmitting unit 14N that emitted such directed beam. This, in turn, allows the receiving subsystem 20 to determine the orientation and, hence the travel vector, of the robotic device 100.
Referring to
The transmitting subsystem 12 is further operative so that individual transmitting unit 14N are sequentially cycled on and off at predetermined times with respect to the timing sequence initialization t0 established by the receiving subsystem 20. For example, with respect to
That is, the first transmitting unit 141 is cycled on and transitions to a peak signal strength at time t1. The second transmitting unit 142 is cycled on as the directed beam from the first transmitting unit 141 achieves its peak signal strength at time t1. The second transmitting unit 142 transitions to a peak signal strength at time t2, at which point the first transmitting unit 141 has transitioned to a zero (0) signal strength and is cycled off. The third transmitting unit 143 is cycled on as the directed beam from the second transmitting unit 142 achieves its peak signal strength at time t2. The foregoing operating pattern is repeated for the second, third, and fourth transmitting units 142, 143, 144, as applicable, so that at time t3 the second transmitting unit 142 is cycled off, the directed beam emitted by the third transmitting unit 143 has achieved its peak signal strength, and the fourth transmitting unit 144 is cycled on; and at time t4 the third transmitting unit 143 is cycled off and the directed beam emitted by the fourth transmitting unit 144 has achieved its peak strength. The transmitting subsystem 12 is operative to repeat the above-described synchronization—sequential transmission procedure during operation of the navigational control system 12 according to the present invention.
In another enhanced version of the basic embodiment of the transmitting subsystem 12, interchangeable transmitting units 14N are operated in a different predetermined manner that allows the receiving subsystem 20 to process detected directed beams to identify the directed beam having the highest signal strength, which, in turn, allows the receiving subsystem 20 to identify the interchangeable transmitting unit 14N that emitted such directed beam. This, in turn, allows the receiving subsystem 20 to determine the orientation and, hence the travel vector, of the robotic device 100.
Referring to
The transmitting subsystem 12 is further operative so that individual transmitting unit 14N are sequentially cycled on and off at predetermined times with respect to the timing sequence initialization t0 established by the receiving subsystem 20. For example, with respect to
That is, the first transmitting unit 141 is cycled on at t11 (where the first “1” identifies the transmitting unit number and the second “1” indicates that the transmitting unit is cycled on) and cycled off at t12 (where the “2” indicates that the transmitting unit is cycled off). In a similar manner, the second transmitting unit 142 is cycled on at t21 and cycled off at t22, the third transmitting unit 143 is cycled on at t31 and cycled off at t32, and fourth transmitting units 144 is cycled on at t41 and cycled off at t42. The transmitting subsystem 12 is operative to repeat the above-described synchronization-sequential transmission procedure during operation of the navigational control system 12 according to the present invention.
In a more sophisticated embodiment of the transmitting subsystem 12, the transmitting units 14N are discrete and identifiable, each transmitting unit 14N operating at a unique operating frequency to emit a directed beam (which is preferably planar with respect to the surface of the defined working area) having a predetermined emission pattern along a specific propagation axis. These operating frequencies are also preferably modulated to facilitate detection thereof by the receiving subsystem 20 in an environment where other electromagnetic radiation sources are operating. Since each directed beam is readily and uniquely identifiable, the receiving subsystem 20 can process detected directed beams in a conventional manner to derive not only the absolute bearing and to the robotic device 100, but also the travel vector for the robotic device 10 at any particular time.
The receiving subsystem 20 of the navigational control system 10 according to the present invention comprises a processing unit 22 that includes a microprocessor 24, a signal processing unit 26, a memory module 28, and a set of detection units 30M. Additionally, the receiving subsystem 20 can also include a transmitting unit 32 for those preferred embodiments of the navigational control system 10 wherein the receiving subsystem 20 is operated or functions as the base station for the navigational control system 10.
The memory module 28 comprises RAM 28A and ROM 28B. Data relating to the current operation of the robotic device 100 within the defined working area is stored in the RAM 28A. Such current operational data can include the grid map of cells C defining the defined working area and the position history of the robotic device 100 within the defined working area for the ‘position history’ embodiment of the navigational control system 10. Stored in the ROM 28B are one or more navigation control algorithms for the navigational control system 10, a set of one or more control signals associated with each navigation control algorithm, and a signal processing algorithm for converting signals generated by the signal processing unit 26 to one or more sets of instantaneous position parameters, i.e., a bearing, distance pair (and travel vector, if applicable). For the ‘position history’ embodiment of the system 10, a set of instantaneous position parameters that define the position history of the robotic device 100, which are correlated with the grid map to identify the cells C comprising the position history.
The terminology “navigation control algorithm” as used herein encompasses a set of instructions that: (a) define how the position history or instantaneous position is used by the navigational control system 10 (e.g., counting and comparing cells visited, a true-false determination for cells visited, true-false determination whether the predetermined triggering event has occurred); (b) defines the triggering event or events associated with the use of the position history or the instantaneous position; and (c) identifies the control signal(s) to be implemented when the triggering event is realized. For example, in one representative navigation control algorithm for the ‘position history’ embodiment of the navigational control system 10 according to the present invention, the microprocessor 24 is operative to count and store the number of visits to each cell and to compute the total number of visits to cells contiguous to (neighboring) each such visited cell (cell counting). The microprocessor 24 is further operative to compare the total number of neighboring-cell visits as each cell is visited to a threshold value (see, e.g.,
In another representative navigation control algorithm for the ‘position history’ embodiment of the navigational control system 10 of the present invention, one or more cells in the stored grid map are pre-identified (i.e., prior to operating the robotic device 100) as “hot spots” in the defined working area. As the robotic device 100 visits any particular cell C, the microprocessor 24 is operative to determine whether the visited cell has been identified as a “hot spot” (true-false determination). If the microprocessor 24 determines that the visited cell C is a “hot spot” (triggering event), the microprocessor 24 is operative to cause a control signal to be communicated to the robotic device 100 via the control signal transmitting unit 32. Reception of the control signal causes the robotic device 100 to implement the prescribed conduct specified by the control signal, e.g., one or more of the basic maneuvers described above and/or a Spot Coverage pattern or Obstacle-Following behavioral mode as described above.
The foregoing representative examples of navigation control algorithms for the ‘position history’ embodiment of the navigational control system 10 according to the present invention are implemented without knowledge of the travel vector of the robotic device 100, i.e., based solely upon the identification of visited cells by means of the bearing, distance parameters derived by the receiving subsystem 20. Another representative example of a navigation control algorithm for the ‘position history’embodiment of the navigation control system 10 of the present invention utilizes the travel vector as an element of the position history in issuing a control signal.
The microprocessor 24 is operative to count and store the number of times a cell has been visited (cell counting) and further operative to compare this number to the number of times each contiguous (or neighboring) cell has been visited. For this navigation control algorithm, the triggering event is a numerical differential between the number of visits to the currently-visited cell number and the number of visits to each of the neighboring-cells that identifies the neighboring cell or cells that have been least-visited as compared to the currently-visited cell. The triggering event would cause the receiving system 20 to issue a control signal to the robotic device 100 that causes the robotic device 100 to move from the currently-visited cell to the neighboring cell that has been visited least, e.g., by implementing one or more basic maneuvers as described herein. If two or more neighboring cells have been visited least, the control signal would cause the robotic device to move from the currently-visited cell to the least visited neighboring cell that is most compatible with the current travel vector of the robotic device 100, e.g., minimum travel distance.
Using
One representative navigation control algorithm for the ‘instantaneous position’ of the navigational control system 10 uses elapsed time (either random or predetermined) as the predetermined triggering event to cause the robotic device 10 to move to a predetermined position B in the defined working environment. The microprocessor 24 is operative, upon expiration of the elapsed time (the predetermined triggering event), to determine the instantaneous position (hereinafter identified as “position A”) of the robotic device 100 as described herein. Since position A is an unknown variable until the predetermined triggering event is realized, the prescribed conduct, i.e., the basic maneuvers, necessary to move the robotic device 100 from position A to position B are also unknown. Once position A has been determined by the navigational control system 10, the basic maneuvers necessary to move the robotic device 100 from position A to position B are determinable since both position A and position B are known variables (in terms of their known bearing, distance parameter pairs with respect to the receiving subsystem 20). A determination of the basic maneuvers that will be implemented by the robotic device 100 can be accomplished by any conventional computational technique.
Another exemplary navigation control algorithm for the ‘instantaneous position’ embodiment of the navigational control system 10 is a variation of the “hot spot” navigation control algorithm for the ‘position history’ embodiment of the navigational control system 10. In this illustrative embodiment, both position A and position B are known variables and accordingly, the basic maneuver(s) to move the robotic device 100 from position A to position B are known. In this example, the predetermined triggering event is a TRUE determination that the instantaneous position of the robotic device 100 is equal to position A (position A may be stored in memory 28 as a “zone”—defining some arbitrary area centered about position A—rather than a single point position to increase the probability that the instantaneous position of the robotic device 100 at some time will equal position A).
The receiving subsystem 20 comprises a set of detection units 30M where M is an integer defining the number of individual detection units comprising the set for the navigational control system 10. The number and positioning of the set of detection units 30M should be such that as much of the defined working area as possible is within the field-of-view of the receiving subsystem 20 and that the fields-of-view of at least two (but preferably more) detection units 30M cover the same area within the defined working area.
In preferred embodiments of the navigational control system 10 according to the present invention, the receiving subsystem 20 functions as a “base station” for the system 10. In this functional role, the receiving subsystem 20 is a portable, standalone unit that is stationarily positioned within the defined working area, preferably abutting a wall bounding the defined working area (the ‘wall unit’ configuration). Alternatively, the receiving subsystem 20 can be positioned within the defined working area distally of the walls bounding the defined working area (the ‘free-standing’ configuration). The receiving subsystem 20 as the base station establishes and, for the ‘position history’ embodiment of the navigational control system 10, stores the grid map of cells representing the defined working area and represents the origin (0, 0) of the grid map of cells described above.
For those embodiments where the receiving subsystem 20 is operated as a wall unit configuration, the individual detection units 30M have a known spaced-apart relationship and configured and operative to have a 180° field-of-view. For example,
For those embodiments where the receiving subsystem 20 is operated as a free-standing configuration, the individual detection units 30M likewise spaced apart by known angular distances and configured and operative have a field-of-view greater than 180°. A representative embodiment of the receiving subsystem 20 operated as a free-standing base station would comprise four detection units 30M.
The detection units 30M are configured and operative to detect a parameter of one or more of the directed beams emitted by the transmitting units 14N, e.g., voltages V representing the relative signal strengths of the detected directed beam(s). In a preferred embodiment, each detection unit 30M is configured and operative to average the detected signal strength parameter (e.g., voltage) when the detection unit 30M detects two directed beams simultaneously. The receiving subsystem 20 executes a signal processing algorithm that processes the detected parameters provided by the detection units 30M, i.e., relative signal strengths of the detected beams, utilizing a conventional technique to determine the absolute bearing between the robotic device 100 and the receiving subsystem 20.
To provide the distance determination capability for the receiving subsystem 20, the receiving subsystem 20 is preferably calibrated prior to use. This involves positioning the robotic device 100 at a predetermined distance from the receiving subsystem 20 and operating one (or more) of the transmitting units 14N to emit a directed beam at the receiving subsystem 20. The parameter of the directed beam detected by the detection units 30M, e.g., a voltage representing the signal strength of the directed beam as detected, is correlated to the predetermined distance and used to generate a look-up table of signal strength versus distance for the defined working area. This look-up table is stored in the memory module 28 of the receiving subsystem 20. As the signal strengths of directed beams are detected during operation of the navigational control system 10, the receiving subsystem 20 uses the detected signal strengths as pointers to the stored look-up table to determine the corresponding distances (between the receiving subsystem 20 and the robotic device 100).
Alternatively, the receiving subsystem 20 could be configured and operative to implement a signal processing algorithm that utilizes the known attenuation characteristics, i.e., signal strength versus distance, of the operating frequency of the directed beams emitted by the transmitting units 14N. This embodiment presupposes that the transmitting units 14N are rated and emitting directed beams of known signal strength.
For the sophisticated embodiment of the navigational control system 10 according to the present invention described above wherein the individual transmitting units 14N of the transmitting subsystem 12 are operated at a unique operating frequency, the detection units 30M of the receiving subsystem 20 are configured to scan the set of unique operating frequencies utilized by the transmitting units 14N. The receiving subsystem 20 is configured and operative to cause the detection units 30M to sequentially scan through these frequencies during operation of the navigational control system 10.
For the innovative embodiment of the transmitting subsystem 12 described above in connection with
For the innovative embodiment of the transmitting subsystem 12 described above in connection with
The signal strength detection times allows the particular transmitting units 14N on the robotic device 100 to be identified, i.e., transmitting units 143 and 142 in the example of
Even though the transmitting units 14N mounted in combination with the robotic device 100 are interchangeable, the specific location of each individual transmitting unit 14N on the robotic device 100 is a known quantity. Based upon the identification of the transmitting unit 14N that emitted the directed beam detected by the receiving subsystem 20, the receiving subsystem 20 can execute rather straightforward geometric calculations, based upon the location of the identified transmitting unit 14N, to determine the travel vector of the robotic device 100.
When the receiving subsystem 20 functions as the base station, a means is required to communicate the control signal to the robotic device. Accordingly, embodiments of the receiving subsystem 20 that operate as a base station further include a transmitting unit 32 (see
While the robotic device 100 is described (and depicted in
As disclosed above, in preferred embodiments of the navigational control system 10 according to the present invention, the receiving subsystem 20 functions as the base station, i.e., the wall unit configuration, and the transmitting subsystem 12 is integrated in combination with the robotic device 100. One preferred embodiment that is illustrative of the features and functionality of the navigational control system 10 according to the present invention is exemplarily illustrated in
The transmitting subsystem 12 of the illustrated embodiment of the navigational control system 10 is integrated in combination with the robotic device 100 and comprises a set of transmitting units 14N (eight (8) for the described embodiment such that N equals the integers 1-8) that are operative to generate a corresponding set of directed beams DBN (where N equals the integers 1-8) as illustrated in
Preferably, the predetermined emission pattern θN of the directed beams DBN is correlated with the number of transmitting units 14N so that the transmitting subsystem 12 of the navigational control system 10 emulates an omnidirectional transmitting source. An omnidirectional transmitting source is necessary to ensure that one or more of the directed beams DBN are detected by the receiving subsystem 20 since the position and orientation of the robotic device 100 in the defined working area (e.g., in terms of its forward motion FM), with respect to the receiving station 20, is an unknown variable at any particular moment in time. Preferably the emission patterns θN of the directed beams DBN overlap.
As an examination of
The receiving subsystem 20 is operative to implement the signal processing algorithm to compute the absolute bearing and distance between the receiving subsystem 20 and the robotic device 100. The receiving subsystem 20 then implements the navigation control algorithm to correlate the computed bearing and distance with one of the cells comprising the grid map of the defined working area WA stored in the memory module 28, and adds such cell to the position history of the robotic device 100 to update the position history. The receiving subsystem 20 is then operative under the navigation control algorithm to determine if there is a predetermined triggering event associated with this updated position history. If so, the receiving subsystem 20 is operative to select the appropriate control signal, as determined by the navigation control algorithm, and transmit such control signal to the receiver unit 16 of the robotic device 100 using the transmitting system 32 (see
An exemplary embodiment of a navigational control system 10′ according to the present invention wherein the transmitting subsystem 12 functions as a base station and the receiving subsystem 20 is integrated in combination with the robotic device 100 is illustrated in
Each transmitting unit 14N comprising this distributed set is configured and operative to emit a directed beam having a predetermined emission pattern θN along a predetermined beam axis DBN (DB1, DB2, and DB3 in
The receiving subsystem 20 for the navigational control system 10′ preferably comprises a single omnidirectional detection unit 30 which may be of the type described in commonly-owned, U.S. patent application Ser. No. 10/056,804, filed 24 Jan. 2002, entitled METHOD AND SYSTEM FOR ROBOT LOCALIZATION AND CONFINEMENT (the virtual wall system summarily described herein). The omnidirectional detection unit 30 is configured and operative to scan through the unique operating frequencies utilized by the distributed transmitting units 141, 142, 143.
The omnidirectional detection unit 30 is operative to detect the directed beams DB1, DB2, DB3 emitted by the distributed transmitting units 141, 142, 143. The receiving subsystem is configured and operative to process the signals of the detected directed beam to determine the absolute position of the robotic device 100 within the defined working area WA. This absolute position is defined in terms of a cell of the grid map of the defined working area WA. A sequence of absolute positions, determined as described above, identifies a sequence of cells that defines the position history of the robotic device 100.
The receiver subsystem 20 is operative as described above to utilize a navigation control algorithm to determine whether a triggering event has occurred in the position history, and if a trigger event has occurred, the receiver subsystem 20 is operative to communicate the control signal associated with the triggering event/navigation control algorithm to the robotic device 100. The robotic device 100 is operative, in response to the communicated control signal, to implement the prescribed conduct specified by the control signal.
A variety of modifications and variations of the present invention are possible in light of the above teachings. The navigational control system 10 according to the present invention has been described above as determining and using the instantaneous position (or a sequence of instantaneous positions) of a robotic device as a control parameter for directly altering the movement activity of the robotic device. One skilled in the art will appreciate that the navigational control system according to the present invention can be used for other purposes. For example, the navigational control system of the present invention can be used for correcting errors in movement activity of robotic devices relying upon dead reckoning. It is therefore to be understood that, within the scope of the appended claims, the present invention may be practiced other than as specifically described herein.
This U.S. patent application is a continuation of, and claims priority under 35 U.S.C. §120 from, U.S. patent application Ser. No. 12/512,114, filed on Jul. 30, 2009, which is a continuation-in-part of, and claims priority under 35 U.S.C. §120 from, U.S. patent application Ser. No. 11/682,642, filed on Mar. 6, 2007, which is a continuation of U.S. patent application Ser. No. 11/341,111, filed on Jan. 27, 2006 (now U.S. Pat. No. 7,188,000), which is a continuation of U.S. patent application Ser. No. 10/661,835, filed Sep. 12, 2003 (now U.S. Pat. No. 7,024,278), which claims priority under 35 U.S.C. §119(e) to U.S. Provisional Application 60/410,480, filed on Sep. 13, 2002. U.S. patent application Ser. No. 12/512,114 is also a continuation-in-part of, and claims priority under 35 U.S.C. §120 from, U.S. patent application Ser. No. 12/255,393, filed on Oct. 21, 2008, which is a continuation of U.S. patent application Ser. No. 11/860,272, filed on Sep. 24, 2007 (now U.S. Pat. No. 7,459,871), which is a continuation of U.S. patent application Ser. No. 11/533,294, filed on Sep. 19, 2006 (now U.S. Pat. No. 7,288,912), which is a continuation of U.S. patent application Ser. No. 11/109,832, filed on Apr. 19, 2005, which is a continuation of U.S. patent application Ser. No. 10/766,303, filed on Jan. 28, 2004. The disclosures of these prior applications are considered part of the disclosure of this application and are hereby incorporated herein by reference in their entireties. This U.S. patent application is related to commonly-owned U.S. patent application Ser. No. 10/056,804, filed on Jan. 24, 2002 entitled “Method and System for Robot Localization and Confinement”, U.S. patent application Ser. No. 10/320,729, filed on Dec. 16, 2002, entitled “Autonomous Floor-Cleaning Device”, U.S. patent application Ser. No. 10/167,851, filed on Jun. 12, 2002, entitled “Method and System for Multi-Mode Coverage for an Autonomous Robot”, and U.S. continuation-in-part patent application Ser. No. 10/453,202, filed on Jun. 3, 2003, entitled “Robot Obstacle Detection System”, each of which is hereby incorporated herein by reference in its entirety.
Number | Name | Date | Kind |
---|---|---|---|
1755054 | Darst | Apr 1930 | A |
1780221 | Buchmann | Nov 1930 | A |
1970302 | Gerhardt | Aug 1934 | A |
2136324 | John | Nov 1938 | A |
2302111 | Dow et al. | Nov 1942 | A |
2353621 | Sav et al. | Jul 1944 | A |
2770825 | Pullen | Nov 1956 | A |
3119369 | Harland et al. | Jan 1964 | A |
3166138 | Dunn | Jan 1965 | A |
3333564 | Waters | Aug 1967 | A |
3375375 | Robert et al. | Mar 1968 | A |
3381652 | Schaefer et al. | May 1968 | A |
3569727 | Aggarwal et al. | Mar 1971 | A |
3678882 | Kinsella | Jul 1972 | A |
3744586 | Leinauer | Jul 1973 | A |
3756667 | Bombardier et al. | Sep 1973 | A |
3809004 | Leonheart | May 1974 | A |
3816004 | Bignardi | Jun 1974 | A |
3845831 | James | Nov 1974 | A |
RE28268 | Autrand | Dec 1974 | E |
3853086 | Asplund | Dec 1974 | A |
3863285 | Hukuba | Feb 1975 | A |
3888181 | Kups | Jun 1975 | A |
3952361 | Wilkins | Apr 1976 | A |
3989311 | Debrey | Nov 1976 | A |
3989931 | Phillips | Nov 1976 | A |
4004313 | Capra | Jan 1977 | A |
4012681 | Finger et al. | Mar 1977 | A |
4070170 | Leinfelt | Jan 1978 | A |
4175589 | Nakamura et al. | Nov 1979 | A |
4175892 | De brey | Nov 1979 | A |
4196727 | Verkaart et al. | Apr 1980 | A |
4198727 | Farmer | Apr 1980 | A |
4199838 | Simonsson | Apr 1980 | A |
4209254 | Reymond et al. | Jun 1980 | A |
D258901 | Keyworth | Apr 1981 | S |
4297578 | Carter | Oct 1981 | A |
4309758 | Halsall et al. | Jan 1982 | A |
4328545 | Halsall et al. | May 1982 | A |
4367403 | Miller | Jan 1983 | A |
4401909 | Gorsek | Aug 1983 | A |
4416033 | Specht | Nov 1983 | A |
4445245 | Lu | May 1984 | A |
4465370 | Yuasa et al. | Aug 1984 | A |
4477998 | You | Oct 1984 | A |
4481692 | Kurz | Nov 1984 | A |
4482960 | Pryor | Nov 1984 | A |
4492058 | Goldfarb et al. | Jan 1985 | A |
D278732 | Ohkado | May 1985 | S |
4518437 | Sommer | May 1985 | A |
4534637 | Suzuki et al. | Aug 1985 | A |
4556313 | Miller et al. | Dec 1985 | A |
4575211 | Matsumura et al. | Mar 1986 | A |
4580311 | Kurz | Apr 1986 | A |
4601082 | Kurz | Jul 1986 | A |
4618213 | Chen | Oct 1986 | A |
4620285 | Perdue | Oct 1986 | A |
4624026 | Olson et al. | Nov 1986 | A |
4628454 | Ito | Dec 1986 | A |
4638445 | Mattaboni | Jan 1987 | A |
4644156 | Takahashi et al. | Feb 1987 | A |
4649504 | Krouglicof et al. | Mar 1987 | A |
4652917 | Miller | Mar 1987 | A |
4654492 | Koerner et al. | Mar 1987 | A |
4654924 | Getz et al. | Apr 1987 | A |
4660969 | Sorimachi et al. | Apr 1987 | A |
4662854 | Fang | May 1987 | A |
4680827 | Hummel | Jul 1987 | A |
D292223 | Trumbull | Oct 1987 | S |
4700301 | Dyke | Oct 1987 | A |
4703820 | Reinaud | Nov 1987 | A |
4710020 | Maddox et al. | Dec 1987 | A |
4728801 | O'Connor | Mar 1988 | A |
4733343 | Yoneda et al. | Mar 1988 | A |
4735136 | Lee et al. | Apr 1988 | A |
4735138 | Gawler et al. | Apr 1988 | A |
4748336 | Fujie et al. | May 1988 | A |
4748833 | Nagasawa | Jun 1988 | A |
4767213 | Hummel | Aug 1988 | A |
4769700 | Pryor | Sep 1988 | A |
D298766 | Tanno et al. | Nov 1988 | S |
4796198 | Boultinghouse et al. | Jan 1989 | A |
4806751 | Abe et al. | Feb 1989 | A |
4811228 | Hyyppa | Mar 1989 | A |
4813906 | Matsuyama et al. | Mar 1989 | A |
4817000 | Eberhardt | Mar 1989 | A |
4818875 | Weiner | Apr 1989 | A |
4829442 | Kadonoff et al. | May 1989 | A |
4829626 | Harkonen et al. | May 1989 | A |
4832098 | Palinkas et al. | May 1989 | A |
4851661 | Everett | Jul 1989 | A |
4854006 | Nishimura et al. | Aug 1989 | A |
4855915 | Dallaire | Aug 1989 | A |
4857912 | Everett et al. | Aug 1989 | A |
4858132 | Holmquist | Aug 1989 | A |
4867570 | Sorimachi et al. | Sep 1989 | A |
4880474 | Koharagi et al. | Nov 1989 | A |
4891762 | Chotiros | Jan 1990 | A |
4893025 | Lee | Jan 1990 | A |
4905151 | Weiman et al. | Feb 1990 | A |
4912643 | Beirne | Mar 1990 | A |
4918441 | Bohman | Apr 1990 | A |
4919489 | Kopsco | Apr 1990 | A |
4920060 | Parrent et al. | Apr 1990 | A |
4920605 | Takashima | May 1990 | A |
4937912 | Kurz | Jul 1990 | A |
4953253 | Fukuda et al. | Sep 1990 | A |
4954962 | Evans et al. | Sep 1990 | A |
4955714 | Stotler et al. | Sep 1990 | A |
4961303 | McCarty et al. | Oct 1990 | A |
4961304 | Ovsborn et al. | Oct 1990 | A |
4971591 | Raviv et al. | Nov 1990 | A |
4973912 | Kaminski et al. | Nov 1990 | A |
4977618 | Allen | Dec 1990 | A |
4977639 | Takahashi et al. | Dec 1990 | A |
4986663 | Cecchi et al. | Jan 1991 | A |
5001635 | Yasutomi et al. | Mar 1991 | A |
5012886 | Jonas et al. | May 1991 | A |
5018240 | Holman | May 1991 | A |
5020186 | Lessig et al. | Jun 1991 | A |
5022812 | Coughlan et al. | Jun 1991 | A |
5023788 | Kitazume et al. | Jun 1991 | A |
5024529 | Svetkoff et al. | Jun 1991 | A |
D318500 | Malewicki et al. | Jul 1991 | S |
5032775 | Mizuno et al. | Jul 1991 | A |
5033151 | Kraft et al. | Jul 1991 | A |
5033291 | Podoloff et al. | Jul 1991 | A |
5040116 | Evans et al. | Aug 1991 | A |
5045769 | Everett | Sep 1991 | A |
5049802 | Mintus et al. | Sep 1991 | A |
5051906 | Evans et al. | Sep 1991 | A |
5062819 | Mallory | Nov 1991 | A |
5070567 | Holland | Dec 1991 | A |
5084934 | Lessig et al. | Feb 1992 | A |
5090321 | Abouav | Feb 1992 | A |
5094311 | Akeel | Mar 1992 | A |
5105550 | Shenoha | Apr 1992 | A |
5127128 | Lee | Jul 1992 | A |
5136675 | Hodson | Aug 1992 | A |
5142985 | Stearns et al. | Sep 1992 | A |
5144471 | Takanashi et al. | Sep 1992 | A |
5144714 | Mori et al. | Sep 1992 | A |
5144715 | Matsuyo et al. | Sep 1992 | A |
5152028 | Hirano | Oct 1992 | A |
5152202 | Strauss | Oct 1992 | A |
5155684 | Burke et al. | Oct 1992 | A |
5163202 | Kawakami et al. | Nov 1992 | A |
5163320 | Goshima et al. | Nov 1992 | A |
5164579 | Pryor et al. | Nov 1992 | A |
5165064 | Mattaboni | Nov 1992 | A |
5170352 | McTamaney et al. | Dec 1992 | A |
5173881 | Sindle | Dec 1992 | A |
5182833 | Yamaguchi et al. | Feb 1993 | A |
5202742 | Frank et al. | Apr 1993 | A |
5206500 | Decker et al. | Apr 1993 | A |
5208521 | Aoyama | May 1993 | A |
5216777 | Moro et al. | Jun 1993 | A |
5227985 | DeMenthon | Jul 1993 | A |
5233682 | Abe et al. | Aug 1993 | A |
5251358 | Moro et al. | Oct 1993 | A |
5276618 | Everett | Jan 1994 | A |
5276939 | Uenishi | Jan 1994 | A |
5277064 | Knigga et al. | Jan 1994 | A |
5284452 | Corona | Feb 1994 | A |
D345707 | Alister | Apr 1994 | S |
5307273 | Oh et al. | Apr 1994 | A |
5309592 | Hiratsuka | May 1994 | A |
5310379 | Hippely et al. | May 1994 | A |
5315227 | Pierson et al. | May 1994 | A |
5319827 | Yang | Jun 1994 | A |
5321614 | Ashworth | Jun 1994 | A |
5323483 | Baeg | Jun 1994 | A |
5341186 | Kato | Aug 1994 | A |
5341549 | Wirtz et al. | Aug 1994 | A |
5345649 | Whitlow | Sep 1994 | A |
5353224 | Lee et al. | Oct 1994 | A |
5363305 | Cox et al. | Nov 1994 | A |
5363935 | Schempf et al. | Nov 1994 | A |
5369838 | Wood et al. | Dec 1994 | A |
5386862 | Glover et al. | Feb 1995 | A |
5399951 | Lavallee et al. | Mar 1995 | A |
5400244 | Watanabe et al. | Mar 1995 | A |
5404612 | Ishikawa | Apr 1995 | A |
5410479 | Coker | Apr 1995 | A |
5435405 | Schempf et al. | Jul 1995 | A |
5442358 | Keeler et al. | Aug 1995 | A |
5446445 | Bloomfield et al. | Aug 1995 | A |
5451135 | Schempf et al. | Sep 1995 | A |
5465525 | Mifune et al. | Nov 1995 | A |
5465619 | Sotack et al. | Nov 1995 | A |
5471560 | Allard et al. | Nov 1995 | A |
5491670 | Weber | Feb 1996 | A |
5498948 | Bruni et al. | Mar 1996 | A |
5502638 | Takenaka | Mar 1996 | A |
5505072 | Oreper | Apr 1996 | A |
5510893 | Suzuki | Apr 1996 | A |
5511147 | Abdel | Apr 1996 | A |
5537711 | Tseng | Jul 1996 | A |
5542148 | Young | Aug 1996 | A |
5546631 | Chambon | Aug 1996 | A |
5551525 | Pack et al. | Sep 1996 | A |
D375592 | Ljunggren | Nov 1996 | S |
5608306 | Rybeck et al. | Mar 1997 | A |
5608894 | Kawakami et al. | Mar 1997 | A |
5608944 | Gordon | Mar 1997 | A |
5610488 | Miyazawa | Mar 1997 | A |
5613261 | Kawakami et al. | Mar 1997 | A |
5613269 | Miwa | Mar 1997 | A |
5636402 | Kubo et al. | Jun 1997 | A |
5642299 | Hardin et al. | Jun 1997 | A |
5646494 | Han | Jul 1997 | A |
5647554 | Ikegami et al. | Jul 1997 | A |
5652489 | Kawakmai | Jul 1997 | A |
5682839 | Grimsley et al. | Nov 1997 | A |
5696675 | Nakamura et al. | Dec 1997 | A |
5698861 | Oh | Dec 1997 | A |
5710506 | Broell et al. | Jan 1998 | A |
5714119 | Kawagoe et al. | Feb 1998 | A |
5717169 | Liang et al. | Feb 1998 | A |
5717484 | Hamaguchi et al. | Feb 1998 | A |
5720077 | Nakamura et al. | Feb 1998 | A |
5732401 | Conway | Mar 1998 | A |
5735959 | Kubo et al. | Apr 1998 | A |
5745235 | Vercammen et al. | Apr 1998 | A |
5752871 | Tsuzuki | May 1998 | A |
5756904 | Oreper et al. | May 1998 | A |
5764888 | Bolan et al. | Jun 1998 | A |
5767437 | Rogers | Jun 1998 | A |
5767960 | Orman | Jun 1998 | A |
5777596 | Herbert | Jul 1998 | A |
5778486 | Kim | Jul 1998 | A |
5781697 | Jeong | Jul 1998 | A |
5786602 | Pryor et al. | Jul 1998 | A |
5793900 | Nourbakhsh et al. | Aug 1998 | A |
5812267 | Everett et al. | Sep 1998 | A |
5814808 | Takada et al. | Sep 1998 | A |
5815880 | Nakanishi | Oct 1998 | A |
5815884 | Imamura et al. | Oct 1998 | A |
5819008 | Asama et al. | Oct 1998 | A |
5819360 | Fujii | Oct 1998 | A |
5819936 | Saveliev et al. | Oct 1998 | A |
5820821 | Kawagoe et al. | Oct 1998 | A |
5821730 | Drapkin | Oct 1998 | A |
5825981 | Matsuda | Oct 1998 | A |
5828770 | Leis et al. | Oct 1998 | A |
5831597 | West et al. | Nov 1998 | A |
5839532 | Yoshiji et al. | Nov 1998 | A |
5869910 | Colens | Feb 1999 | A |
5896611 | Haaga | Apr 1999 | A |
5903124 | Kawakami | May 1999 | A |
5905209 | Oreper | May 1999 | A |
5907886 | Buscher | Jun 1999 | A |
5910700 | Crotzer | Jun 1999 | A |
5911260 | Suzuki | Jun 1999 | A |
5916008 | Wong | Jun 1999 | A |
5924167 | Wright et al. | Jul 1999 | A |
5933102 | Miller et al. | Aug 1999 | A |
5933913 | Wright et al. | Aug 1999 | A |
5940346 | Sadowsky et al. | Aug 1999 | A |
5942869 | Katou et al. | Aug 1999 | A |
5947225 | Kawakami et al. | Sep 1999 | A |
5950408 | Schaedler | Sep 1999 | A |
5959423 | Nakanishi et al. | Sep 1999 | A |
5968281 | Wright et al. | Oct 1999 | A |
5974348 | Rocks | Oct 1999 | A |
5974365 | Mitchell | Oct 1999 | A |
5983448 | Wright et al. | Nov 1999 | A |
5984880 | Lander et al. | Nov 1999 | A |
5989700 | Krivopal | Nov 1999 | A |
5991951 | Kubo et al. | Nov 1999 | A |
5995883 | Nishikado | Nov 1999 | A |
5995884 | Allen et al. | Nov 1999 | A |
5996167 | Close | Dec 1999 | A |
5998953 | Nakamura et al. | Dec 1999 | A |
5998971 | Corbridge | Dec 1999 | A |
6000088 | Wright et al. | Dec 1999 | A |
6009358 | Angott et al. | Dec 1999 | A |
6021545 | Delgado et al. | Feb 2000 | A |
6023813 | Thatcher et al. | Feb 2000 | A |
6023814 | Imamura | Feb 2000 | A |
6025687 | Himeda et al. | Feb 2000 | A |
6026539 | Mouw et al. | Feb 2000 | A |
6030464 | Azevedo | Feb 2000 | A |
6030465 | Marcussen et al. | Feb 2000 | A |
6032542 | Warnick et al. | Mar 2000 | A |
6036572 | Sze | Mar 2000 | A |
6038501 | Kawakami | Mar 2000 | A |
6040669 | Hog | Mar 2000 | A |
6041472 | Kasen et al. | Mar 2000 | A |
6046800 | Ohtomo et al. | Apr 2000 | A |
6049620 | Dickinson et al. | Apr 2000 | A |
6052821 | Chouly et al. | Apr 2000 | A |
6055042 | Sarangapani | Apr 2000 | A |
6055702 | Imamura et al. | May 2000 | A |
6061868 | Moritsch et al. | May 2000 | A |
6065182 | Wright et al. | May 2000 | A |
6073432 | Schaedler | Jun 2000 | A |
6076025 | Ueno et al. | Jun 2000 | A |
6076026 | Jambhekar et al. | Jun 2000 | A |
6076226 | Reed | Jun 2000 | A |
6076227 | Schallig et al. | Jun 2000 | A |
6081257 | Zeller | Jun 2000 | A |
6088020 | Mor | Jul 2000 | A |
6094775 | Behmer | Aug 2000 | A |
6099091 | Campbell | Aug 2000 | A |
6101670 | Song | Aug 2000 | A |
6101671 | Wright et al. | Aug 2000 | A |
6108031 | King et al. | Aug 2000 | A |
6108067 | Okamoto | Aug 2000 | A |
6108076 | Hanseder | Aug 2000 | A |
6108269 | Kabel | Aug 2000 | A |
6108597 | Kirchner et al. | Aug 2000 | A |
6112143 | Allen et al. | Aug 2000 | A |
6112996 | Matsuo | Sep 2000 | A |
6119057 | Kawagoe | Sep 2000 | A |
6122798 | Kobayashi et al. | Sep 2000 | A |
6124694 | Bancroft et al. | Sep 2000 | A |
6125498 | Roberts et al. | Oct 2000 | A |
6131237 | Kasper et al. | Oct 2000 | A |
6138063 | Himeda | Oct 2000 | A |
6142252 | Kinto et al. | Nov 2000 | A |
6146278 | Kobayashi | Nov 2000 | A |
6154279 | Thayer | Nov 2000 | A |
6154694 | Aoki et al. | Nov 2000 | A |
6160479 | Åhlen et al. | Dec 2000 | A |
6167332 | Kurtzberg et al. | Dec 2000 | A |
6167587 | Kasper et al. | Jan 2001 | B1 |
6192548 | Huffman | Feb 2001 | B1 |
6216307 | Kaleta et al. | Apr 2001 | B1 |
6220865 | Macri et al. | Apr 2001 | B1 |
6226830 | Hendriks et al. | May 2001 | B1 |
6230362 | Kasper et al. | May 2001 | B1 |
6237741 | Guidetti | May 2001 | B1 |
6240342 | Fiegert et al. | May 2001 | B1 |
6243913 | Frank et al. | Jun 2001 | B1 |
6255793 | Peless et al. | Jul 2001 | B1 |
6259979 | Holmquist | Jul 2001 | B1 |
6261379 | Conrad et al. | Jul 2001 | B1 |
6263539 | Baig | Jul 2001 | B1 |
6263989 | Won | Jul 2001 | B1 |
6272936 | Oreper et al. | Aug 2001 | B1 |
6276478 | Hopkins et al. | Aug 2001 | B1 |
6278918 | Dickson et al. | Aug 2001 | B1 |
6282526 | Ganesh | Aug 2001 | B1 |
6283034 | Miles | Sep 2001 | B1 |
6285778 | Nakajima et al. | Sep 2001 | B1 |
6285930 | Dickson et al. | Sep 2001 | B1 |
6300737 | Bergvall et al. | Oct 2001 | B1 |
6321337 | Reshef et al. | Nov 2001 | B1 |
6321515 | Colens | Nov 2001 | B1 |
6323570 | Nishimura et al. | Nov 2001 | B1 |
6324714 | Walz et al. | Dec 2001 | B1 |
6327741 | Reed | Dec 2001 | B1 |
6332400 | Meyer | Dec 2001 | B1 |
6339735 | Peless et al. | Jan 2002 | B1 |
6362875 | Burkley | Mar 2002 | B1 |
6370453 | Sommer | Apr 2002 | B2 |
6374155 | Wallach et al. | Apr 2002 | B1 |
6374157 | Takamura | Apr 2002 | B1 |
6381802 | Park | May 2002 | B2 |
6385515 | Dickson et al. | May 2002 | B1 |
6388013 | Saraf et al. | May 2002 | B1 |
6389329 | Colens | May 2002 | B1 |
6400048 | Nishimura et al. | Jun 2002 | B1 |
6401294 | Kasper | Jun 2002 | B2 |
6408226 | Byrne et al. | Jun 2002 | B1 |
6412141 | Kasper et al. | Jul 2002 | B2 |
6415203 | Inoue et al. | Jul 2002 | B1 |
6421870 | Basham et al. | Jul 2002 | B1 |
6427285 | Legatt et al. | Aug 2002 | B1 |
6430471 | Kintou et al. | Aug 2002 | B1 |
6431296 | Won | Aug 2002 | B1 |
6437227 | Theimer | Aug 2002 | B1 |
6437465 | Nishimura et al. | Aug 2002 | B1 |
6438456 | Feddema et al. | Aug 2002 | B1 |
6438793 | Miner et al. | Aug 2002 | B1 |
6442476 | Poropat | Aug 2002 | B1 |
6443509 | Levin et al. | Sep 2002 | B1 |
6446302 | Kasper et al. | Sep 2002 | B1 |
6454036 | Airey et al. | Sep 2002 | B1 |
D464091 | Christianson | Oct 2002 | S |
6457206 | Judson | Oct 2002 | B1 |
6459955 | Bartsch et al. | Oct 2002 | B1 |
6463368 | Feiten et al. | Oct 2002 | B1 |
6465982 | Bergvall et al. | Oct 2002 | B1 |
6473167 | Odell | Oct 2002 | B1 |
6480762 | Uchikubo et al. | Nov 2002 | B1 |
6481515 | Kirkpatrick et al. | Nov 2002 | B1 |
6490539 | Dickson et al. | Dec 2002 | B1 |
6491127 | Holmberg et al. | Dec 2002 | B1 |
6493612 | Bisset et al. | Dec 2002 | B1 |
6493613 | Peless et al. | Dec 2002 | B2 |
6502657 | Kerrebrock et al. | Jan 2003 | B2 |
6504610 | Bauer et al. | Jan 2003 | B1 |
6507773 | Parker et al. | Jan 2003 | B2 |
D471243 | Cioffi et al. | Mar 2003 | S |
6535793 | Allard | Mar 2003 | B2 |
6540607 | Mokris et al. | Apr 2003 | B2 |
6548982 | Papanikolopoulos et al. | Apr 2003 | B1 |
6553612 | Dyson et al. | Apr 2003 | B1 |
6556722 | Russell et al. | Apr 2003 | B1 |
6556892 | Kuroki et al. | Apr 2003 | B2 |
6557104 | Vu et al. | Apr 2003 | B2 |
D474312 | Stephens et al. | May 2003 | S |
6563130 | Dworkowski et al. | May 2003 | B2 |
6571422 | Gordon et al. | Jun 2003 | B1 |
6572711 | Sclafani et al. | Jun 2003 | B2 |
6574536 | Kawagoe et al. | Jun 2003 | B1 |
6584376 | Van Kommer | Jun 2003 | B1 |
6586908 | Petersson et al. | Jul 2003 | B2 |
6587573 | Stam et al. | Jul 2003 | B1 |
6590222 | Bisset et al. | Jul 2003 | B1 |
6594551 | McKinney et al. | Jul 2003 | B2 |
6594844 | Jones | Jul 2003 | B2 |
D478884 | Slipy et al. | Aug 2003 | S |
6601265 | Burlington | Aug 2003 | B1 |
6604021 | Imai et al. | Aug 2003 | B2 |
6604022 | Parker et al. | Aug 2003 | B2 |
6611734 | Parker et al. | Aug 2003 | B2 |
6615108 | Peless et al. | Sep 2003 | B1 |
6615885 | Ohm | Sep 2003 | B1 |
6622465 | Jerome et al. | Sep 2003 | B2 |
6624744 | Wilson et al. | Sep 2003 | B1 |
6625843 | Kim et al. | Sep 2003 | B2 |
6629028 | Paromtchik et al. | Sep 2003 | B2 |
6639659 | Granger | Oct 2003 | B2 |
6658325 | Zweig | Dec 2003 | B2 |
6658354 | Lin | Dec 2003 | B2 |
6658692 | Lenkiewicz et al. | Dec 2003 | B2 |
6661239 | Ozick | Dec 2003 | B1 |
6662889 | De Fazio et al. | Dec 2003 | B2 |
6668951 | Won | Dec 2003 | B2 |
6670817 | Fournier et al. | Dec 2003 | B2 |
6687571 | Byrne et al. | Feb 2004 | B1 |
6690993 | Foulke et al. | Feb 2004 | B2 |
6697147 | Ko et al. | Feb 2004 | B2 |
6711280 | Stafsudd et al. | Mar 2004 | B2 |
6732826 | Song et al. | May 2004 | B2 |
6737591 | Lapstun et al. | May 2004 | B1 |
6741364 | Lange et al. | May 2004 | B2 |
6756703 | Chang | Jun 2004 | B2 |
6760647 | Nourbakhsh et al. | Jul 2004 | B2 |
6764373 | Osawa et al. | Jul 2004 | B1 |
6769004 | Barrett | Jul 2004 | B2 |
6774596 | Bisset | Aug 2004 | B1 |
6779380 | Nieuwkamp | Aug 2004 | B1 |
6810305 | Kirkpatrick | Oct 2004 | B2 |
6832407 | Salem et al. | Dec 2004 | B2 |
6836701 | McKee | Dec 2004 | B2 |
6845297 | Allard | Jan 2005 | B2 |
6856811 | Burdue et al. | Feb 2005 | B2 |
6859010 | Jeon et al. | Feb 2005 | B2 |
6859682 | Naka et al. | Feb 2005 | B2 |
6860206 | Rudakevych et al. | Mar 2005 | B1 |
6865447 | Lau et al. | Mar 2005 | B2 |
6870792 | Chiappetta | Mar 2005 | B2 |
6871115 | Huang et al. | Mar 2005 | B2 |
6886651 | Slocum et al. | May 2005 | B1 |
6888333 | Laby | May 2005 | B2 |
6906702 | Tanaka et al. | Jun 2005 | B1 |
6914403 | Tsurumi | Jul 2005 | B2 |
6917854 | Bayer | Jul 2005 | B2 |
6925357 | Wang et al. | Aug 2005 | B2 |
6925679 | Wallach et al. | Aug 2005 | B2 |
6929548 | Wang | Aug 2005 | B2 |
6940291 | Ozick | Sep 2005 | B1 |
6941199 | Bottomley et al. | Sep 2005 | B1 |
6957712 | Song et al. | Oct 2005 | B2 |
6960986 | Asama et al. | Nov 2005 | B2 |
6965211 | Tsurumi | Nov 2005 | B2 |
6968592 | Takeuchi et al. | Nov 2005 | B2 |
6975246 | Trudeau | Dec 2005 | B1 |
6980229 | Ebersole | Dec 2005 | B1 |
6985556 | Shanmugavel et al. | Jan 2006 | B2 |
6993954 | George et al. | Feb 2006 | B1 |
7013527 | Thomas et al. | Mar 2006 | B2 |
7024280 | Parker et al. | Apr 2006 | B2 |
7027893 | Perry et al. | Apr 2006 | B2 |
7030768 | Wanie | Apr 2006 | B2 |
7031805 | Lee et al. | Apr 2006 | B2 |
7032469 | Bailey | Apr 2006 | B2 |
7053578 | Diehl et al. | May 2006 | B2 |
7054716 | McKee et al. | May 2006 | B2 |
5987383 | Keller et al. | Jun 2006 | C1 |
7055210 | Keppler et al. | Jun 2006 | B2 |
7057120 | Ma et al. | Jun 2006 | B2 |
7057643 | Iida et al. | Jun 2006 | B2 |
7065430 | Naka et al. | Jun 2006 | B2 |
7066291 | Martins et al. | Jun 2006 | B2 |
7069124 | Whittaker et al. | Jun 2006 | B1 |
7079923 | Abramson et al. | Jul 2006 | B2 |
7085623 | Siegers | Aug 2006 | B2 |
7113847 | Chmura et al. | Sep 2006 | B2 |
7133746 | Abramson et al. | Nov 2006 | B2 |
7142198 | Lee | Nov 2006 | B2 |
7148458 | Schell et al. | Dec 2006 | B2 |
7155308 | Jones | Dec 2006 | B2 |
7167775 | Abramson et al. | Jan 2007 | B2 |
7171285 | Kim et al. | Jan 2007 | B2 |
7173391 | Jones et al. | Feb 2007 | B2 |
7174238 | Zweig | Feb 2007 | B1 |
7193384 | Norman et al. | Mar 2007 | B1 |
7196487 | Jones et al. | Mar 2007 | B2 |
7201786 | Wegelin et al. | Apr 2007 | B2 |
7211980 | Bruemmer et al. | May 2007 | B1 |
7225500 | Diehl et al. | Jun 2007 | B2 |
7246405 | Yan | Jul 2007 | B2 |
7248951 | Hulden | Jul 2007 | B2 |
7275280 | Haegermarck et al. | Oct 2007 | B2 |
7283892 | Boillot et al. | Oct 2007 | B1 |
7288912 | Landry et al. | Oct 2007 | B2 |
7318248 | Yan | Jan 2008 | B1 |
7320149 | Huffman et al. | Jan 2008 | B1 |
7324870 | Lee | Jan 2008 | B2 |
7328196 | Peters | Feb 2008 | B2 |
7332890 | Cohen et al. | Feb 2008 | B2 |
7352153 | Yan | Apr 2008 | B2 |
7359766 | Jeon et al. | Apr 2008 | B2 |
7360277 | Moshenrose et al. | Apr 2008 | B2 |
7363108 | Noda et al. | Apr 2008 | B2 |
7388879 | Sabe et al. | Jun 2008 | B2 |
7389166 | Harwig et al. | Jun 2008 | B2 |
7408157 | Yan | Aug 2008 | B2 |
7418762 | Arai et al. | Sep 2008 | B2 |
7430455 | Casey et al. | Sep 2008 | B2 |
7430462 | Chiu et al. | Sep 2008 | B2 |
7441298 | Svendsen et al. | Oct 2008 | B2 |
7444206 | Abramson et al. | Oct 2008 | B2 |
7448113 | Jones et al. | Nov 2008 | B2 |
7459871 | Landry et al. | Dec 2008 | B2 |
7467026 | Sakagami et al. | Dec 2008 | B2 |
7474941 | Kim et al. | Jan 2009 | B2 |
7503096 | Lin | Mar 2009 | B2 |
7515991 | Egawa et al. | Apr 2009 | B2 |
7555363 | Augenbraun et al. | Jun 2009 | B2 |
7557703 | Yamada et al. | Jul 2009 | B2 |
7568259 | Yan | Aug 2009 | B2 |
7571511 | Jones et al. | Aug 2009 | B2 |
7578020 | Jaworski et al. | Aug 2009 | B2 |
7600521 | Woo | Oct 2009 | B2 |
7603744 | Reindle | Oct 2009 | B2 |
7617557 | Reindle | Nov 2009 | B2 |
7620476 | Morse et al. | Nov 2009 | B2 |
7636982 | Jones et al. | Dec 2009 | B2 |
7647144 | Haegermarck | Jan 2010 | B2 |
7650666 | Jang | Jan 2010 | B2 |
7660650 | Kawagoe et al. | Feb 2010 | B2 |
7663333 | Jones et al. | Feb 2010 | B2 |
7693605 | Park | Apr 2010 | B2 |
7706917 | Chiappetta et al. | Apr 2010 | B1 |
7765635 | Park | Aug 2010 | B2 |
7801645 | Taylor et al. | Sep 2010 | B2 |
7805220 | Taylor et al. | Sep 2010 | B2 |
7809944 | Kawamoto | Oct 2010 | B2 |
7849555 | Hahm et al. | Dec 2010 | B2 |
7853645 | Brown et al. | Dec 2010 | B2 |
7920941 | Park et al. | Apr 2011 | B2 |
7937800 | Yan | May 2011 | B2 |
7957836 | Myeong et al. | Jun 2011 | B2 |
20010004719 | Sommer | Jun 2001 | A1 |
20010013929 | Torsten | Aug 2001 | A1 |
20010020200 | Das et al. | Sep 2001 | A1 |
20010025183 | Shahidi | Sep 2001 | A1 |
20010037163 | Allard | Nov 2001 | A1 |
20010043509 | Green et al. | Nov 2001 | A1 |
20010045883 | Holdaway et al. | Nov 2001 | A1 |
20010047895 | De Fazio et al. | Dec 2001 | A1 |
20020011367 | Kolesnik | Jan 2002 | A1 |
20020021219 | Edwards | Feb 2002 | A1 |
20020027652 | Paromtchik et al. | Mar 2002 | A1 |
20020036779 | Kiyoi et al. | Mar 2002 | A1 |
20020081937 | Yamada et al. | Jun 2002 | A1 |
20020095239 | Wallach et al. | Jul 2002 | A1 |
20020097400 | Jung et al. | Jul 2002 | A1 |
20020104963 | Mancevski | Aug 2002 | A1 |
20020108209 | Peterson | Aug 2002 | A1 |
20020112742 | Bredo et al. | Aug 2002 | A1 |
20020113973 | Ge | Aug 2002 | A1 |
20020116089 | Kirkpatrick | Aug 2002 | A1 |
20020120364 | Colens | Aug 2002 | A1 |
20020124343 | Reed | Sep 2002 | A1 |
20020153185 | Song et al. | Oct 2002 | A1 |
20020159051 | Guo | Oct 2002 | A1 |
20020166193 | Kasper | Nov 2002 | A1 |
20020169521 | Goodman et al. | Nov 2002 | A1 |
20020189871 | Won | Dec 2002 | A1 |
20030009259 | Hattori et al. | Jan 2003 | A1 |
20030023356 | Keable | Jan 2003 | A1 |
20030024986 | Mazz et al. | Feb 2003 | A1 |
20030028286 | Glenn et al. | Feb 2003 | A1 |
20030030399 | Jacobs | Feb 2003 | A1 |
20030058262 | Sato et al. | Mar 2003 | A1 |
20030067451 | Tagg et al. | Apr 2003 | A1 |
20030124312 | Autumn | Jul 2003 | A1 |
20030126352 | Barrett | Jul 2003 | A1 |
20030146384 | Logsdon et al. | Aug 2003 | A1 |
20030193657 | Uomori et al. | Oct 2003 | A1 |
20030221114 | Hino et al. | Nov 2003 | A1 |
20030229421 | Chmura et al. | Dec 2003 | A1 |
20030229474 | Suzuki et al. | Dec 2003 | A1 |
20030233171 | Heiligensetzer | Dec 2003 | A1 |
20030233870 | Mancevski | Dec 2003 | A1 |
20030233930 | Ozick | Dec 2003 | A1 |
20040016077 | Song et al. | Jan 2004 | A1 |
20040030451 | Solomon | Feb 2004 | A1 |
20040030570 | Solomon | Feb 2004 | A1 |
20040055163 | McCambridge et al. | Mar 2004 | A1 |
20040074038 | Im et al. | Apr 2004 | A1 |
20040074044 | Diehl et al. | Apr 2004 | A1 |
20040083570 | Song et al. | May 2004 | A1 |
20040085037 | Jones et al. | May 2004 | A1 |
20040093122 | Galibraith | May 2004 | A1 |
20040098167 | Yi et al. | May 2004 | A1 |
20040111821 | Lenkiewicz et al. | Jun 2004 | A1 |
20040113777 | Matsuhira et al. | Jun 2004 | A1 |
20040117064 | McDonald | Jun 2004 | A1 |
20040117846 | Karaoguz et al. | Jun 2004 | A1 |
20040118998 | Wingett et al. | Jun 2004 | A1 |
20040128028 | Miyamoto et al. | Jul 2004 | A1 |
20040133316 | Dean | Jul 2004 | A1 |
20040143919 | Wilder | Jul 2004 | A1 |
20040148419 | Chen et al. | Jul 2004 | A1 |
20040148731 | Damman et al. | Aug 2004 | A1 |
20040153212 | Profio et al. | Aug 2004 | A1 |
20040181706 | Chen et al. | Sep 2004 | A1 |
20040187249 | Jones et al. | Sep 2004 | A1 |
20040187457 | Colens | Sep 2004 | A1 |
20040196451 | Aoyama | Oct 2004 | A1 |
20040204792 | Taylor et al. | Oct 2004 | A1 |
20040210345 | Noda et al. | Oct 2004 | A1 |
20040210347 | Sawada et al. | Oct 2004 | A1 |
20040221790 | Sinclair et al. | Nov 2004 | A1 |
20040255425 | Arai et al. | Dec 2004 | A1 |
20050010330 | Abramson et al. | Jan 2005 | A1 |
20050021181 | Kim et al. | Jan 2005 | A1 |
20050067994 | Jones et al. | Mar 2005 | A1 |
20050085947 | Aldred et al. | Apr 2005 | A1 |
20050137749 | Jeon et al. | Jun 2005 | A1 |
20050144751 | Kegg et al. | Jul 2005 | A1 |
20050150074 | Diehl et al. | Jul 2005 | A1 |
20050150519 | Keppler et al. | Jul 2005 | A1 |
20050154795 | Kuz et al. | Jul 2005 | A1 |
20050165508 | Kanda et al. | Jul 2005 | A1 |
20050166354 | Uehigashi | Aug 2005 | A1 |
20050166355 | Tani | Aug 2005 | A1 |
20050172445 | Diehl et al. | Aug 2005 | A1 |
20050183229 | Uehigashi | Aug 2005 | A1 |
20050183230 | Uehigashi | Aug 2005 | A1 |
20050187678 | Myeong et al. | Aug 2005 | A1 |
20050192707 | Park et al. | Sep 2005 | A1 |
20050209736 | Kawagoe | Sep 2005 | A1 |
20050211880 | Schell et al. | Sep 2005 | A1 |
20050212929 | Schell et al. | Sep 2005 | A1 |
20050213082 | DiBernardo et al. | Sep 2005 | A1 |
20050213109 | Schell et al. | Sep 2005 | A1 |
20050217042 | Reindle | Oct 2005 | A1 |
20050218852 | Landry et al. | Oct 2005 | A1 |
20050222933 | Wesby | Oct 2005 | A1 |
20050229340 | Sawalski et al. | Oct 2005 | A1 |
20050229355 | Crouch et al. | Oct 2005 | A1 |
20050235451 | Yan | Oct 2005 | A1 |
20050251292 | Casey et al. | Nov 2005 | A1 |
20050255425 | Pierson | Nov 2005 | A1 |
20050258154 | Blankenship et al. | Nov 2005 | A1 |
20050273967 | Taylor et al. | Dec 2005 | A1 |
20050288819 | de Guzman | Dec 2005 | A1 |
20060000050 | Cipolla et al. | Jan 2006 | A1 |
20060010638 | Shimizu et al. | Jan 2006 | A1 |
20060020369 | Taylor et al. | Jan 2006 | A1 |
20060020370 | Abramson | Jan 2006 | A1 |
20060021168 | Nishikawa | Feb 2006 | A1 |
20060025134 | Cho et al. | Feb 2006 | A1 |
20060037170 | Shimizu | Feb 2006 | A1 |
20060042042 | Mertes et al. | Mar 2006 | A1 |
20060044546 | Lewin et al. | Mar 2006 | A1 |
20060060216 | Woo | Mar 2006 | A1 |
20060061657 | Rew et al. | Mar 2006 | A1 |
20060064828 | Stein et al. | Mar 2006 | A1 |
20060087273 | Ko et al. | Apr 2006 | A1 |
20060089765 | Pack et al. | Apr 2006 | A1 |
20060100741 | Jung | May 2006 | A1 |
20060119839 | Bertin et al. | Jun 2006 | A1 |
20060143295 | Costa-Requena et al. | Jun 2006 | A1 |
20060146776 | Kim | Jul 2006 | A1 |
20060190133 | Konandreas et al. | Aug 2006 | A1 |
20060190146 | Morse et al. | Aug 2006 | A1 |
20060196003 | Song et al. | Sep 2006 | A1 |
20060220900 | Ceskutti et al. | Oct 2006 | A1 |
20060259194 | Chiu | Nov 2006 | A1 |
20060259494 | Chiu | Nov 2006 | A1 |
20060288519 | Jaworski et al. | Dec 2006 | A1 |
20060293787 | Kanda et al. | Dec 2006 | A1 |
20070006404 | Cheng et al. | Jan 2007 | A1 |
20070017061 | Yan | Jan 2007 | A1 |
20070028574 | Yan | Feb 2007 | A1 |
20070032904 | Kawagoe | Feb 2007 | A1 |
20070042716 | Goodall et al. | Feb 2007 | A1 |
20070043459 | Abbott et al. | Feb 2007 | A1 |
20070061041 | Zweig | Mar 2007 | A1 |
20070114975 | Cohen et al. | May 2007 | A1 |
20070150096 | Yeh et al. | Jun 2007 | A1 |
20070157415 | Lee et al. | Jul 2007 | A1 |
20070157420 | Lee et al. | Jul 2007 | A1 |
20070179670 | Chiappetta et al. | Aug 2007 | A1 |
20070226949 | Hahm et al. | Oct 2007 | A1 |
20070234492 | Svendsen et al. | Oct 2007 | A1 |
20070244610 | Ozick et al. | Oct 2007 | A1 |
20070250212 | Halloran et al. | Oct 2007 | A1 |
20070266508 | Jones et al. | Nov 2007 | A1 |
20080007203 | Cohen et al. | Jan 2008 | A1 |
20080039974 | Sandin et al. | Feb 2008 | A1 |
20080052846 | Kapoor et al. | Mar 2008 | A1 |
20080091304 | Ozick et al. | Apr 2008 | A1 |
20080184518 | Taylor | Aug 2008 | A1 |
20080276407 | Schnittman et al. | Nov 2008 | A1 |
20080281470 | Gilbert et al. | Nov 2008 | A1 |
20080282494 | Won et al. | Nov 2008 | A1 |
20080294288 | Yamauchi | Nov 2008 | A1 |
20080302586 | Yan | Dec 2008 | A1 |
20080307590 | Jones et al. | Dec 2008 | A1 |
20090007366 | Svendsen et al. | Jan 2009 | A1 |
20090038089 | Landry et al. | Feb 2009 | A1 |
20090049640 | Lee et al. | Feb 2009 | A1 |
20090055022 | Casey et al. | Feb 2009 | A1 |
20090102296 | Greene et al. | Apr 2009 | A1 |
20090292393 | Casey et al. | Nov 2009 | A1 |
20100011529 | Won et al. | Jan 2010 | A1 |
20100049365 | Jones et al. | Feb 2010 | A1 |
20100063628 | Landry et al. | Mar 2010 | A1 |
20100107355 | Won et al. | May 2010 | A1 |
20100257690 | Jones et al. | Oct 2010 | A1 |
20100257691 | Jones et al. | Oct 2010 | A1 |
20100263158 | Jones et al. | Oct 2010 | A1 |
20100268384 | Jones et al. | Oct 2010 | A1 |
20100312429 | Jones et al. | Dec 2010 | A1 |
Number | Date | Country |
---|---|---|
2003275566 | Jun 2004 | AU |
2003275566 | Jun 2004 | AU |
2128842 | Dec 1980 | DE |
3317376 | Nov 1984 | DE |
3536907 | Feb 1989 | DE |
3404202 | Dec 1992 | DE |
199311014 | Oct 1993 | DE |
4414683 | Oct 1995 | DE |
4338841 | Aug 1999 | DE |
19849978 | Feb 2001 | DE |
19849978 | Feb 2001 | DE |
10242257 | Apr 2003 | DE |
102004038074.0 | Jun 2005 | DE |
10357636 | Jul 2005 | DE |
10357636 | Jul 2005 | DE |
102004041021 | Aug 2005 | DE |
102004041021 | Aug 2005 | DE |
102005046813 | Apr 2007 | DE |
102005046813 | Apr 2007 | DE |
198803389 | Dec 1988 | DK |
265542 | May 1988 | EP |
281085 | Sep 1988 | EP |
307381 | Jul 1990 | EP |
358628 | May 1991 | EP |
437024 | Jul 1991 | EP |
433697 | Dec 1992 | EP |
479273 | May 1993 | EP |
294101 | Dec 1993 | EP |
554978 | Mar 1994 | EP |
615719 | Sep 1994 | EP |
861629 | Sep 1998 | EP |
792726 | Jun 1999 | EP |
930040 | Oct 1999 | EP |
845237 | Apr 2000 | EP |
1018315 | Jul 2000 | EP |
1172719 | Jan 2002 | EP |
1228734 | Jun 2003 | EP |
1331537 | Jul 2003 | EP |
1380245 | Jan 2004 | EP |
1380245 | Jan 2004 | EP |
1380246 | Jan 2004 | EP |
1380246 | Mar 2005 | EP |
155773 | Jul 2005 | EP |
1553472 | Jul 2005 | EP |
1557730 | Jul 2005 | EP |
1642522 | Apr 2006 | EP |
1642522 | Nov 2007 | EP |
2238196 | Nov 2006 | ES |
2601443 | Nov 1991 | FR |
2828589 | Dec 2003 | FR |
702426 | Jan 1954 | GB |
2128842 | Apr 1986 | GB |
2213047 | Aug 1989 | GB |
2225221 | May 1990 | GB |
2225221 | May 1990 | GB |
2284957 | Jun 1995 | GB |
1167360 | Dec 1995 | GB |
2283838 | Dec 1997 | GB |
2300082 | Sep 1999 | GB |
2404330 | Jul 2005 | GB |
2417354 | Feb 2006 | GB |
53021869 | Feb 1978 | JP |
53110257 | Sep 1978 | JP |
53110257 | Sep 1978 | JP |
943901 | Mar 1979 | JP |
57014726 | Jan 1982 | JP |
57064217 | Apr 1982 | JP |
59005315 | Feb 1984 | JP |
59033511 | Mar 1984 | JP |
59094005 | May 1984 | JP |
59099308 | Jul 1984 | JP |
59112311 | Jul 1984 | JP |
59033511 | Aug 1984 | JP |
59120124 | Aug 1984 | JP |
59131668 | Sep 1984 | JP |
59164973 | Sep 1984 | JP |
59184917 | Oct 1984 | JP |
2283343 | Nov 1984 | JP |
59212924 | Dec 1984 | JP |
59226909 | Dec 1984 | JP |
60089213 | May 1985 | JP |
60089213 | Jun 1985 | JP |
60211510 | Oct 1985 | JP |
60259895 | Dec 1985 | JP |
61023221 | Jan 1986 | JP |
61097712 | May 1986 | JP |
61023221 | Jun 1986 | JP |
62074018 | Apr 1987 | JP |
62070709 | May 1987 | JP |
62120510 | Jul 1987 | JP |
62154008 | Sep 1987 | JP |
62164431 | Oct 1987 | JP |
62263507 | Nov 1987 | JP |
62263508 | Nov 1987 | JP |
62189057 | Dec 1987 | JP |
63079623 | Apr 1988 | JP |
63158032 | Jul 1988 | JP |
63183032 | Jul 1988 | JP |
1162454 | Jun 1989 | JP |
2006312 | Jan 1990 | JP |
2026312 | Jun 1990 | JP |
2283343 | Nov 1990 | JP |
3051023 | Mar 1991 | JP |
3197758 | Aug 1991 | JP |
3201903 | Sep 1991 | JP |
4019586 | Mar 1992 | JP |
4084921 | Mar 1992 | JP |
5023269 | Apr 1993 | JP |
5091604 | Apr 1993 | JP |
5042076 | Jun 1993 | JP |
5046246 | Jun 1993 | JP |
5150827 | Jun 1993 | JP |
5150829 | Jun 1993 | JP |
5046239 | Jul 1993 | JP |
5054620 | Jul 1993 | JP |
5054620 | Jul 1993 | JP |
5040519 | Oct 1993 | JP |
5257527 | Oct 1993 | JP |
5257533 | Oct 1993 | JP |
5285861 | Nov 1993 | JP |
6003251 | Jan 1994 | JP |
06038912 | Feb 1994 | JP |
6026312 | Apr 1994 | JP |
6293095 | Apr 1994 | JP |
6137828 | May 1994 | JP |
6327598 | Nov 1994 | JP |
6105781 | Dec 1994 | JP |
7059702 | Mar 1995 | JP |
7129239 | May 1995 | JP |
7059702 | Jun 1995 | JP |
7222705 | Aug 1995 | JP |
7222705 | Aug 1995 | JP |
7270518 | Oct 1995 | JP |
7281742 | Oct 1995 | JP |
7281752 | Oct 1995 | JP |
7295636 | Nov 1995 | JP |
7311041 | Nov 1995 | JP |
7313417 | Dec 1995 | JP |
7313417 | Dec 1995 | JP |
7319542 | Dec 1995 | JP |
8000393 | Jan 1996 | JP |
8000393 | Jan 1996 | JP |
8016241 | Jan 1996 | JP |
2076776 | Feb 1996 | JP |
8016776 | Feb 1996 | JP |
8063229 | Mar 1996 | JP |
8083125 | Mar 1996 | JP |
8083125 | Mar 1996 | JP |
8089449 | Apr 1996 | JP |
8089451 | Apr 1996 | JP |
2520732 | May 1996 | JP |
8123548 | May 1996 | JP |
8123548 | May 1996 | JP |
8152916 | Jun 1996 | JP |
8152916 | Jun 1996 | JP |
8256960 | Oct 1996 | JP |
8263137 | Oct 1996 | JP |
8263137 | Oct 1996 | JP |
8286741 | Nov 1996 | JP |
8286744 | Nov 1996 | JP |
8322774 | Dec 1996 | JP |
8322774 | Dec 1996 | JP |
8335112 | Dec 1996 | JP |
8335112 | Dec 1996 | JP |
9043901 | Feb 1997 | JP |
9044240 | Feb 1997 | JP |
9047413 | Feb 1997 | JP |
9066855 | Mar 1997 | JP |
9066855 | Mar 1997 | JP |
9145309 | Jun 1997 | JP |
9160644 | Jun 1997 | JP |
9160644 | Jun 1997 | JP |
9179625 | Jul 1997 | JP |
9179625 | Jul 1997 | JP |
9179685 | Jul 1997 | JP |
9185410 | Jul 1997 | JP |
9192069 | Jul 1997 | JP |
9204223 | Aug 1997 | JP |
9206258 | Aug 1997 | JP |
9206258 | Aug 1997 | JP |
9233712 | Sep 1997 | JP |
09251318 | Sep 1997 | JP |
9251318 | Sep 1997 | JP |
9265319 | Oct 1997 | JP |
9265319 | Oct 1997 | JP |
9269807 | Oct 1997 | JP |
9269807 | Oct 1997 | JP |
9269810 | Oct 1997 | JP |
9269810 | Oct 1997 | JP |
02555236 | Nov 1997 | JP |
9319431 | Dec 1997 | JP |
9319431 | Dec 1997 | JP |
9319432 | Dec 1997 | JP |
9319432 | Dec 1997 | JP |
9319434 | Dec 1997 | JP |
9319434 | Dec 1997 | JP |
9325812 | Dec 1997 | JP |
9325812 | Dec 1997 | JP |
10055215 | Feb 1998 | JP |
10055215 | Feb 1998 | JP |
10117973 | May 1998 | JP |
10117973 | May 1998 | JP |
10117973 | May 1998 | JP |
10118963 | May 1998 | JP |
10118963 | May 1998 | JP |
10177414 | Jun 1998 | JP |
10214114 | Aug 1998 | JP |
10214114 | Aug 1998 | JP |
10228316 | Aug 1998 | JP |
10240342 | Sep 1998 | JP |
10260727 | Sep 1998 | JP |
63241610 | Oct 1998 | JP |
10295595 | Nov 1998 | JP |
10295595 | Nov 1998 | JP |
11015941 | Jan 1999 | JP |
11015941 | Jan 1999 | JP |
11065655 | Mar 1999 | JP |
11085269 | Mar 1999 | JP |
11102219 | Apr 1999 | JP |
11102220 | Apr 1999 | JP |
11102220 | Apr 1999 | JP |
11162454 | Jun 1999 | JP |
11174145 | Jul 1999 | JP |
11174145 | Jul 1999 | JP |
11175149 | Jul 1999 | JP |
11175149 | Jul 1999 | JP |
11178764 | Jul 1999 | JP |
11178765 | Jul 1999 | JP |
11212642 | Aug 1999 | JP |
11212642 | Aug 1999 | JP |
11213157 | Aug 1999 | JP |
11213157 | Aug 1999 | JP |
11508810 | Aug 1999 | JP |
11246806 | Sep 1999 | JP |
11510935 | Sep 1999 | JP |
11282532 | Oct 1999 | JP |
11282533 | Oct 1999 | JP |
11295412 | Oct 1999 | JP |
11295412 | Oct 1999 | JP |
11346964 | Dec 1999 | JP |
2000047728 | Feb 2000 | JP |
2000056006 | Feb 2000 | JP |
2000056006 | Feb 2000 | JP |
2000056831 | Feb 2000 | JP |
2000056831 | Feb 2000 | JP |
2000066722 | Mar 2000 | JP |
2000066722 | Mar 2000 | JP |
2000075925 | Mar 2000 | JP |
2000075925 | Mar 2000 | JP |
10240343 | May 2000 | JP |
2000275321 | Oct 2000 | JP |
2000353014 | Dec 2000 | JP |
2000353014 | Dec 2000 | JP |
200122443 | Jan 2001 | JP |
2001022443 | Jan 2001 | JP |
2001067588 | Mar 2001 | JP |
2001087182 | Apr 2001 | JP |
2001087182 | Apr 2001 | JP |
2001121455 | May 2001 | JP |
2001125641 | May 2001 | JP |
2001216482 | Aug 2001 | JP |
2001258807 | Sep 2001 | JP |
2001265437 | Sep 2001 | JP |
2001265437 | Sep 2001 | JP |
2001275908 | Oct 2001 | JP |
2001289939 | Oct 2001 | JP |
2001306170 | Nov 2001 | JP |
2001320781 | Nov 2001 | JP |
2001525567 | Dec 2001 | JP |
2002078650 | Mar 2002 | JP |
2002204768 | Jul 2002 | JP |
2002204769 | Jul 2002 | JP |
2002247510 | Aug 2002 | JP |
2002532178 | Oct 2002 | JP |
2002323925 | Nov 2002 | JP |
2002333920 | Nov 2002 | JP |
2002333920 | Nov 2002 | JP |
03356170 | Dec 2002 | JP |
2002355206 | Dec 2002 | JP |
2002360471 | Dec 2002 | JP |
2002360479 | Dec 2002 | JP |
2002360482 | Dec 2002 | JP |
2002366227 | Dec 2002 | JP |
2002369778 | Dec 2002 | JP |
2002369778 | Dec 2002 | JP |
2003005296 | Jan 2003 | JP |
2003010076 | Jan 2003 | JP |
2003010076 | Jan 2003 | JP |
2003010088 | Jan 2003 | JP |
2003010088 | Jan 2003 | JP |
2003015740 | Jan 2003 | JP |
2003015740 | Jan 2003 | JP |
2003028528 | Jan 2003 | JP |
03375843 | Feb 2003 | JP |
2003036116 | Feb 2003 | JP |
2003047579 | Feb 2003 | JP |
2003052596 | Feb 2003 | JP |
2003505127 | Feb 2003 | JP |
2003061882 | Mar 2003 | JP |
2003084994 | Mar 2003 | JP |
2003167628 | Jun 2003 | JP |
2003167628 | Jun 2003 | JP |
2003180586 | Jul 2003 | JP |
2003180587 | Jul 2003 | JP |
2003186539 | Jul 2003 | JP |
2003190064 | Jul 2003 | JP |
2003190064 | Jul 2003 | JP |
2003241836 | Aug 2003 | JP |
2003262520 | Sep 2003 | JP |
2003262520 | Sep 2003 | JP |
2003285288 | Oct 2003 | JP |
2003304992 | Oct 2003 | JP |
2003304992 | Oct 2003 | JP |
2003310489 | Nov 2003 | JP |
2003310509 | Nov 2003 | JP |
2003310509 | Nov 2003 | JP |
2003330543 | Nov 2003 | JP |
2004123040 | Apr 2004 | JP |
2004123040 | Apr 2004 | JP |
2004148021 | May 2004 | JP |
2004148021 | May 2004 | JP |
2004160102 | Jun 2004 | JP |
2004160102 | Jun 2004 | JP |
2004166968 | Jun 2004 | JP |
2004174228 | Jun 2004 | JP |
2004174228 | Jun 2004 | JP |
2004198330 | Jul 2004 | JP |
2004219185 | Aug 2004 | JP |
2005352707 | Feb 2005 | JP |
2005118354 | May 2005 | JP |
2005135400 | May 2005 | JP |
2005135400 | May 2005 | JP |
2005211360 | Aug 2005 | JP |
2005224265 | Aug 2005 | JP |
2005230032 | Sep 2005 | JP |
2005245916 | Sep 2005 | JP |
2005245916 | Sep 2005 | JP |
2005296511 | Oct 2005 | JP |
2005346700 | Dec 2005 | JP |
2005352707 | Dec 2005 | JP |
2006043071 | Feb 2006 | JP |
2006043071 | Feb 2006 | JP |
2006155274 | Jun 2006 | JP |
2006155274 | Jun 2006 | JP |
2006164223 | Jun 2006 | JP |
2006227673 | Aug 2006 | JP |
2006247467 | Sep 2006 | JP |
2006247467 | Sep 2006 | JP |
2006260161 | Sep 2006 | JP |
2006260161 | Sep 2006 | JP |
2006293662 | Oct 2006 | JP |
2006293662 | Oct 2006 | JP |
2006296697 | Nov 2006 | JP |
2006296697 | Nov 2006 | JP |
2007034866 | Feb 2007 | JP |
2007034886 | Feb 2007 | JP |
2007213180 | Aug 2007 | JP |
2007213180 | Aug 2007 | JP |
04074285 | Apr 2008 | JP |
2009015611 | Jan 2009 | JP |
2009015611 | Jan 2009 | JP |
2010198552 | Sep 2010 | JP |
2010198552 | Sep 2010 | JP |
WO9526512 | Oct 1995 | WO |
WO9530887 | Nov 1995 | WO |
WO9617258 | Feb 1997 | WO |
WO9715224 | May 1997 | WO |
WO9740734 | Nov 1997 | WO |
WO9741451 | Nov 1997 | WO |
WO9853456 | Nov 1998 | WO |
W9905580 | Feb 1999 | WO |
WO9916078 | Apr 1999 | WO |
WO9928800 | Jun 1999 | WO |
WO9938056 | Jul 1999 | WO |
WO9938237 | Jul 1999 | WO |
WO9943250 | Sep 1999 | WO |
WO9959042 | Nov 1999 | WO |
WO0004430 | Apr 2000 | WO |
WO0036962 | Jun 2000 | WO |
WO0038026 | Jun 2000 | WO |
WO0038028 | Jun 2000 | WO |
WO0038028 | Jun 2000 | WO |
WO0038029 | Jun 2000 | WO |
WO0078410 | Dec 2000 | WO |
WO0106904 | Feb 2001 | WO |
WO0106905 | Jun 2001 | WO |
WO0180703 | Nov 2001 | WO |
WO0191623 | Dec 2001 | WO |
WO0239864 | May 2002 | WO |
WO0239868 | May 2002 | WO |
WO02058527 | Aug 2002 | WO |
WO02062194 | Aug 2002 | WO |
WO 02071175 | Sep 2002 | WO |
WO02067744 | Sep 2002 | WO |
WO02067745 | Sep 2002 | WO |
WO02067752 | Sep 2002 | WO |
WO02069774 | Sep 2002 | WO |
WO02069775 | Sep 2002 | WO |
WO02071175 | Sep 2002 | WO |
WO02075351 | Sep 2002 | WO |
WO02075356 | Sep 2002 | WO |
WO02075469 | Sep 2002 | WO |
WO02075470 | Sep 2002 | WO |
WO02081074 | Oct 2002 | WO |
WO03015220 | Feb 2003 | WO |
WO03024292 | Mar 2003 | WO |
WO02069775 | May 2003 | WO |
WO03040546 | May 2003 | WO |
WO03040845 | May 2003 | WO |
WO03040846 | May 2003 | WO |
WO03062850 | Jul 2003 | WO |
WO03062852 | Jul 2003 | WO |
WO02101477 | Oct 2003 | WO |
WO03026474 | Nov 2003 | WO |
WO2004004533 | Jan 2004 | WO |
WO2004004534 | Jan 2004 | WO |
WO2004005956 | Jan 2004 | WO |
WO2004006034 | Jan 2004 | WO |
WO2004025947 | May 2004 | WO |
WO2004043215 | May 2004 | WO |
WO2004043215 | May 2004 | WO |
WO2004058028 | Jul 2004 | WO |
WO2004059409 | Jul 2004 | WO |
WO2004058028 | Jul 2004 | WO |
WO2004058028 | Jul 2004 | WO |
WO2004059409 | Jul 2004 | WO |
WO2005006935 | Jan 2005 | WO |
WO2005006935 | Jan 2005 | WO |
WO2005036292 | Apr 2005 | WO |
WO2005036292 | Apr 2005 | WO |
WO2005055795 | Jun 2005 | WO |
WO2005055796 | Jun 2005 | WO |
WO2005055796 | Jun 2005 | WO |
WO2005076545 | Aug 2005 | WO |
WO2005077243 | Aug 2005 | WO |
WO2005077244 | Aug 2005 | WO |
WO2005081074 | Sep 2005 | WO |
WO2005082223 | Sep 2005 | WO |
WO2005082223 | Sep 2005 | WO |
WO2005083541 | Sep 2005 | WO |
WO2005098475 | Oct 2005 | WO |
WO2005098476 | Oct 2005 | WO |
WO2006046400 | May 2006 | WO |
WO2006061133 | Jun 2006 | WO |
WO2006061133 | Jun 2006 | WO |
WO2006068403 | Jun 2006 | WO |
WO2006073248 | Jul 2006 | WO |
WO2006073248 | Jul 2006 | WO |
WO2007036490 | Apr 2007 | WO |
WO2007036490 | May 2007 | WO |
WO2007065033 | Jun 2007 | WO |
WO2007137234 | Nov 2007 | WO |
Entry |
---|
OnRobo “Samsung Unveils Its Multifunction Robot Vacuum”, www.onrobo.com/enews/0210/samsung—vacuum.shtml, 3 pages, Mar. 18, 2005. |
Pages et al. “Optimizing Plane-to-Plane Positioning Tasks by Image-Based Visual Servoing and Structured Light”, IEEE Transactions on Robotics. vol. 22, No. 5. pp. 1000-1010, Oct. 2006. |
Pages et al. “A camera-projector system for robot positioning by visual servoing”, Proceedings of the 2006 Conference on Computer Vision and Recognition Workshop (CVPRW06), 8 pages, Jun. 17-22, 2006. |
Pages, et al. “Robust decoupled visual servoing based on structured light”, 2005 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 2676-2681, 2005. |
Park et al. “A Neural Network Based Real-Time Robot Tracking Controller Using Position Sensitive Detectors,” IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on Neutral Networks. Orlando, Florida pp. 2754-2758, Jun. 27-Jul. 2, 1994. |
Park, et al. “Dynamic Visual Servo Control of Robot Manipulators using Neutral Networks”, The Korean Institute Telematics and Electronics, vol. 29-B, No. 10, pp. 771-779, Oct. 1992. |
Paromtchik “Toward Optical Guidance of Mobile Robots”. |
Paromtchik, et al. “Optical Guidance System for Multiple mobile Robots”, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation, vol. 3, pp. 2935-2940 (May 21-26, 2001). |
Penna, et al. “Models for Map Building and Navigation”, IEEE Transactions on Systems. Man. And Cybernetics. vol. 23 No. 5, pp. 1276-1301. Sep./Oct. 1993. |
Pirjanian “Reliable Reaction”, Proceedings of the 1996 IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems, pp. 158-165, 1996. |
Pirjanian “Challenges for Standards for consumer Robotics”, IEEE Workshop on Advanced Robotics and its Social impacts, pp. 260-264, Jun. 12-15, 2005. |
Pirjanian et al. “Distributed Control for a Modular, Reconfigurable Cliff Robot”, Proceedings of the 2002 IEEE International Conference on Robotics and Automation, Washington D.C. pp. 4083-4088, May 2002. |
Pirjanian et al. “Representation and Execution of Plan Sequences for Multi-Agent Systems”, Proceedings of the 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems, Maui, Hawaii, pp. 2117-2123, Oct. 29-Nov. 3, 2001. |
Pirjanian et al. “Multi-Robot Target Acquisition using Multiple Objective Behavior Coordination”, Proceedings of the 2000 IEEE International Conference on Robotics & Automation, San Francisco, CA, pp. 2696-2702, Apr. 2000. |
Pirjanian et al. “A decision-theoretic approach to fuzzy behavior coordination”, 1999 IEEE International Symposium on Computational Intelligence in Robotics and Automation, 1999. CIRA '99., Monterey, CA, pp. 101-106. Nov. 8-9, 1999. |
Pirjanian et al. “Improving Task Reliability by Fusion of Redundant Homogeneous Modules Using Voting Schemes”, Proceedings of the 1997 IEEE International Conference on Robotics and Automation, Albuquerque, NM, pp. 425-430. Apr. 1997. |
Prassler et al., “A Short History of Cleaning Robots”, Autonomous Robots 9, 211-226, 2000, 16 pages. |
Radio Frequency Identification: Tracking ISS Consumables, Author Unknown, 41 pages. |
Remazeilles, et al. “Image based robot navigation in 3D environments”, Proc. of SPIE, vol. 6052, pp. 1-14, Dec. 6, 2005. |
Rives, et al. “Visual servoing based on ellipse features”, SPIE vol. 2056 Intelligent Robots and Computer Vision pp. 356-367, 1993. |
Robotics World Jan. 2001: “A Clean Sweep” (Jan. 2001). |
Ronnback “On Methods for Assistive Mobile Robots”, http://www.openthesis.org/documents/methods-assistive-mobile-robots-595019.html, 218 pages, Jan. 1, 2006. |
Roth-Tabak, et al. “Environment Model for mobile Robots Indoor Navigation”, SPIE vol. 1388 Mobile Robots pp. 453-463, 1990. |
Sadath M Malik et al. “Virtual Prototyping for Conceptual Design of a Tracked Mobile Robot”. Electrical and Computer Engineering, Canadian Conference on, IEEE, PI. May 1, 2006, pp. 2349-2352. |
Sahin, et al. “Development of a Visual Object Localization Module for Mobile Robots”, 1999 Third European Workshop on Advanced Mobile Robots, (Eurobot '99), pp. 65-72, 1999. |
Salomon, et al. “Low-Cost Optical Indoor Localization system for Mobile Objects without Image Processing”, IEEE Conference on Emerging Technologies and Factory Automation, 2006. (ETFA '06), pp. 629-632, Sep. 20-22, 2006. |
Sato “Range Imaging Based on Moving Pattern Light and Spatio-Temporal Matched Filter”, Proceedings International Conference on Image Processing, vol. 1., Lausanne, Switzerland. pp. 33-36, Sep. 16-19, 1996. |
Schenker, et al. “Lighweight rovers for Mars science exploration and sample return”, Intelligent Robots and Computer Vision XVI, SPIE Proc. 3208, pp. 24-36, 1997. |
Sebastian Thrun, Learning Occupancy Grid Maps With Forward Sensor Models, School of Computer Science, Carnegie Mellon University, pp. 1-28. |
Shimoga et al, “Touch and Force Reflection for Telepresence Surgery”, Engineering in Medicine and Biology Society, 1994. Engineering Advances: New Opportunities for Biomedical Engineers. Proceedings of the 16th Annual International Conference of the IEEE. Baltimore, MD, pp. 1049-1050, 1994. |
Sim, et al “Learning Visual Landmarks for Pose Estimation”, IEEE International Conference on Robotics and Automation, vol. 3, Detroit, MI, pp. 1972-1978, May 10-15, 1999. |
Sobh et al. “Case Studies in Web-Controlled Devices and Remote Manipulation”, Automation Congress, 2002 Proceedings of the 5th Biannual World, pp. 435-440, Dec. 10, 2002. |
Stella, et al. “Self-Location for Indoor Navigation of Autonomous Vehicles”, Part of the SPIE conference on Enhanced and Synthetic Vision SPIE vol. 3364 pp. 298-302, 1998. |
Summet “Tracking Locations of Moving Hand-held Displays Using Projected Light”, Pervasive 2005, LNCS 3468 pp. 37-46 (2005). |
Svedman et al. “Structure from Stereo Vision using Unsynchronized Cameras for Simultaneous Localization and Mapping”, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2993-2998, 2005. |
Takio et al. “Real-Time Position and Pose Tracking Method of Moving Object Using Visual Servo System”, 47th IEEE International Symposium on Circuits and Systems, pp. 167-170, 2004. |
Teller “Pervasive pose awareness for people, Objects and Robots”, http://www.ai.mit.edu/lab/dangerous-ideas/Spring2003/teller-pose.pdf, 6 pages, Apr. 30, 2003. |
Terada et al. “An Acquisition of the Relation between Vision and Action using Self-Organizing Map and Reinforcement Learning”, 1998 Second International Conference on Knowledge-Based Intelligent Electronic Systems, Adelaide, Australiam pp. 429-434, Apr. 21-23, 1998. |
The Sharper Image “e-Vac Robotic Vacuum, S1727 Instructions”www.sharperimage.com, 18 pages. |
The Sharper Image “Robotic Vacuum Cleaner—Blue” www.Sharperimage.com, 2 pages, Mar. 18, 2005. |
The Sharper Image “E Vac Robotic Vacuum”, www.sharperimage.com/us/en/templates/products/pipmorework1printable.jhtml, 2 pages, Mar. 18, 2005. |
TheRobotStore.com “Friendly Robotics Robotic Vacuum RV400—The Robot Store”, www.therobotstore.com/s.nl/sc.9/category.-109/it.A/id.43/.f, 1 page, Apr. 20, 2005. |
TotalVac.com RC3000 RoboCleaner website Mar. 18, 2005. |
Trebi-Ollennu et al. “Mars Rover Pair Cooperatively Transporting a Long Payload”, Proceedings of the 2002 IEEE International Conference on Robotics & Automation, Washington, D.C. pp. 3136-3141, May 2002. |
Tribelhorn et al., “Evaluating the Roomba: A low-cost, ubiquitous platform for robotics research and education,” 2007, IEEE, p. 1393-1399. |
Tse et al “Design of a Navigation System for a Household Mobile Robot Using Neural Networks”, Department of Manufacturing Engg. & Engg. Management, City University of Hong Kong, pp. 2151-2156, 1998. |
UAMA (Asia) Industrial Co., Ltd. “RobotFamily”, 2005. |
Watanabe et al. “Position Estimation of Mobile Robots With Internal and External Sensors Using Uncertainty Evolution Technique”, 1990 IEEE International Conference on Robotics and Automation, Cincinnati, OH, pp. 2011-2016, May 13-18, 1990. |
Watts “Robot, boldly goes where no man can”, The Times—pp. 20, Jan. 1985. |
Wijk et al. “Triangulation-Based Fusion of Sonar Data with Application in Robot Pose Tracking”, IEEE Transactions on Robotics and Automation, vol. 16, No. 6, pp. 740-752, Dec. 2000. |
U.S. Appl. No. 60/605,066 as provided to WIPO in PCT/US2005/030422, corresponding to U.S. Appl. No. 11/574,290, U.S. publication 2008/0184518, filing date Aug. 27, 2004. |
U.S. Appl. No. 60/605,181 as provided to WIPO in PCT/US2005/030422, corresponding to U.S. Appl. No. 11/574,290, U.S. publication 2008/0184518, filing date Aug. 27, 2004. |
Derek Kurth, “Range-Only Robot Localization and SLAM with Radio”, http://www.ri.cmu.edu/pub—files/pub4/kurth—derek—2004—1/kurth—derek—2004—1.pdf. 60 pages, May 2004, accessed Jul. 27, 2012. |
Florbot GE Plastics, 1989-1990, 2 pages, available at http://www.fuseid.com/, accessed Sep. 27, 2012. |
Gregg et al., “Autonomous Lawn Care Applications,” 2006 Florida Conference on Recent Advances in Robotics, Miami, Florida, May 25-26, 2006, Florida International University, 5 pages. |
Hitachi ‘Feature’, http://kadenfan.hitachi.co.jp/robot/feature/feature.html, 1 page, Nov. 19, 2008. |
Hitachi, http://www.hitachi.co.jp/New/cnews/hi—030529—hi—030529.pdf, 8 pages, May 29, 2003. |
Home Robot—UBOT; Microbotusa.com, retrieved from the WWW at www.microrobotusa.com, accessed Dec. 2, 2008. |
King and Weiman, “Helpmate™ Autonomous Mobile Robots Navigation Systems,” SPIE vol. 1388 Mobile Robots, pp. 190-198 (1990). |
Miwako Doi “Using the symbiosis of human and robots from approaching Research and Development Center,” Toshiba Corporation, 16 pages, available at http://warp.ndl.go.jp/info:ndljp/pid/258151/www.soumu.go.jp/joho—tsusin/policyreports/chousa/netrobot/pdf/030214—1—33—a.pdf, Feb. 26, 2003. |
Written Opinion of the International Searching Authority, PCT/US2004/001504, Aug. 20, 2012, 9 pages. |
Borges et al. “Optimal Mobile Robot Pose Estimation Using Geometrical Maps”, IEEE Transactions on Robotics and Automation, vol. 18, No. 1, pp. 87-94, Feb. 2002. |
Braunstingl et al. “Fuzzy Logic Wall Following of a Mobile Robot Based on the Concept of General Perception” ICAR '95, 7th International Conference on Advanced Robotics, Sant Feliu De Guixols, Spain, pp. 367-376, Sep. 1995. |
Bulusu, et al. “Self Configuring Localization systems: Design and Experimental Evaluation”, ACM Transactions on Embedded Computing Systems vol. 3 No. 1 pp. 24-60, 2003. |
Caccia, et al. “Bottom-Following for Remotely Operated Vehicies”, 5th IFAC conference, Alaborg, Denmark, pp. 245-250 Aug. 1, 2000. |
Chae, et al. “StarLITE: A new artificial landmark for the navigation of mobile robots”, http://www.irc.atr.jp/jk-nrs2005/pdf/Starlite.pdf, 4 pages, 2005. |
Chamberlin et al. “Team 1: Robot Locator Beacon System” NASA Goddard FC, Design Proposal, 15 pages, Feb. 17, 2006. |
Champy “Physical management of IT assets in Data Centers using RFID technologies”, RFID 2005 University, Oct. 12-14, 2005. |
Chiri “Joystick Control for Tiny OS Robot”, http://www.eecs.berkeley.edu/Programs/ugrad/superb/papers2002/chiri.pdf. 12 pages, Aug. 8, 2002. |
Christensen et al. “Theoretical Methods for Planning and Control in Mobile Robotics” 1997 First International Conference on Knowledge-Based Intelligent Electronic Systems, Adelaide, Australia, pp. 81-86, May 21-27, 1997. |
Andersen et al., “Landmark based navigation strategies”, SPIE Conference on Mobile Robots XIII, SPIE vol. 3525, pp. |
Clerentin, et al. “A localization method based on two omnidirectional perception systems cooperation” Proc of IEEE International Conference on Robotics & Automation, San Francisco, CA vol. 2, pp. 1219-1224, Apr. 2000. |
Corke “High Performance Visual serving for robots end-point control”. SPIE vol. 2056 Intelligent robots and computer vision 1993. |
Cozman et al. “Robot Localization using a Computer Vision Sextant”, IEEE International Midwest Conference on Robotics and Automation, pp. 106-111, 1995. |
D'Orazio, et al. “Model based Vision System for mobile robot position estimation”, SPIE vol. 2058 Mobile Robots VIII, pp. 38-49, 1992. |
De Bakker, et al. “Smart PSD—array for sheet of light range imaging”, Proc. Of SPIE vol. 3965. pp. 1-12, May 15, 2000. |
Desaulniers, et al. “An Efficient Algorithm to find a shortest path for a car-like Robot”, IEEE Transactions on robotics and Automation vol. 11 No. 6, pp. 819-828, Dec. 1995. |
Dorfmüller-Ulhaas “Optical Tracking From User Motion to 3D Interaction”, http://www.cg.tuwien.ac.at/research/publications/2002/Dorfmueller-Ulhaas-thesis, 182 pages, 2002. |
Dorsch, et al. “Laser Triangulation: Fundamental uncertainty in distance measurement”, Applied Optics. vol. 33 No. 7, pp. 1306-1314, Mar. 1, 1994. |
Dudek, et al. “Localization A Robot with Minimum Travel” Proceedings of the sixth annual ACM-SIAM symposium on Discrete algorithms, vol. 27 No. 2 pp. 583-604, Apr. 1998. |
Dulimarta, et al. “Mobile Robot Localization in Indoor Environment”, Pattern Recognition, vol. 30, No. 1, pp. 99-111, 1997. |
EBay “Roomba Timer→Timed Cleaning—Floorvac Robotic Vacuum”, Cgi.ebay.com/ws/eBay|SAP|.dll?viewitem&category=43526&item=4375198387&rd=1. 5 pages, Apr. 20, 2005. |
Electrolux “Welcome to the Electrolux trilobite” www.electroluxusa.com/node57.asp?currentURL=node142.asp%3F, 2 pages, Mar. 18, 2005. |
Eren, et al. “Accuracy in position estimation of mobile robots based on coded infrared signal transmission”, Proceedings: Integrating Intelligent Instrumentation and Control, Instrumentation and Measurement Technology Conference, 1995. IMTC/95, pp. 548-551, 1995. |
Eren, et al. “Operation of Mobile Robots in a Structured Infrared Environment”, Proceedings. ‘Sensing, Processing, Networking’, IEEE Instrumentation and Measurement Technology Conference, 1997 (IMTC/97), Ottawa, Canada vol. 1, pp. 20-25, May 19-21, 1997. |
Barker, “Navigation by the Stars—Ben Barker 4th Year Project” Power point pp. 1-20. |
Becker, et al. “Reliable Navigation Using Landmarks ” IEEE International Conference on Robotics and Automation, 0-7803-1965-6, pp. 401-406, 1995. |
Benayad-Cherif, et al., “Mobile Robot Navigation Sensors” SPIE vol. 1831 Mobile Robots, VII, pp. 378-387, 1992. |
Facchinetti, Claudio et al. “Using and Learning Vision-Based Self-Positioning for Autonomous Robot Navigation”, ICARCV '94, vol. 3 pp. 1694-1698, 1994. |
Betke. et al., “Mobile Robot localization using Landmarks” Proceedings of the IEEE/RSJ/GI International Conference on Intelligent Robots and Systems '94 “Advanced Robotic Systems and the Real World” (IROS '94), Vol. |
Facchinetti, Claudio et al, “Self-Positioning Robot Navigation Using Ceiling Images Sequences”, ACCV '95, 5 pages, Dec. 5-8, 1995. |
Fairfield, Nathaniel et al. “Mobile Robot Localization with Sparse Landmarks”, SPIE vol. 4573 pp. 148-155, 2002. |
Favre-Bulle, Bernard “Efficient tracking of 3D—Robot Position by Dynamic Triangulation”, IEEE Instrumentation and Measurement Technology Conference IMTC 98 Session on Instrumentation and Measurement in Robotics, vol. 1, pp. 446-449, May 18-21, 1998. |
Fayman “Exploiting Process Integration and Composition in the context of Active Vision”, IEEE Transactions on Systems, Man, and Cybernetics—Part C: Application and reviews, vol. 29 No. 1, pp. 73-86, Feb. 1999. |
Florbot GE Plastics Image (1989-1990). |
Franz, et al. “Biomimetric robot navigation”, Robotics and Autonomous Systems vol. 30 pp. 133-153, 2000. |
Friendly Robotics “Friendly Robotics—Friendly Vac, Robotic Vacuum Cleaner”, www.friendlyrobotics.com/vac.htm. 5 pages Apr. 20, 2005. |
Fuentes, et al. “Mobile Robotics 1994”, University of Rochester. Computer Science Department, TR 588, 44 pages, Dec. 7, 1994. |
Bison, P et al., “Using a structured beacon for cooperative position estimation” Robotics and Autonomous Systems vol. 29, No. 1, pp. 33-40, Oct. 1999. |
Fukuda, et al. “Navigation System based on Ceiling Landmark Recognition for Autonomous mobile robot”. 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems 95. ‘Human Robot Interaction and Cooperative Robots’, Pittsburgh, PA, pp. 1466/1471, Aug. 5-9, 1995. |
Gionis “A hand-held optical u scanner for environmental Modeling and Virtual Reality”, Virtual Reality World, 16 pages 1996. |
Goncalves et al. “A Visual Front-End for Simultaneous Localization and Mapping”, Proceedings of the 2005 IEEE International Conference on Robotics and Automation, Barcelona, Spain, pp. 44-49, Apr. 2005. |
Gregg et al. “Autonomous Lawn Care Applications”, 2006 Florida Conference on Recent Advances in Robotics, FCRAR 2006, pp. 1-5, May 25-26, 2006. |
Hamamatsu “SI PIN Diode S5980, S5981 S5870—Multi-element photodiodes for surface mounting,” Hamatsu Photonics, 2 pages Apr. 2004. |
Hammacher Schlemmer “Electrolux Trilobite Robotic Vacuum” www.hammacher.com/publish/71579.asp?promo=xsells, 3 pages, Mar. 18, 2005. |
Haralick et al. “Pose Estimation from Corresponding Point Data”, IEEE Transactions on systems, Man, and Cybernetics, vol. 19, No. 6, pp. 1426-1446, Nov. 1989. |
Hausler “About the Scaling Behaviour of Optical Range Sensors”, Fringe '97, Proceedings of the 3rd International Workshop on Automatic Processing of Fringe Patterns, Bremen, Germany, pp. 147-155, Sep. 15-17, 1997. |
Blaasvaer, et al. “AMOR—An Autonomous Mobile Robot Navigation System”, Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, pp. 2266-2271, 1994. |
Hoag, et al. “Navigation and Guidance in interstellar space”, ACTA Astronautica vol. 2, pp. 513-533, Feb. 14, 1975. |
Huntsberger et al. “CAMPOUT: A Control Architecture for Tightly Coupled Coordination of Multirobot Systems for Planetary Surface Exploration”, IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, vol. 33, No. 5, pp. 550-559, Sep. 2003. |
Iirobotics.com “Samsung Unveils Its Multifunction Robot Vacuum”, www.iirobotics.com/webpages/hotstuff.php?ubre=111, 3 pages, Mar. 18, 2005. |
Jarosiewicz et al. “Final Report—Lucid”, University of Florida, Deprtmetn of Electrical and Computer Engineering, EEL 5666—Intelligent Machine Design Laboratory, 50 pages, Aug. 4, 1999. |
Jensfelt, et al. “Active Global Localization for a mobile robot using multiple hypothesis tracking”, IEEE Transactions on Robots and Automation vol. 17. No. 5, pp. 748-760, Oct. 2001. |
Jeong, et al. “An intelligent map-building system for indoor mobile robot using low cost photo sensors”, SPIE vol. 6042 6 pages, 2005. |
Kahney, “Robot Vacs are in the House,” www.wired.com/news/technology/o,1282,59237,00.html, 6 pages, Jun. 18, 2003. |
Karcher “Product Manual Download Karch”, www.karcher.com, 17 pages, 2004. |
Karcher “Karcher RoboCleaner RC 3000”, www.robocleaner.de/english/screen3.html, 4 pages, Dec. 12, 2003. |
Karcher USA “RC 3000 Robotics cleaner”, www.karcher-usa.com, 3 pages, Mar. 18, 2005. |
Karlsson et al., The vSLAM Algorithm for Robust Localization and Mapping, Proceedings of the 2005 IEEE International Conference on Robotics and Automation, Barcelona, Spain, pp. 24-29, Apr. 2005. |
Karisson, et al Core Technologies for service Robotics, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2004), vol. 3, pp. 2979-2984, Sep. 28-Oct. 2, 2004. |
King “Heplmate-TM—Autonomous mobile Robots Navigation Systems”, SPIE vol. 1388 Mobile Robots pp. 190-198, 1990. |
Kleinberg, The Localization Problem for Mobile Robots, Laboratory for Computer Science, Massachusetts Institute of Technology, 1994 IEEE, pp. 521-531, 1994. |
Knight, et al., “Localization and Identification of Visual Landmarks”, Journal of Computing Sciences in Colleges, vol. 16 Issue 4, 2001, pp. 312-313, May 2001. |
Kolodko et al. “Experimental System for Real-Time Motion Estimation”, Proceedings of the 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003), pp. 981-986, 2003. |
Komoriya et al., Planning of Landmark Measurement for the Navigation of a Mobile Robot, Proceedings of the 1992 IEEE/RSJ International Cofnerence on Intelligent Robots and Systems, Raleigh, NC pp. 1476-1481, Jul. 7-10, 1992. |
Koolatron “KOOLVAC—Owner's Manual”, 13 pages. |
Krotov, et al. “Digital Sextant”, Downloaded from the internet at: http://www.cs.cmu.edu/˜epk/, 1 page, 1995. |
Krupa et al. “Autonomous 3-D Positioning of Surgical Instruments in Robotized Laparoscopic Surgery Using Visual Servoing”, IEEE Transactions on Robotics and Automation, vol. 19, No. 5, pp. 842-853, Oct. 5, 2003. |
Kuhl, et al. “Self Localization in Environments using Visual Angles”, VRCAI '04 Proceedings of the 2004 ACM SIGGRAPH international conference on Virtual Reality continuum and its applications in industry, pp. 472-475, 2004. |
Kurth, “Range-Only Robot Localization and SLAM with Radio”, http://www.ri.cmu.edu/pub—files/pub4/kurth—derek—2004—1/kurth—derek—2004—1.pdf, 60 pages, May 2004. |
Lambrinos, et al. “A mobile robot employing insect strategies for navigation”, http://www8.cs.umu.se/kurser/TDBD17/VT04/dl/Assignment%20Papers/lambrinos-RAS-2000.pdf, 38 pages, Feb. 19, 1999. |
Lang et al. “Visual Measurement of Orientation Using Ceiling Features”, 1994 IEEE, pp. 552-555, 1994. |
Lapin, “Adaptive position estimation for an automated guided vehicle”, SPIE vol. 1831 Mobile Robots VII, pp. 82-94, 1992. |
LaValle et al. “Robot Motion Planning in a Changing, Partially Predictable Environment”, 1994 IEEE International Symposium on Intelligent Control, Columbus, OH, pp. 261-266, Aug. 16-18, 1994. |
Lee, et al. “Localization of a Mobile Robot Using the Image of a Moving Object”, IEEE Transaction on Industrial Electronics, vol. 50, No. 3 pp. 612-619, Jun. 2003. |
Lee, et al. “Development of Indoor Navigation system for Humanoid Robot Using Multi-sensors Integration”, ION NTM, San Diego, CA pp. 798-805, Jan. 22-24, 2007. |
Leonard, et al. “Mobile Robot Localization by tracking Geometric Beacons”, IEEE Transactions on Robotics and Automation, vol. 7, No. 3 pp. 376-382, Jun. 1991. |
Lie et al. “Robost Statistical Methods for Securing Wireless Localization in Sensor Networks”, Wireless Information Network Laboratory, Rutgers University. |
Li et al. “Making a Local Map of Indoor Environments by Swiveling a Camera and a Sonar”, Proceedings of the 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 954-959, 1999. |
Lin, et al., “Mobile Robot Navigation Using Artificial Landmarks”, Journal of robotics System 14(2). pp. 93-106, 1997. |
Linde “Dissertation, “On Aspects of Indoor Localization”” https://eldorado.tu-dortmund.de/handle/2003/22854, University of Dortmund, 138 pages, Aug. 28, 2006. |
Lumelsky, et al. “An Algorithm for Maze Searching with Azimuth Input”, 1994 IEEE International Conference on Robotics and Automation, San Diego, CA vol. 1, pp. 111-116, 1994. |
Luo et al., “Real-time Area-Covering Operations with Obstacle Avoidance for Cleaning Robots,” 2002, IEeE, p. 2359-2364. |
Ma “Thesis: Documentation On Northstar”, California Institute of Technology, 14 pages, May 17, 2006. |
Madsen, et al. “Optimal landmark selection for triangulation of robot position”, Journal of Robotics and Autonomous Systems vol. 13 pp. 277-292, 1998. |
Martishevcky, “The Accuracy of point light target coordinate determination by dissectoral tracking system”, SPIE vol. 2591 pp. 25-30. |
Matsutek Enterprises Co. Ltd “Automatic Rechargeable Vacuum Cleaner”, http://matsutek.manufacturer.globalsources.com/si/6008801427181/pdtl/Home-vacuum/10 . . . , Apr. 23, 2007. |
McGillem, et al. “Infra-red Lacation System for Navigation and Autonomous Vehicles”, 1988 IEEE International Conference on Robotics and Automation, vol. 2, pp. 1236-1238, Apr. 24-29, 1988. |
McGitlem,et al. “A Beacon Navigation Method for Autonomous Vehicles”, IEEE Transactions on Vehicular Technology, vol. 38, No. 3, pp. 132-139, Aug. 1989. |
Michelson “Autonomous Navigation”, 2000 Yearbook of Science & Technology, McGraw-Hill, New York, ISBN 0-07-052771-7, pp. 28-30, 1999. |
Miro, et al. “Towards Vision Based Navigation in Large Indoor Environments”, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China, pp. 2096-2102, Oct. 9-15, 2006. |
MobileMag “Samsung Unveils High-tech Robot Vacuum Cleaner”, http://www.mobilemag.com/content/100/102/C2261/, 4 pages, Mar. 18, 2005. |
Monteiro, et al. “Visual Servoing for Fast Mobile Robot: Adaptive Estimation of Kinematic Parameters”, Proceedings of the IECON '93., International Conference on Industrial Electronics, Maui, HI, pp. 1588-1593, Nov. 15-19, 1993. |
Moore, et al. A simple Map-bases Localization strategy using range measurements, SPIE vol. 5804 pp. 612-620, 2005. |
Munich et al. “SIFT-ing Through Features with ViPR”, IEEE Robotics & Automation Magazine, pp. 72-77, Sep. 2006. |
Munich et al. “ERSP: A Software Platform and Architecture for the Service Robotics Industry”, Intelligent Robots and Systems, 2005. (IROS 2005), pp. 460-467, Aug. 2-6, 2005. |
Nam, et al. “Real-Time Dynamic Visual Tracking Using PSD Sensors and extended Trapezoidal Motion Planning”, Applied Intelligence 10, pp. 53-70, 1999. |
Nitu et al. “Optomechatronic System for Position Detection of a Mobile Mini-Robot”, IEEE Transactions on Industrial Electronics, vol. 52, No. 4, pp. 969-973, Aug. 2005. |
On Robo “Robot Reviews Samsung Robot Vacuum (VC-RP30W)”, www.onrobo.com/reviews/AT—Home/vacuum—cleaners/on00vcrb3Orosam/index.htm.. 2 pages, 2005. |
InMach “Intelligent Machines”, www.inmach.de/inside.html, 1 page, Nov. 19, 2008. |
Innovation First “2004 EDU Robot Controller Reference Guide”, http://www.ifirobotics.com, 13 pgs., Mar. 1, 2004. |
Wolf et al. “Robust Vision-based Localization for Mobile Robots Using an Image Retrieval System Based on Invariant Features”, Proceedings of the 2002 IEEE International Conference on Robotics & Automation, Washington, D.C. pp. 359-365, May 2002. |
Wolf et al. “Robust Vision-Based Localization by Combining an Image-Retrieval System with Monte Carol Localization”, IEEE Transactions on Robotics, vol. 21, No. 2, pp. 208-216, Apr. 2005. |
Wong “EIED Online>> Robot Business”, ED Online ID# 13114, 17 pages, Jul. 2006. |
Yamamoto et al “Optical Sensing for Robot Perception and Localization”, 2005 IEEE Workshop n Advanced Robotics and its Social Impacts, pp. 14-17, 2005. |
Yata et al. “Wall Following Using Angle Information Measured by a Single Ultrasonic Transducer”, Proceedings of the 1998 IEEE, International Conference on Robotics & Automation, Leuven, Belgium. pp. 1590-1596, May 1998. |
Yun, et al. “Image-Based Absolute Positioning System for Mobile Robot Navigation”, IAPR International Workshops SSPR, Hong Kong, pp. 261-269, Aug. 17-19, 2006. |
Yun, et al. “Robust Positioning a Mobile Robot with Active Beacon Sensors”, Lecture Notes in Computer Science, 2006, vol. 4251, pp. 690-897, 2006. |
Yuta, et al. “Implementation of an Active Optical Range sensor Using Laser Slit for In-Door Intelligent Mobile Robot”, IEE/RSJ International workshop on Intelligent Robots and systems (IROS 91) vol. 1, Osaka, Japan, pp. 415-420, Nov. 3-5, 1991. |
Zha et al. “Mobile Robot Localization Using Incomplete Maps for Change Detection in a Dynamic Environment”, Advanced Intelligent Mechatronics '97. Final Program and Abstacts., IEEE/ASME International Conference, pp. 110, Jun. 16-20, 1997. |
Zhang, et al. “A Novel Mobile Robot Localization Based on Vision”, SPIE vol. 6279, 6 pages, Jan. 29, 2007. |
Euroflex Intellegente Monstre Mauele (English only except). |
Roboking—not just a vacuum cleaner, a robot! Jan. 21, 2004, 5 pages. |
SVET Computers—New Technologies—Robot vacuum cleaner, 1 page. |
Popco.net Make your digital life http://www.popco.net/zboard/view.php?id=tr—review&no=40 accessed Nov. 1, 2011. |
Matsumura Camera Online Shop http://www.rakuten.co.jp/matsucame/587179/711512/ accessed Nov. 1, 2011. |
Dyson's Robot Vacuum Cleaner—the DC06, May 2, 2004 http://www.gizmag.com/go/12821 accessed Novmber 11, 2011. |
Electrolux Trilobite, http://www.electrolux-ui.com:8080/2002%5C822%5C833102EN.pdf 10 pages. |
Electrolux Trilobite, Time to enjoy life, 38 pages http://www.robocon.co.kr/trilobite/Presentation—Trilobite—Kor—030104.ppt accessed Dec. 22, 2011. |
Facts on the Trilobite http://www.frc.ri.cmu.edu/˜hpm/talks/Extras/trilobite.desc.html 2 pages accessed Nov. 1, 2011. |
Euroflex Jan. 1, 2006 http://www.euroflex.tv/novita—dett.php?id=15 1 page accessed Nov. 1, 2011. |
FloorBotics, VR-8 Floor Cleaning Robot. Product Description for Manuafacturers, http://www.consensus.com.au/SoftwareAwards/CSAarchive/CSA2004/CSAart04/FloorBot/F. |
Friendly Robotics, 18 pages http://www.robotsandrelax.com/PDFs/RV400Manual.pdf accessed Dec. 22, 2011. |
It's eye, 2003 www.hitachi.co.jp/rd/pdf/topics/hitac2003—10.pdf 2 pages. |
Hitachi, May 29, 2003 http://www.hitachi.co.jp/New/cnews/hl—030529—hl—030529.pdf 8 pages. |
Robot Buying Guide, LG announces the first robotic vacuum cleaner for Korea. Apr. 21, 2003 http://robotbg.com/news/2003/04/22/lg—announces—the—first—robotic—vacu. |
CleanMate 365, Intelligent Automatic Vacuum Cleaner, Model No. QQ-1, User Manual www.metapo.com/support/user—manual.pdf 11 pages. |
UBOT, cleaning robot capable of wiping with a wet duster, http://us.aving.net/news/view.php?articleId=23031, 4 pages accessed Nov. 1, 2011. |
Taipei Times, Robotic vacuum by Matsuhita about ot undergo testing. Mar. 26, 2002 http://www.taipeitimes.com/News/worldbiz/archives/2002/03/26/0000129338 accessed. |
Tech-on! http://techon.nikkeibp.co.jp/members/01db/200203/10065011, 4 pages, accessed Nov. 1, 2011. |
http://ascii.jp/elem/000/000/330/330024/. |
IT media http://www.itmedia.co.jp/news/0111/16/robofesta—m.html accessed Nov. 1. 2011. |
Yujin Robotics, an intelligent cleaning robot ‘iclebo Q’ AVING USA http://us.aving.net/news/view.php?articleId=7257, 8 pages accessed Nov. 4, 2011. |
Special Reports, Vacuum Cleaner Robot Operated in Conjunction with 3G Celluar Phone vol. 59, No. 9, 2004 3 pages http://www.toshiba.co.jp/tech/review/2004/09/59—0. |
Toshiba Corporation 2003, http://warp.ndl.go.jp/info:ndljp/pid/258151/www.soumu.go.jp/joho—tsusin/policyreports/chousa/netrobot/pdf/030214—1—33—a.pdf 16 pages. |
http://www.karcher.de/versions/intg/assets/video/2—4—robo—en.swf. Accessed Sep. 25, 2009. |
McLurkin “The Ants: A Community of Microrobots”, Paper submitted for requirements of BSEE at MIT, May 12, 1995. |
Grumet “Robots Clean House”, Popular Mechanics, N0vember 2003. |
McLurkin Stupid Robot Tricks: A Behavior-based Distributed Algorithm Library for Programming Swarms of Robots, Paper submitted for requirements of BSEE at MIT, May 2004. |
Kurs et al, Wireless Power transfer via Strongly Coupled Magnetic Resonances, Downloaded from www.sciencemag.org, Aug. 17, 2007. |
U.S. Appl. No. 60/605,066, filed Aug. 27, 2004, Taylor. |
U.S. Appl. No. 60/605,181, filed Aug. 27, 2004, Taylor. |
Roboking—not just a vacuum cleaner, a robot! |
Robovacc1999. |
SVET Computers—New Technologies—Robot vacuum claeaner. |
http://www.popco.net/zboard/view.php?id=tr—review&no=40, Dec. 31, 2012. |
http://www.rakuten.co.jp/matsucame/587179/711512/. |
http://www.gizmag.com/go/1282/. |
http://www.electrolux-ui.com:8080/2002%5C822%5C833102EN.pdf. |
http://www.robocon.co.kr/trilobrite/Presentation—Trilobrite—Kor—030104.ppt. |
http://www.frc.ri.cmu.edu/˜hpm/talks/Extras/trilobrite.desc.html. |
http://www.euroflex.tv/novita—dett.php?id=15. |
http://www.consensus.com.au/SoftwareAwards/CSAarchive/CSA2004/CSAart04/FloorBot/FX1%20Product%20Description%2020%20January%202004.pdf. |
http://www.robotsandrelax.com/PDFs/RV400Manual.pdf. |
http://www.hitachi.co.jp/rd/pdf/topics/hitac2003—10.pdf. |
http://www.hitachi.co.jp/New/cnews/hl—030529—hl—030529.pdf. |
http://robotbg.com/news/2003/04/22/lg—announces—the—first—robotic—vacuum—cleaner—of—korea. |
www.metapo.com/support/user—manual.pdf. |
http://us.aving.net/news/view.php?articleId=23031. |
http://www.taipeitimes.com/News/worldbiz/archives/2002/03/26/0000129338. |
http://techon.nikkeibp.co.jp/members/01db/200203/1006501/. |
http://ascii.jp/elem/000/000/330/3300243. |
http://www.itmedia.co.jp/news/0111/16/robofesta—m.html. |
http://us.aving.net/news/view.php?articleId=7257. |
http://www.toshiba.co.jp/tech/review/2004/09/59—09pdf/a13.pdf. |
http://warp.ndl.go.jp/info:ndljp/pid/258151/www.soumu.go.jp/joho—tsusin/policyreports/chousa/netrobot/pdf/030214—1—33—a.pdf. |
http://www.sk.rs/1999/10/sknt01.html. |
International search report and written opinion dated Aug. 30, 2011 for corresponding application PCT/US2010/043922. |
Number | Date | Country | |
---|---|---|---|
20100063628 A1 | Mar 2010 | US |
Number | Date | Country | |
---|---|---|---|
60410480 | Sep 2002 | US |
Number | Date | Country | |
---|---|---|---|
Parent | 12512114 | Jul 2009 | US |
Child | 12610792 | US | |
Parent | 11341111 | Jan 2006 | US |
Child | 11682642 | US | |
Parent | 10661835 | Sep 2003 | US |
Child | 11341111 | US | |
Parent | 11860272 | Sep 2007 | US |
Child | 12255393 | US | |
Parent | 11533294 | Sep 2006 | US |
Child | 11860272 | US | |
Parent | 11109832 | Apr 2005 | US |
Child | 11533294 | US | |
Parent | 10766303 | Jan 2004 | US |
Child | 11109832 | US |
Number | Date | Country | |
---|---|---|---|
Parent | 11682642 | Mar 2007 | US |
Child | 12512114 | US | |
Parent | 12255393 | Oct 2008 | US |
Child | 12512114 | US |