Patent Application
20120169482
This application is related to U.S. patent application Ser. No. 12/338,996, filed Dec. 18, 2008, entitled “Host System and Method for Determining an Attitude of a Device Undergoing Dynamic Acceleration,” which application is incorporated by reference herein in its entirety.
The disclosed embodiments relate generally to remotely controlling devices, and more specifically selecting a device to be remotely controlled in response to input from a remote control in accordance with a navigational state of the remote control.
A remote control (e.g., a human interface device for remotely controlling other devices) may be used to interact with and control a device remotely. In some circumstances a single remote control is able to remotely control multiple different devices. However, enabling a remote control to control multiple devices creates the possibility that when a user attempts to control a first device, a second device will be inadvertently controlled by the remote control. For example, a remote control that uses infrared light pulses to control two televisions may adjust the volume of both of the televisions when a user attempts to adjust the volume of one of the televisions. This is frustrating for users and requires users to spend additional time, and often additional money, to correct the problem. One conventional method of addressing this problem is to have devices with different remote control input schemes. However, this method results in each device needing a separate remote control. Another conventional method of addressing this problem is to reduce the operating range of remote controls for devices. However, this method reduces the utility of the remote control, as the user can only use the remote control in the operating range. Moreover, these problems with conventional remote controls only become more severe as the number of devices that can be remotely controlled increases. Accordingly, as the number of remotely controlled devices increases, it would be highly desirable to find an intuitive and efficient method for selecting devices for remote control by a single remote control.
Accordingly, the embodiments disclosed herein provide a method, system and computer readable storage medium for selecting a device for remote control that reduces or eliminates the problems with conventional methods of selecting a device for remote control. In particular, the disclosed embodiments describe an intuitive and efficient method, system and computer readable storage medium for selecting a device for remote control based on a determined navigational state of a remote control.
Some embodiments provide a method for remotely controlling a device, the method including, at a computer system including one or more processors and memory storing one or more programs: receiving data corresponding to a device-selection command performed at a remote control, where the remote control is configured to provide remote-control commands to a plurality of devices. The method further includes, in response to receiving the data corresponding to the device-selection command: selecting one of the devices as a selected device in accordance with a navigational state of the remote control relative to the selected device, or relative to a proxy for the selected device, at the time that the device-selection command was performed at the remote control; and generating a respective remote-control command for the selected device, where the respective remote-control command will, when received by the selected device, cause the selected device to perform a predefined operation that corresponds to the respective remote-control command.
In accordance with some embodiments, the computer system is the remote control. In accordance with some embodiments, the computer system is a controller that is in communication with the plurality of devices. In accordance with some embodiments, the remote control is a multifunction device with a remote control application. In accordance with some embodiments, the remote control is a dedicated remote control device. In some embodiments, the method further includes preparing, for display at the remote control, information identifying the selected device.
In accordance with some embodiments, the method also includes identifying multiple candidate devices from the plurality of devices in accordance with the navigational state and selecting a respective candidate device from the multiple devices as the selected device. In some embodiments, the multiple candidate devices are selected in accordance with historical navigational states of the remote control. In some embodiments, the respective candidate device is automatically selected using predefined criteria. In some embodiments, the respective candidate device is selected in accordance with additional input from a user of the remote control. In some embodiments, the method further includes, prior to selecting the respective candidate device generating a list including two or more of the multiple candidate devices and receiving a response indicating selection of the respective candidate device from the list.
In some embodiments, the method also includes receiving data corresponding to a plurality of device-selection commands for a single device where the plurality of device-selection commands were performed at a plurality of distinct remote controls, and generating the remote-control command in accordance with predefined criteria. In some embodiments, the selected device has a predefined device class; the respective remote-control command is a broadcast command that is broadcast to two or more of the plurality of devices; and the respective remote-control command will, when received by a respective additional device that has the predefined device class, cause the respective additional device to perform the predefined operation. In some embodiments, the selected device has a predefined device class, and the method further includes, after selecting the selected device: identifying one or more additional devices that have the predefined device class; and generating one or more additional remote-control commands, where a respective additional remote-control command will, when received by a respective additional device, cause the respective additional device to perform the predefined operation.
In some embodiments, the method further includes acquiring one or more sensor inputs that correspond to beacon data for one or more beacons on the remote control and calculating the navigational state of the remote control, in accordance with the acquired sensor inputs, as the remote control is moved by a user. In some of these embodiments, calculating the navigational state of the remote control includes calculating an attitude and a position of the remote control. In some embodiments, the method also includes acquiring one or more sensor inputs from sensors on the remote control and calculating the navigational state of the remote control, in accordance with the acquired sensor inputs, as the remote control is moved by a user. In some of these embodiments, calculating the navigational state of the remote control includes calculating an attitude and a position of the remote control. In some embodiments, the attitude of the remote control is calculated using a Kalman filter.
In some embodiments, a computer system (e.g., a remote control or a central controller system) includes one or more processors, memory, and one or more programs; the one or more programs are stored in the memory and configured to be executed by the one or more processors and the one or more programs include instructions for performing the operations of any of the methods described above or elsewhere in this document. In accordance with some embodiments, a non-transitory computer readable storage medium (e.g., for use by a remote control or a central controller system) has stored therein instructions which when executed by one or more processors, cause a computer system (e.g., a remote control or a central controller system) to perform the operations of any of the methods described above or elsewhere in this document.
Like reference numerals refer to corresponding parts throughout the drawings.
Attention is now directed towards
In some embodiments, a selected device is a device at which remote control 102 is currently pointed, as determined by the navigational state of the remote control 102. In some embodiments, a respective device 104 has a proxy, which represents the device, so that when remote control 102 is pointed at the proxy, the device represented by the proxy is identified as the selected device. For example, in
Remote control 102 generates remote-control command(s) based on the additional input from user 103. The remote-control command(s) are either sent directly to the devices or to a central controller system 101. In some embodiments, remote control 102 sends the remote-control command(s) only to the selected device. In some embodiments, remote control 102 sends the remote-control command(s) to central controller system 101, which sends the remote-control command(s) to the selected device. In other embodiments, remote control 102 transmits remote-control commands to multiple devices (e.g., all devices within range) and central controller system 101 instructs one or more of the devices (e.g., devices other than the selected device) to ignore the remote-control commands. In each of these embodiments, remote control 102 is enabled to control a selected device 104 without inadvertently controlling other devices that are currently within range of remote control 102 but are not the selected device; where the determination of the selected device is based at least in part on a navigational state of remote control 102.
As mentioned above, the selected device is selected in accordance with a navigational state of remote control 102. In some embodiments the navigational state includes an attitude and/or position of the remote relative to the devices. In some embodiments the attitude and/or position are determined using sensors on remote control 102, as illustrated in
In some embodiments, the wireless interface is selected from the group consisting of: a Wi-Fi interface, a Bluetooth interface, an infrared interface, an audio interface, a visible light interface, a radio frequency (RF) interface, and any combination of the aforementioned wireless interfaces. In some embodiments, the wireless interface is a unidirectional wireless interface from remote control 102 to central controller system 101. In some embodiments, the wireless interface is a bidirectional wireless interface. In some embodiments, bidirectional communication is used to perform handshaking and pairing operations. In some embodiments, a wired interface can be used instead of a wireless interface. As with the wireless interface, the wired interface may be a unidirectional or bidirectional wired interface.
In some embodiments, data corresponding to a navigational state of remote control 102 (e.g., raw measurements, calculated attitude, correction factors, position information, etc.) is transmitted from remote control 102 and received and processed on central controller system 101 (e.g., by a “host” side device driver). Central controller system 101 can then use this data to select a selected device and generate remote-control commands (e.g., specifying operations to be performed by a controlled device).
Attention is now directed towards
It should be understood that many different types of sensors can be classified as either absolute sensors or relative sensors. As used herein, an absolute sensor is any sensor capable of providing information on the lowest order navigational state in the reference frame of interest. In other words, an absolute sensor is any sensor that can determine a navigational state of a device relative to a reference frame of interest (e.g., a set of stationary RF/magnetic/sonic beacons, a magnetic field, etc.) without requiring knowledge of a previous known navigational state of the device relative to the reference frame of interest.
In contrast, as used herein, a relative sensor is a sensor that provides a measurement of a change in navigational state relative to a previous navigational state. In other words, a relative sensor can be used to determine an amount of change of a quantity of interest (e.g., displacement, rotation, speed, acceleration, etc.) over time, however this change cannot be used to determine a navigational state of the remote control relative to the reference frame of interest without a previous known navigational state relative to the reference frame of interest. In many situations it is advantageous (e.g., because it is less expensive, faster, more efficient, less computationally intensive, etc.) to use relative sensors to track change in the navigational state. However, relative sensors can accumulate a substantial amount of drift between the actual navigational state and the determined/estimated navigational state, which will persist until the sensors are recalibrated by identifying a known navigational state of the sensors in the reference frame of interest (e.g., by moving the user interface device to a known navigational state or using an absolute sensor to determine a navigational state of the remote control.) Thus, typically, some combination of absolute and/or relative sensors is used to determine the navigational state of the remote control. However it should be understood that, in some embodiments (e.g., with sufficiently accurate relative sensors) the navigational state of a remote control could be determined based on a known starting navigational state and input from only relative sensors.
In some embodiments the absolute sensor(s) include a multi-dimensional magnetometer and a multi-dimensional accelerometer (e.g., the frame of reference is the local magnetic field and gravity). In some embodiments the absolute sensor(s) include one or more camera sensors (e.g., the frame of reference is an infra-red light bar or other visual landmarks). In some embodiments the absolute sensor(s) include one or more magnetic beacon sensors (e.g., the frame of reference is one or more magnetic beacons). In some embodiments the absolute sensor(s) include one or more sonic beacon sensors (e.g., the frame of reference is one or more sonic beacons). In some embodiments the absolute sensor(s) include one or more radio-frequency beacon sensors (e.g., the frame of reference is one or more radio-frequency beacons).
In some embodiments the relative sensor(s) include an inertial measurement unit (e.g., a combination of an accelerometer, magnetometer and gyroscope that is used to determine relative position). In some embodiments the relative sensor(s) include a Doppler effect sensor, proximity sensor/switch, odometer, and/or one or more gyroscopes. In some embodiments the relative sensor(s) include one or more accelerometers.
Different combinations of sensors have different trade-offs in terms of price, accuracy, and sample rate. For some applications one particularly advantageous combination of sensors is a first multi-dimensional accelerometer, a second multi-dimensional accelerometer and a multi-dimensional magnetometer, as described in greater detail below. For some other applications one particularly advantageous combination of sensors is a gyroscope (e.g., a MEMS gyroscope), a multi-dimensional accelerometer and a camera (e.g., in combination with an infrared light bar).
In some embodiments, the one or more processors (1102,
Attention is now directed towards
As one example, in
Selecting a Device for Remote Control
As mentioned above, movement of remote control 102 causes changes to the navigational state of the remote control. These changes are detectable using many different combinations of sensors. For the sake of simplicity and so as not to unnecessarily obscure relevant aspects of the disclosed embodiments, for the description of
Attention is now directed towards
The computer system receives (410) data corresponding to a device-selection command performed at a remote control (e.g., 102 in
In some embodiments, the remote-control commands are device-specific remote-control commands. In other words, in these embodiments each device controlled by the remote control has a unique set of remote-control commands and will not respond to remote-control commands intended for other devices. In some other embodiments, the remote-control commands are not device-specific, and thus the same remote-control commands (e.g., sequences of radio frequency (RF)/infrared (IR)/sonic output) could cause different devices to perform respective functions. For example, a particular sequence of IR pulses could cause one television to increase volume and cause another television to change channels. In another example, the particular sequence of IR pulses could cause two separate televisions to increase volume.
In some embodiments, the computer system is (412) the remote control (e.g., the logic for selecting the selected device is included in the remote control). In some embodiments, the computer system is (414) a controller that is in communication with the plurality of devices. In some embodiments, the controller (e.g. a home automation system or other remote control communication unit) may communicate with another controller that communicates with the devices, or subset of devices (e.g., an audio subsystem, multimedia subsystem, kitchen subsystem, etc.). In some embodiments, the remote control is (416) a multifunction device (e.g., a PDA, smart phone, handheld computer or other multi-purpose portable computing device) with a remote control application. In some embodiments, the remote control is (418) a dedicated remote control device. For example, the dedicated remote control device may be a universal remote control or a remote control bundled with a consumer electronic device or a home automation unit which is primarily intended for use as a remote control.
In some embodiments, receiving the data corresponding to a device-selection command includes receiving (420) data corresponding to a plurality of device-selection commands for a single device, that were performed at a plurality of distinct remote controls; and the remote-control command is (422) generated in accordance with predefined criteria (e.g., distance of the remotes from the single device, predefined hierarchy of the remote controls, etc.). In other words, in some situations there will be multiple different remote controls that are all capable of controlling a set of the devices, and the computer system uses some predefined criteria to determine which of the remote controls will be allowed to control which of the devices. For example where two users have remote controls (e.g., smart phones with remote control applications) that each control any of the televisions in a house, when the computer system receives remote-control commands for a television from both of the remote controls, the computer system will determine which remote control will be allowed to control a particular television. The remote control that is allowed to control the particular television will typically be either: the closest remote control to the particular television, the remote control that is pointed at the particular television, the highest priority remote control (determined in accordance with user preferences), or some combination of these factors.
Operations 426-470 are performed (424) in response to receiving the data corresponding to the device-selection command. In some embodiments, the computer system identifies (426) multiple candidate devices from the plurality of devices in accordance with a navigational state of the remote control. In other words, when there are multiple devices that are located proximate to the remote control, it may not be possible for the computer system to determine with sufficient confidence that one of the devices should be the selected device. In these situations, the computer system selects the most likely devices and subsequently makes a selection of a selected device from the candidate devices, as described in greater detail below with reference to operations 448-454.
In some embodiments, the multiple candidate devices are identified (428) in accordance with historical navigational states of the remote control. For example, the computer system may identify paths that have been traveled by remote controls. In addition to showing typical traffic patterns of users, these paths will not pass through walls, thus the computer system will be able to approximately determine the locations of walls and other permanent barriers, and thus will be able to determine which devices are in the same room as the remote control. Once rooms have been identified, the computer system may give preference to remote controls for controlling devices that are located in the same room as the device (e.g., only devices in the same room as the remote control are selected as candidate devices), as it is more likely that a user is attempting to control a device that is located in the same room as the user. Similarly historical temporal data may also be used to give preference to devices that are typically operated around a particular time of day when the device-selection command is received at the particular time of day. For example, a television may be preferred in the evening, while a radio is preferred in the morning if the television is typically operated by the user in the evening and the radio is typically operated by the user in the morning.
In some embodiments, prior to selecting the respective candidate device, the computer system generates (430) a list including two or more of the multiple candidate devices, and receives (432) a response indicating selection of the respective candidate device from the list. In other words, when the computer system identifies multiple candidate devices that the user most likely intended to select (e.g., multiple devices that are within a predefined threshold distance from the device in a particular direction), the computer system provides the user of the remote control with a list of candidate devices, and the selected device is a respective device selected by the user from the list of candidate devices presented to the user.
The computer system selects (434) one of the devices as a selected device in accordance with a navigational state of the remote control relative to the selected device, or a proxy for the selected device, at the time that the device-selection command was performed at the remote control. For example, when there are multiple televisions in a room, and the remote control is pointed at a particular television, the computer system will select the particular television as the selected device, because the television at which the user pointed the remote control is most likely the television that the user intended to control with the remote control. In some embodiments, the navigational state of the remote control is determined based on sensor inputs from the remote control. In some embodiments, the navigational state of the remote control is also based on sensor inputs (e.g., cameras) from other devices. For example, if one or more of the devices has a camera that can see the remote control, that camera may have additional information that will help to more accurately determine a position and/or attitude of the remote control based on the visual appearance of the remote control.
In some embodiments, the navigational state of the remote control is the navigational state of the remote control at the time the operation (e.g., a button press on the remote by the user) that caused the device-select command to be generated was performed. It should be understood that the navigational state of the remote control “at the time that the device select command was performed,” may include either the nearest or next or preceding attitude and/or position determination or a combination/interpolation (e.g., average) of two or more of these navigational states.
While the selected device will typically be selected in accordance with a navigational state of the remote control relative to the selected device, the selected device may also be selected in accordance with a navigational state of the remote control relative to a proxy for the selected device. For example, a user may define one or more objects, symbols or physical positions as a representation/proxy of a particular device. After the user has generated or otherwise established this definition, when the remote control is pointed at the representation/proxy of the particular device, the computer system will treat the particular device as though it were located at the position of the representation/proxy of the particular device (e.g., the particular device will be included in the set of candidate devices or the particular device will be selected as the selected device). As one example of defining a representation/proxy of a particular device, a user may define a picture on a wall in a house as a proxy for a light switch in a garage, and when the remote control is pointed at the picture on the wall, the computer system enables the user to control the light switch in the garage by inputting commands via the remote control (e.g., pressing or tapping an on/off button). In other words, in some embodiments, the proxy is at a location that is different than (e.g., remote from) a location of the selected device.
In some embodiments, the computer system acquires (436) one or more sensor inputs from sensors on the remote control and calculates (438) the navigational state of the remote control, in accordance with the acquired sensor inputs, as the remote control is moved by a user. In some embodiments, calculating the navigational state of the remote control includes calculating (440) an attitude and a position of the remote control. For example, sensors on the remote control (e.g., magnetometers, gyroscopes, accelerometers, beacon sensors, etc.) are used to identify a position and attitude of the remote control relative to the devices, as illustrated in
In some embodiments, the computer system acquires (442) one or more sensor inputs that correspond to beacon data for one or more beacons on the remote control and calculates (438) the navigational state of the remote control, in accordance with the acquired sensor inputs, as the remote control is moved by a user. In some embodiments, calculating (440) the navigational state of the remote control includes calculating an attitude and a position of the remote control. In other words, the computer system acquires sensor inputs from devices 104 that are able to observe signals from one or more beacons on remote control 102. For example, signals from one or more beacons (e.g., 106-b in
In some embodiments, the beacons include one or more different types of beacons, including: sonic beacons, radio frequency (RF) beacons, light (IR) beacons, etc. It should be understood that the sensors and/or the beacons may be on the remote control, on the devices, on the central controller system, or separate from the remote control, devices, and central controller system (e.g., either the sensors or the beacons may be stand-alone sensors or stand-alone beacons); typically, however, at one of the beacons or beacon sensors is on (or in) the remote control. In some embodiments, the navigational state of the remote control is determined in accordance with signals from multiple beacons detected by a single sensor. In some embodiments, the navigational state of the remote control is determined in accordance with signals from a single beacon detected by multiple sensors.
In some embodiments, the navigational state of the remote control includes an attitude and a position of the remote control. In some embodiments, the attitude of the remote control is (444) calculated using a Kalman filter, as described in greater detail in U.S. patent application Ser. No. 12/338,996 (particularly with reference to
In some embodiments, the selecting includes identifying (448) multiple candidate devices from the plurality of devices in accordance with the navigational state; and selecting (450) a respective candidate device from the multiple devices as the selected device. In some of these embodiments, the respective candidate device is selected (452) in accordance with additional input from a user of the remote control. In other words, instead of presenting the user with a list of all of the available devices within the operational range of the remote control, the computer system selects a reduced set of these devices (e.g., the devices that are generally in a direction in which the remote control is pointing), presents to the user a list having the reduced set of devices, and asks the user to select a device from the reduced set of devices, which enables the user to efficiently select a device that the user wants to control. The user then presses a button on the remote control, touches a touch-sensitive display on the remote control, or otherwise provides input to the remote control so as to select a device from the presented list. This embodiment is particularly useful in situations where there are many devices that can be controlled by the remote control and multiple devices are good matches to the navigational state of the remote control. For example, the multiple devices may be positioned close to each other. In these situations, reducing the number of devices from which a user must make a selection reduces the amount of searching required by the user, while still allowing the user to quickly pick the correct device, thereby reducing any delay caused by the computer system selecting the wrong device.
In some other embodiments, or in some circumstances, the respective candidate device is automatically selected (454) using predefined criteria (e.g., distance from the devices, hierarchy of the devices, etc.). In other words, the respective candidate device is selected without further user intervention in accordance with automated procedures at the computer system. These embodiment is particularly useful in situations where there are a lot of devices that can be controlled by the remote control but there is only one device that is a good match to the navigational state of the remote control. In these situations, the matching device can be selected in a single operation, without requiring further input from the user, thereby reducing the number of steps that the user has to perform before the remote control can control the selected device. In some embodiments the respective candidate device is automatically selected when there is only one candidate device that is a good match, while a list of candidate devices is presented to the user if there are multiple candidate devices that are good matches.
Optionally, the computer system prepares (456), for display at the remote control, information identifying the selected device. For example, the remote control displays an indication (e.g., an icon or text on a display or illumination of a button or light on the remote control) that identifies the selected device. Thus, the user is able to determine which device is being controlled by the remote control simply by looking at the indicator on the remote control. Additionally, when the selected device changes (e.g., because the user points the remote control at another device), the indicator of the currently selected device would change to indicate that the other device was the currently selected device.
The computer system generates (458) a respective remote-control command for the selected device, where the respective remote-control command will, when received by the selected device, cause the selected device to perform a predefined operation that corresponds to the respective remote-control command. In some embodiments, the remote-control command prepares the device to receive subsequent remote-control commands directly from the remote control. In some embodiments, the remote-control command causes the device to perform a specific action (e.g., volume adjust, channel adjust, on/off etc.) In other words, after selecting the selected device, the computer system either prepares the selected device to receive additional commands from the remote control, sends commands directly to the selected device, or both.
In some embodiments, the selected device has (460) a predefined device class, which is, optionally, one of a plurality of predefined device classes. It should be understood that when a device “has” a predefined device class, it is a member of that device class. Additionally, a device may have multiple different classes of different scope (e.g., a television may be a “television” device, a “video” device, an “audio” device, an “entertainment center” device, and a “first floor” device). In some of these embodiments, the respective remote-control command is (462) a broadcast command that is broadcast to two or more of the plurality of devices (e.g., the broadcast command is broadcast to a subset of the devices including the selected device and one or more other devices). In some of these embodiments, the respective remote-control command will, when received by a respective additional device that has the predefined device class, cause the respective additional device to perform (464) the predefined operation (e.g., the same predefined operation that was performed by the selected device is performed by all of the devices with the predefined device class).
In some embodiments, the respective remote-control command is sent only to devices that have the predefined device class (e.g., the subset of the plurality of devices consists of the devices that have the predefined class). In some embodiments, the respective remote-control command includes an indicator of the predefined device class so that even when some of the plurality of devices have the predefined device class while other of the devices do not have the predefined device class, only devices with the predefined device class perform operations in response to receiving the respective remote-control command. In particular, when the respective remote-control command is sent to all of the plurality of devices, only those devices that have the predefined device class would perform the predefined operation. As one example, a “mute” command is sent to all devices with a header indicating that the command is intended only for televisions, and in response to the “mute” command, all of the televisions are muted, while other audio devices are not muted. As another example, an “on” command is sent to all devices with a header indicating that the command is intended only for lights, and in response to the “on” command all of the lights are turned on, while none of the other electronic devices are turned on. In this way multiple devices of the same type can be controlled from a single remote control with a single remote-control command.
In some embodiments, the selected device has (466) a predefined device class (e.g., of a plurality of predefined device classes, as described in greater detail above). In some of these embodiments, after selecting the selected device, the computer system identifies (468) one or more additional devices that have the predefined device class (e.g., one or more devices that have the same device class as the device class of the selected device) and generates (470) one or more additional remote-control commands, where a respective additional remote-control command will, when received by a respective additional device, cause the respective additional device to perform the predefined operation (e.g., the same predefined operation that was performed by the selected device). In other words, in some embodiments, in response to receiving the data corresponding to the device-selection command, the computer system selects a first device (e.g., a first light) in accordance with information indicating that the remote control was pointed at the first device (e.g., the first light) at the time that the device-selection command was performed at the remote control, where the selected device is a member of a predefined device class (e.g., “lights”). In these embodiments, the computer system also generates a respective remote-control command for a set of devices that are members of the predefined device class including the first device (e.g., the first light) and a second device (e.g., a second light) different from the first device, where the respective remote-control command will, when received by the first device and the second device, cause the first device and the second device to perform a same predefined operation that corresponds to the respective remote-control command (e.g., turn the first light on and the second light on).
In some embodiments, the additional devices are selected based on the device-selection command that was initially received from the remote control. For example, a first device-selection command only controls the selected device (e.g., turn on/off only the selected device or mute/unmute only the selected device), while a second device-selection command controls the selected device and one or more additional devices (e.g., turn on/off all devices or mute/unmute all audio sources).
Note that method 400 described above may be governed by instructions that are stored in a non-transitory computer readable storage medium and that are executed by one or more processors of a remote control or a central controller system. As noted above, in some embodiments these methods may be performed in part on a remote control and in part on a central controller system, or on a single integrated system which performs all the necessary operations. Each of the operations shown in
System Structure
It is noted that in some of the embodiments described above, remote control 102 does not include one or more of: position determination module 1118, device-selection module 1122, historical data 1128, remote-control command module 1130, gesture determination module 1132, and/or Kalman filter module 1134 because the various functions performed by these modules and data are either optional, or performed at central controller system 101. For example, remote control 102 may transmit sensor measurements (e.g., accelerometer and magnetometer measurements) and, optionally, button presses 1116 to a central controller system 101 at which one or more of the position determination, attitude determination, device selection, remote-control command generation, gesture determination and other functions are performed.
Each of the above identified elements may be stored in one or more of the previously mentioned memory devices, and each of the above identified programs or modules corresponds to a set of instructions for performing a function described above. The set of instructions can be executed by one or more processors (e.g., CPUs 1102). The above identified modules or programs (i.e., sets of instructions) need not be implemented as separate software programs, procedures or modules, and thus various subsets of these modules may be combined or otherwise re-arranged in various embodiments. In some embodiments, memory 1110 may store a subset of the modules and data structures identified above. Furthermore, memory 1110 may store additional modules and data structures not described above.
Although
It is noted that in some of the embodiments described above, central controller system 101 does not include one or more of: position determination module 1218, device-selection module 1222, historical data 1228, remote-control command module 1230, gesture determination module 1232, and/or Kalman filter module 1134 because the various functions performed by these modules and data are instead performed at remote control 102. In other words, remote control 102 may process sensor measurements (e.g., accelerometer and magnetometer measurements), button presses and other data and transmit remote control navigational state information and/or device selection information to central controller system 101, which uses the information to control devices 104, as described in greater detail above.
Each of the above identified elements may be stored in one or more of the previously mentioned memory devices, and each of the above identified programs or modules corresponds to a set of instructions for performing a function described above. The set of instructions can be executed by one or more processors (e.g., CPUs 1202). The above identified modules or programs (i.e., sets of instructions) need not be implemented as separate software programs, procedures or modules, and thus various subsets of these modules may be combined or otherwise re-arranged in various embodiments. The actual number of processors and software modules used to implement central controller system 101 and how features are allocated among them will vary from one implementation to another. In some embodiments, memory 1210 may store a subset of the modules and data structures identified above. Furthermore, memory 1210 may store additional modules and data structures not described above.
Device 104 optionally includes a user interface 1305 comprising a display device 1306 (LCD display, LED display, CRT display, projector, etc.) and input devices 1307 (e.g., remote control such as a multi-dimensional pointer, a mouse, a keyboard, a trackpad, a trackball, a keypad, buttons, etc.). Memory 1310 includes high-speed random access memory, such as DRAM, SRAM, DDR RAM or other random access solid state memory devices; and may include non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid state storage devices. Memory 1310 may optionally include one or more storage devices remotely located from CPU(s) 1302. Memory 1310, or alternately the non-volatile memory device(s) within memory 1310, comprises a non-transitory computer readable storage medium. In some embodiments, memory 1310 stores the following programs, modules and data structures, or a subset thereof:
Each of the above identified elements may be stored in one or more of the previously mentioned memory devices, and each of the above identified programs or modules corresponds to a set of instructions for performing a function described above. The set of instructions can be executed by one or more processors (e.g., CPUs 1302). The above identified modules or programs (i.e., sets of instructions) need not be implemented as separate software programs, procedures or modules, and thus various subsets of these modules may be combined or otherwise re-arranged in various embodiments. The actual number of processors and software modules used to implement device 104 and how features are allocated among them will vary from one implementation to another. In some embodiments, memory 1310 may store a subset of the modules and data structures identified above. Furthermore, memory 1310 may store additional modules and data structures not described above.
While the descriptions provided above address various methods and systems for selecting a device for remote control in accordance with a navigational state of a remote control, the descriptions provided below address how to determine the navigational state of a remote control based on sensor measurements. One method for accurately determining an attitude of a human interface device such as a remote control is described in greater detail in U.S. patent application Ser. No. 12/338,996, which is incorporated by reference in its entirety.
The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to best explain the principles of the invention and its practical applications, to thereby enable others skilled in the art to best utilize the invention and various embodiments with various modifications as are suited to the particular use contemplated.
Accurately determining a navigational state of a remote control is a non-trivial problem. While a number of different approaches to determining a navigational state of a remote control are known in the art, many of these approaches are either prohibitively expensive, insufficiently accurate or suffer from other flaws that make them unsuitable for use with the remote control (e.g., 102, 1100) described herein. As such, in order to provide a more complete description of the disclosed embodiments, an exemplary remote control 200 including one or more multi-dimensional magnetometers and two or more multi-dimensional accelerometers that are used to inexpensively and accurately determine the attitude of remote control 200 is described below. It should be understood that remote control 200 is a particular embodiment of the remote controls 102, 1100 described above.
One problem with accurately determining a navigational state (e.g., position and/or attitude) of a remote control is that the movement of remote control 200 causes accelerations and decelerations that may cause conventional attitude-determination techniques to fail. Specifically, consider a device that includes a single multi-dimensional magnetometer (e.g., a tri-axial magnetometer) and a single multi-dimensional accelerometer (e.g., a tri-axial accelerometer), which is subject to dynamic acceleration. Note that the term “dynamic acceleration” refers to acceleration and/or deceleration (e.g., accelerations/decelerations during movement of the device). Applying the TRIAD technique to magnetic field measurements from a single multi-dimensional magnetometer and acceleration measurements from a single multi-dimensional accelerometer results in attitude measurements that include errors. The errors arise because the TRIAD technique depends on a constant relationship between the Earth's magnetic field and gravity. Consequently, the TRIAD technique only produces correct attitude measurements when the device is not undergoing dynamic acceleration (e.g., at rest or at constant velocity). If the device is being accelerated, the acceleration measurement includes a combination of gravity and the acceleration imparted by movements of the device. Using this acceleration measurement to represent the Earth's gravity produces substantial errors in the computed attitude. These problems are described in more detail with respect to
One solution is to use a remote control that includes a gyroscope (e.g., a MEMS gyroscope). However, the physics of the gyroscopes can cause artifacts. For example, these types of remote controls can drift when the device is held in a stationary position. Furthermore, these remote controls can require substantial force before the device produces a reaction in the user interface.
Thus, to solve the aforementioned problems, some embodiments use magnetic field measurements from one or more multi-dimensional magnetometers and acceleration measurements from two or more multi-dimensional accelerometers that are included in a remote control to calculate the attitude of the device. In these embodiments, the calculated attitude of the remote control is compensated for errors that would otherwise be caused by dynamic acceleration. In some embodiments, the multi-dimensional accelerometers are placed a specified distance apart in a rigid frame (e.g., a printed circuit board on the device). When the remote control is rotated, the multi-dimensional accelerometers experience different accelerations due to their different radiuses of rotation. Note that when the frame is moved in translation (e.g., without rotation), all the accelerometers experience the same acceleration. It is then possible to use the differences in the accelerometer readings to distinguish between user movement (e.g., dynamic acceleration) and the acceleration caused by Earth's gravity to correctly estimate the attitude of the device.
In some embodiments, the two or more multi-dimensional accelerometers 201-202 are selected from the group consisting of: a 2-axis accelerometer that measures a magnitude and a direction of an acceleration force in two dimensions and a 3-axis accelerometer that measures a magnitude and a direction of an acceleration force in three dimensions.
In some embodiments, the one or more multi-dimensional magnetometers 203 are selected from the group consisting of: a 2-axis magnetometer that measures a magnitude and a direction of a magnetic field in two dimensions and a 3-axis magnetometer that measures a magnitude and a direction of a magnetic field in three dimensions.
In some embodiments, remote control 200 also includes one or more of the following additional user interface components: a keypad, one or more thumb wheels, one or more light-emitting diodes (LEDs), a audio speaker, an audio microphone, a liquid crystal display (LCD), a projector, etc.
In some embodiments, remote control 200 includes one or more processors. In these embodiments, the one or more processors process the acceleration measurements received from multi-dimensional accelerometers 201-202 and/or magnetic field measurements received from multi-dimensional magnetometer 203 to determine displacements (e.g., lateral displacements and/or attitude changes) of remote control 200. These calculations are described in more detail with respect to
In some embodiments, the one or more processors of remote control 200 perform one or more of the following operations: sampling measurement values, at a respective sampling rate, produced by each of the multi-dimensional accelerometers 201-202 and the multi-dimensional magnetometers 203; processing sampled data to determine displacement; transmitting displacement information to central controller system 101; monitoring the battery voltage and alerting central controller system 101 when the charge of the battery is low; monitoring other user input devices (e.g., keypads, buttons, etc.), if any, on remote control 200 (sometimes called a multi-dimensional pointing device); continuously or periodically run background processes to maintain or update calibration of the multi-dimensional accelerometers 201-202 and the multi-dimensional magnetometers 203; provide feedback to the user as needed on the remote (e.g., via LEDs, etc.); and recognizing gestures performed by user movement of the multi-dimensional pointing device (remote control 200).
The software architecture 300 also includes an operating system (e.g., OpenCable Application Platform (OCAP), Windows, Linux, etc.) 310, which includes an execution engine (or virtual machine) 311 that executes applications, an optional API 312 for communicating with a remote control that does not conform to a human interface standard implemented in the operating system 310, middleware 313 that provides management of the resources of central controller system 101 (e.g., allocation of memory, access to access hardware, etc.) and services that connect software components and/or applications, respectively, and central controller system device drivers 314. In some embodiments, central controller system device drivers 314 adjust the gain of remote control 200 based on the resolution and/or aspect ratio of the display of central controller system 101, translates physical movement of remote control 200 to movement of a cursor (or an object) within the user interface of central controller system 101, allows central controller system applications to adjust cursor movement sensitivity, and/or reports hardware errors (e.g., a battery low condition, etc.) to middleware 313.
In some embodiments, remote control 200 periodically samples its sensors. Remote control 200 may also periodically provide the sampled sensor data to the central controller system (e.g., 101 or 1200) at a respective update rate. To reduce power consumption caused by transmitting data to central controller system 101, the update rate may be set at a substantially smaller rate than the sampling rate. Note that the minimum update rate may be governed by the frame rate of the display of the central controller system (e.g., 25 Hz in Europe and 30 Hz in the United States and Asia). Note that there may be no perceivable advantage in providing faster updates than the frame rate except when the transmission media is lossy.
In some embodiments, remote control 200 uses digital signal processing techniques. Thus, the sampling rate must be set high enough to avoid aliasing errors. Movements typically occur at or below 10 Hz, but AC power can create ambient magnetic field fluctuations at 50-60 Hz that can be picked up by a magnetometer. For example, to make sure there is sufficient attenuation above 10 Hz, remote control 200 may use a 100 Hz sampling rate and a 50 Hz update rate.
In some embodiments, remote control 200 reports raw acceleration and magnetic field measurements to central controller system 101. In these embodiments, the central controller system device drivers 314 calculate lateral and/or angular displacements based on the measurements. The lateral and/or angular displacements are then translated to cursor movements based on the size and/or the resolution of the display of central controller system 101. In some embodiments, central controller system device drivers 314 use a discrete representation of angular displacement to perform sampling rate conversion to smoothly convert from the physical resolution of remote control 200 (e.g., the resolution of the accelerometers and/or the magnetometers) to the resolution of the display.
In some embodiments, central controller system device drivers 314 interpret a sequence of movements (e.g., changes in attitude, displacements, etc.) as a gesture. For example, the user 103 may use remote control 200 to move a cursor in a user interface of central controller system 101 so that the cursor points to a dial on the display of central controller system 101. The user 103 can then select the dial (e.g., by pressing a button on remote control 200) and turn remote control 200 clockwise or counter-clockwise (e.g., roll) to activate a virtual knob that changes the brightness, contrast, volume, etc., of a television set. Thus, the user 103 may use a combination or sequence of keypad presses and pointing device movements to convey commands to the central controller system. Similarly, the user 103 may use a twist of a wrist to select the corner of a selected image (or video) for sizing purposes. Note that the corner of an image may be close to another active object. Thus, selecting the image may require careful manipulation of remote control 200 and could be a tiresome exercise. In these cases, using a roll movement as a context sensitive select button may reduce the accuracy users need to maintain with the movement of remote control 200.
In some embodiments, remote control 200 computes the physical displacement of the device and transmits the physical displacement of the device to central controller system 101. Central controller system device drivers 314 interpret the displacement as cursor movements and/or gestures. Thus, central controller system device drivers 314 can be periodically updated with new gestures and/or commands to improve user experience without having to update the firmware in remote control 200.
In some other embodiments, remote control 200 computes the physical displacement of the device and interprets the displacements as cursor movements and/or gestures. The determined cursor movements and/or gestures are then transmitted to central controller system 101.
In some embodiments, remote control 200 reports its physical spatial (e.g., lateral and/or angular) displacements based on a fixed spatial resolution to central controller system 101. Central controller system device drivers 314 interpret the distance and/or angle traversed into appropriate cursor movements based on the size of the display and/or the resolution of the display. These calculated displacements are then translated into cursor movements in the user interface of central controller system 101.
Although remote control 200 may provide data (e.g., position/displacement information, raw measurements, etc.) to central controller system 101 at a rate greater than the frame rate of a display of central controller system 101, the central controller system device drivers 314 needs to be robust enough to accommodate situations where packet transmission fails. In some embodiments, each packet received from remote control 200 is time stamped so that central controller system device drivers 314 can extrapolate or interpolate missing data. This time stamp information may also be used for gesture recognition to compensate for a lossy transmission media.
In some embodiments, remote control 200 omits packets to conserve power and/or bandwidth. In some embodiments, remote control 200 omits packets to conserve power and/or bandwidth only if it is determined that central controller system device drivers 314 can recreate the lost packets with minimal error. For example, remote control 200 may determine that packets may be omitted if the same extrapolation algorithm is running on central controller system 101 and on remote control 200. In these cases, remote control 200 may compare the real coordinates against the extrapolated coordinates and omit the transmission of specified packets of data if the extrapolated coordinates and the real coordinates are substantially similar.
In some embodiments, remote control 200 includes a plurality of buttons. The plurality of buttons allows users that prefer a conventional user interface (e.g., arrow keys, etc.) to continue using the conventional user interface. In these embodiments, central controller system device drivers 314 may need to interpret a combination of these buttons as a single event to be conveyed to middleware 313 of the central controller system.
In some embodiments, central controller system device drivers 314 are configured so that remote control 200 is treated by central controller system 101 as a two-dimensional pointing device (e.g., mouse, trackpad, trackball, etc.).
In some embodiments, the sampled sensor measurements are packetized for transmission 407 and transmitted to central controller system 101 by a transmitter 408.
In some embodiments, sensors 401 are calibrated and corrected 403. For example, the sensors 401 may be calibrated and corrected so that a Kalman filter that is used to compute the attitude of a remote control (e.g., the remote control 200 in
The measurements from the sensors and the determined change in position and/or attitude may also be used to enter and/or exit sleep and wake-on-movement modes 409.
In some embodiments, remote control 200 measures rotations of the remote control over a physical space that is independent of the size, distance and direction of the display of central controller system 101. In fact, remote control 200 may report only displacements between two consecutive samples in time. Thus, the orientation of remote control 200 does not matter. For example, yaw may be mapped to left/right cursor movement and pitch may be mapped to up/down cursor movements.
In some embodiments, to conserve system power, remote control 200 detects a lack of movement of remote control 200 for more than a predetermined time period and puts itself into a low power (e.g., sleep) mode. In some embodiments, a single accelerometer is used to sense whether remote control 200 is being moved and to generate an interrupt to wake (e.g., wake-on-demand) remote control 200 from the sleep mode.
In some embodiments, remote control 200 determines that it should enter a sleep mode based on one or more of the following conditions: the magnitude of the acceleration measurement (e.g., Aobserved) is not greater or smaller than the magnitude of Earth's gravity (e.g., G) by a specified threshold, the standard deviation of Aobserved does not exceed a specified threshold, and/or there is an absence of change in the angular relationship between the measurement of the Earth's magnetic field (e.g., B) and Aobserved greater than a specified threshold. Each of the aforementioned conditions may be used to indicate that the remote control 200 has entered a resting state (e.g., no substantial movement). After remote control 200 has remained in a resting state for a specified number of consecutive samples, remote control 200 enters a sleep mode.
In some embodiments, device-side firmware 400 of remote control 200 is updated by central controller system 101 via a wireless interface.
Some embodiments provide one or more games and/or demo applications that demonstrate how to use the remote control (e.g., movement, controlling objects in the user interface, gestures, etc.).
Before continuing with the discussion, it is instructive to define two terms: body frame and the Earth frame. The body frame is the coordinate system in which B and G are measured with respect to a fixed point on the remote control 200. The diagram 500 in
The Earth frame is the coordinate system in which B and G are measured with respect to a fixed point on the surface of the Earth. The Earth frame is typically the frame of reference for the user 103 of the remote control 200. When the user 103 moves the remote control 200, the user 103 typically thinks about the motion relative to the Earth frame.
Thus, the solution to the attitude of the remote control 200 can be formulated as follows: given two measurements of two constant vectors taken with respect to a body frame (of the remote control 200) that has undergone a rotation, solve for the rotation of the remote control 200 in the Earth frame.
There are a number of techniques can determine the attitude of the remote control 200. As discussed above, TRIAD is one such technique. Note that the following calculations may be formulated using Quaternion-based arithmetic to avoid issues with singularity associated with the TRIAD technique. The TRIAD technique operates as follows.
Given w1 and w2, which represent measurements (observations) of the B and G vectors in the body frame, the following are defined:
where, r1 is the normalized column vector w1, r2 is a normalized column vector orthogonal to r1 and w2, and r3 is a normalized column vector orthogonal to r1 and r2.
Correspondingly, B and G are also known in the Earth frame. However these measurements are known a-priori; that is, they do not need to be measured and may be calculated from well-known theoretical models of the earth. For example, the magnitude and direction of the earth's magnetic and gravitational fields in San Jose, Calif. can be calculated without making new measurements. Thus the measurements in the body frame may be compared relative to these known vectors. If we call the vectors representing B and G in the Earth frame v1 and v2, then we may define:
where s1 is the normalized column vector v1, s2 is a normalized column vector orthogonal to s1 and v2, and s3 is a normalized column vector orthogonal to s1 and s2.
Using the normalized column vectors defined above, the attitude matrix (A) that gives the rotational transform (e.g., for generating an uncorrected attitude of the remote control 200) in the Earth frame is:
A=R·S
T (7)
where R=[r1|r2|r3] (e.g., a matrix comprised of the three column vectors r1, r2, and r3), S=[s1|s2|s3] (e.g., a matrix comprised of the three column vectors s1, s2, and s3), and the “T” superscript denotes the transpose of the matrix to which it is applied.
Applying to the problem at hand, if v1 and v2 are given as the B and G vectors in the Earth frame and w1 and w2 are inferred from measurements produced by the multi-dimensional accelerometers 201-202 and the multi-dimensional magnetometer 203, the TRIAD technique may be used to compute the uncorrected attitude A of the remote control 200.
As discussed above, the accuracy of the relative heading/attitude of the remote control 200 determined by the TRIAD technique is predicated on the assumption that the device is not subject to dynamic acceleration. This assumption does not hold true in applications, in which the user 103 makes continuous movements and/or gestures with the remote control 200.
In order to solve the aforementioned problems, some embodiments include two or more accelerometers to measure the dynamic acceleration that the remote control 200 experiences.
Dynamic acceleration experienced the remote control 200 may include translational acceleration imparted by lateral movement of the remote control 200 and rotational acceleration. When the remote control 200 is affected by translational acceleration, both multi-dimensional accelerometers 703-704 experience the same dynamic acceleration. When the device is affected by angular acceleration, the multi-dimensional accelerometers 703-704 experience dynamic acceleration proportional to their distance from the pivot origin 702.
For example, consider the case when the remote control 200 is pivoted about the pivot origin 702, causing the multi-dimensional accelerometers 703 and 704 to produce composite acceleration measurements AOBS 705 and AOBS 706. The composite acceleration measurement AOBS 705 is a vector sum of the acceleration caused by Earth's gravity (G 707) and the dynamic acceleration a experienced by the first multi-dimensional accelerometer 703 (A). The composite acceleration measurement AOBS 706 is a vector sum of the acceleration caused by Earth's gravity (G 707) and the dynamic acceleration b experienced by the second multi-dimensional accelerometer 704 (B). Note that since the multi-dimensional accelerometer 704 is farther from the pivot origin 702 than the multi-dimensional accelerometer 703, the acceleration due to the rotation about the pivot origin 702 is greater at the second multi-dimensional accelerometer 704 (B) than at the first multi-dimensional accelerometer 703 (A). AOBS 705 and AOBS 706 include errors 708 and 709, respectively.
The change in the attitude of the remote control 200 may be computed using measurements from both of the two multi-dimensional accelerometers 703-704. When the dynamic acceleration is entirely translational, the difference between the two computed attitudes is zero. In some embodiments, only rotational movement is translated into cursor movements. Thus, translational displacements do not result in translational cursor movement because purely translational movements do not affect yaw, pitch or roll. However, when the dynamic acceleration includes rotational components, the difference between the two accelerometer measurements produced by the two multi-dimensional accelerometers 703-704 is used to substantially reduce the error in the calculated attitude of the remote control 200 that is caused by dynamic acceleration, thereby creating a more accurate and efficient remote control.
In some embodiments, the attitude of a remote control (e.g., the remote control 200 in
Attention is now directed to
In some embodiments, during the predict phase, a predicted state {circumflex over (x)} and a predicted error covariance matrix P are determined as follows:
where {circumflex over (x)}(tk+1) is the predicted state of the Kalman filter at timestep k+1, f(x,u,t) are the dynamics of the system (defined below), x is the state, u is a control input (e.g., accelerations due to the arm of the user 103), t is time, Pk(tk) is the predicted error covariance matrix at timestep k, Pk(tk+1) is the predicted error covariance matrix at timestep k+1, Q(tk) is an approximation of the process noise matrix at timestep k, and Φ is a state transition matrix, which is obtained from the system dynamics.
The state transition matrix, Φ, is nominally an identity matrix (i.e., ones on the diagonal) for those states that do not have a dynamics model. A dynamics model is a model of the underlying dynamic system. For example, the dynamics model for a body in motion may include Newton's equations of motion. In some embodiments, the dynamics model for attitude determination is defined by Equations (15)-(21) below. In some embodiments, only the quaternion representing the attitude of the remote control and the vector including values representing the body rotation rate are associated with dynamic models. Thus, the only non-zero off-diagonal elements of the state transition matrix Φ are the portions of the state transition matrix that correspond to the covariances of the quaternion and body rotation rate states. Numerical values for this portion of the state transition matrix may be calculated for each timestep using a finite difference scheme instead of calculation of the dynamic system's Jacobian matrix. (Note that finding and integrating the Jacobian is the traditional technique of computing the state transition matrix.) In this finite difference scheme, a set of perturbed state vectors at time tk, as well as the unperturbed state, are propagated through the dynamics model (e.g., represented by equations (15)-(21) below). Each perturbed state vector is perturbed in a single state. The differences between the propagated perturbed state and the propagated unperturbed state are calculated. The difference vectors are divided by size of the initial perturbation. These difference vectors make up the dynamic portion of the state transition matrix.
In some embodiments, the process noise matrix, Q, only includes values on the diagonal elements of the matrix.
In some embodiments, the state of the Kalman filter includes a state vector defined as follows:
where {right arrow over (q)} is a vector including values of a quaternion representing the attitude of the remote control, {right arrow over (ω)} is a vector including values representing the body rotation rate (e.g., the rate at which the attitude of the remote control is rotating), rrot is a vector including a value that represents the radius of rotation between one of the multi-dimensional accelerometers (e.g., the multi-dimensional accelerometer 703 (A)) and the pivot origin (e.g., the pivot origin 702), aYd and aZd are the bias values in the Y and Z directions of the difference between the two accelerometer measurements (e.g., the accelerometer measurements 204-205). In some embodiments, the bias of the multi-dimensional magnetometer is estimated using a separate Kalman filter.
Before continuing with the discussion of the Kalman filter, it is instructive to discuss the quaternion {right arrow over (q)} representing the attitude of the remote control.
Returning to the discussion of the Kalman filter, in some embodiments, the function f(x,u,t) represents the equations of motion. For example, the equations of motion may be:
where {dot over ({right arrow over (q)} is the first time derivative of the quaternion {right arrow over (q)} representing the attitude of the remote control, {tilde over (ω)} (e.g., see Equation (17), where the components ωx, ωy, and ωz are the x, y, and z components of {right arrow over (ω)}) is the linear mapping of the body rates that when multiplied by quaternion state yields the time rate of change of the quaternion state, {dot over ({right arrow over (ω)} is the angular acceleration (e.g., first time derivative of the body rotation rate) of the remote control, h({right arrow over (a)}diff,{right arrow over (ω)}) is a function of the vector representing the difference between the two accelerometer measurements ({right arrow over (a)}diff) and the body rotation rate vector ({right arrow over (ω)}). h({right arrow over (a)}diff,{right arrow over (ω)}) is defined below.
Each multi-dimensional accelerometer measures a composite (e.g., vector sum) of the following accelerations/forces: tangential, centripetal, gravitational (as measured in the body frame of the accelerometer), and translational. These acceleration components may be represented as follows:
{right arrow over (a)}
A=−{dot over ({right arrow over (ω)}×{right arrow over (r)}A−{right arrow over (ω)}×{right arrow over (ω)}×{right arrow over (r)}A+DCM({right arrow over (q)}){right arrow over (g)}+{right arrow over (a)}translational (18)
{right arrow over (a)}
B=−{dot over ({right arrow over (ω)}×{right arrow over (r)}B−{right arrow over (ω)}×{right arrow over (ω)}×{right arrow over (r)}B+DCM({right arrow over (q)}){right arrow over (g)}+{right arrow over (a)}translational (19)
where {right arrow over (a)}A and {right arrow over (a)}B are the composite accelerations measurements (e.g., the acceleration measurements 204-205) for each of the two accelerometers (e.g., the multi-dimensional accelerometers 201-202) of the remote control, {dot over ({right arrow over (ω)} is the rate of change of the body rotation rate {right arrow over (ω)}, {right arrow over (r)}A and {right arrow over (r)}B are the radius of rotations of each of the two accelerometers relative to a pivot origin, DCM({right arrow over (q)}) is the direction cosine matrix (DCM) that is obtained from the quaternion {right arrow over (q)} representing the attitude of the remote control (e.g., the {right arrow over (q)} is converted to a DCM so that it can operate on the gravity vector {right arrow over (g)}), {right arrow over (g)} is the acceleration due to gravity as viewed from the body frame (e.g., the frame of the accelerometer), and {right arrow over (a)}translational is the translational acceleration.
Note that the Kalman state described above only includes a state value representing the radius of rotation, rrot, to one of the accelerometers (e.g., the multi-dimensional accelerometer 703 (A)). If the offset (e.g., L 722,
A vector difference {right arrow over (a)}diff between {right arrow over (a)}A and {right arrow over (a)}B yields:
{right arrow over (a)}
diff
={right arrow over (a)}
B
−{right arrow over (a)}
A=−{dot over ({right arrow over (ω)}×{right arrow over (r)}diff−{right arrow over (ω)}×{right arrow over (ω)}×{right arrow over (r)}diff (20)
where, {right arrow over (r)}diff is the vector difference between {right arrow over (r)}A and {right arrow over (r)}B (e.g., {right arrow over (r)}diff={right arrow over (r)}B−{right arrow over (r)}A). Note that {right arrow over (a)}diff does not include the acceleration forces due to gravity and translation.
Equation (20) may be rearranged to solve for the angular acceleration {dot over ({right arrow over (ω)}:
where {dot over ({right arrow over (ω)} is evaluated at {dot over ({right arrow over (ω)}·{right arrow over (r)}diff=0 (e.g., when the only non-zero components of the angular acceleration {dot over ({right arrow over (ω)}, are orthogonal to the vector {right arrow over (r)}diff, which is defined in paragraph [00130]). Equation (21) is then used in Equation (16). Note that adiff is a measurement (e.g., from the multi-dimensional accelerometers), w is obtained from state vector, and {right arrow over (r)}diff is the vector difference between {right arrow over (r)}A and {right arrow over (r)}B, as explained above.
In some embodiments, the number of states in the error covariance matrix P is reduced by expressing the variation of the quaternion state as orthogonal modified Rodrigues parameters (MRPs), which have three (3) parameters as compared to four (4) parameters in a quaternion. The MRP and the quaternion contain the same rotation information, but the redundant parameter in the quaternion avoids singularities. In these embodiments, the update of the quaternion state is estimated as an MRP rotation, and then converted to a quaternion. The update of the quaternion state is applied multiplicatively and preserves the unit norm property of the quaternion.
During the update phase, the predicted state matrix and predicted error covariance matrix are updated based on the sensor measurement as follows:
{circumflex over (x)}
k+1(tk)={circumflex over (x)}(tk+1)+Kk({right arrow over (y)}m−ŷ) (22)
P
k+1(tk)=(I−KkGk)Pk(tk) (23)
where {circumflex over (x)}k+1(tk) is the updated state vector at timestep k+1, {circumflex over (x)}(tk+1) is the predicted state vector at timestep k that was calculated in the predict phase, Kk is the Kalman gain, {right arrow over (y)}m is the observed measurements (e.g., the sensor measurements), ŷ is the predicted sensor measurements (e.g., the predicted sensor measurements that are obtained from the predicted state vector and the sensor models described in equations (28) and (29) below), I is the identity matrix, and Gk is an observation transformation matrix that maps the deviations from the state vector to deviations from the observed measurements (e.g., the sensor measurements). Note that the term {right arrow over (y)}m−ŷ is referred to as a residual.
Generally, ŷ is a function of the state vector, the first time derivative of the state vector, and time (e.g., û=g({right arrow over (x)},{dot over ({right arrow over (x)},t)), and may be determined using the sensor models described below. The Kalman gain Kk may be determined using the following equations:
where R is the measurement covariance matrix.
In some embodiments, {right arrow over (y)}m includes the following components:
where {right arrow over (H)}xy is the directional residual of the magnetic field measurement (e.g., the magnetic field measurement 206), {right arrow over (a)}A is the accelerometer measurement (e.g., the accelerometer measurement 205) from a first multi-dimensional accelerometer (e.g., the multi-dimensional accelerometer 202), and {right arrow over (a)}B is the accelerometer measurement (e.g., the accelerometer measurement 204) from a second multi-dimensional accelerometer (e.g., the multi-dimensional accelerometer 201). Note that the directional residual of the magnetic field measurement, {right arrow over (H)}xy, may be used because when determining the attitude of a remote control, only the directional information is required; the magnitude of the magnetic field is not used. In fact, in these embodiments, attempting to correct/update the magnitude of the magnetic field in the Kalman filter state causes the Kalman filter state to diverge. {right arrow over (H)}xy may be calculated from the magnetic field measurement using the technique described in “Spinning Spacecraft Attitude Estimation Using Markley Variables: Filter Implementation and Results” (Joseph E. Sedlak, 2005, available at http://www.ai-solutions.com/library/tech.asp), which is hereby incorporated by reference in its entirety.
In some embodiments, the sensor model for the multi-dimensional magnetometer and the multi-dimensional accelerometers are:
Ĥ
xy
=[R
Bzenith][DCM({circumflex over (q)}(tk+1))]{right arrow over (H)}ref (28)
â=−{dot over ({right arrow over (ω)}×{right arrow over (r)}
Acc−{circumflex over (ω)}(tk+1)×{circumflex over (ω)}(tk+1)×{right arrow over (r)}Acc+DCM({right arrow over (q)}(tk+1)){right arrow over (g)} (29)
where Ĥxy is the two-dimensional directional residual between the measured and estimated magnetometer values, RBzenith is a rotation matrix that rotates the magnetic field measurement to the Z-axis vector in the new frame of reference (e.g., the frame of reference described in “Spinning Spacecraft Attitude Estimation Using Markley Variables: Filter Implementation and Results,” whereby the directional variances of a three dimensional vector are expressed as two variables), DCM({circumflex over (q)}(tk+1)) is the DCM that is obtained from the quaternion {circumflex over (q)} representing the estimated attitude of the remote control (e.g., the {circumflex over (q)} is converted to a DCM so that it can operate on the gravity vector {right arrow over (g)} and/or {right arrow over (H)}ref), {right arrow over (H)}ref is the assumed magnetic field measurement in the Earth frame, and {right arrow over (r)}Acc is the radius of rotation for a respective accelerometer, relative to the pivot point. The angular acceleration {dot over ({right arrow over (ω)} may be obtained from the difference of the accelerometer measurements (e.g., Equation (21)) and acts as a “pass-through” variable for the sensor measurements
In some embodiments, the state vector {circumflex over (x)} is a 10×1 matrix, the error covariance matrix P is a 9×9 matrix, and the observation partial derivative matrix G is an 8×9 matrix. In these embodiments, {right arrow over (q)} is a 4×1 vector, {right arrow over (ω)} a 3×1 vector, rrot is a 1×1 vector, and aYd and aZd are each 1×1 vectors. These components of the state vector {circumflex over (x)} together form a 10×1 matrix.
Accelerometer quantization may cause the attitude determined by the Kalman filter to incorrectly indicate that the remote control is moving when it is not. If left uncorrected, accelerometer quantization may significantly degrade performance of the system in which the remote control is used (e.g., the cursor on the central controller system may drift across the user interface). Thus, in some embodiments, for small values of the accelerometer measurements (e.g., values below twenty times the quantization interval), the techniques described in “Covariance Profiling for an Adaptive Kalman Filter to Suppress Sensor Quantization Effects” by D. Luong-Van et al. (43rd IEEE Conference on Decision and Control, Volume 3, pp. 2680-2685, 14-17 Dec. 2004), which is hereby incorporated by reference in its entirety, are used to mitigate the effects of the quantized data measurements reported by the accelerometers.
Furthermore, accelerometer noise may cause jitter causing the attitude determined by the Kalman filter to indicate that the remote control is moving even when the remote control at rest. Thus, in some embodiments, a deadband is used for values of the accelerometer measurements that occur in a specified range of quantization levels of the accelerometer measurements. For example, the specified range may be between two and twenty times the quantization level of the accelerometers. Note that it is desirable to minimize the deadband, but this minimization must be balanced against the device performance at low angular rates and accelerations where quantization effects will dominate the behavior of the pointer.
As discussed above, substantial error can arise in the calculation of the attitude of a remote control that is undergoing dynamic acceleration. These errors arise from the inability of a single multi-dimensional accelerometer to distinguish between the effects of dynamic acceleration and the actual gravity vector. To compensate for this, in some embodiments, the acceleration measurements from the accelerometers are given less weight when the remote control is undergoing dynamic acceleration than when the remote control is not undergoing dynamic acceleration.
The weight of the acceleration measurements in the Kalman filter may be controlled by the Kalman gain (Kk). Thus, in some embodiments, the Kalman gain is adjusted based on the amount of dynamic acceleration experienced by the remote control. For example, the Kalman gain may be adjusted through the measurement covariance matrix R (see equations 24 and 25, above).
Attention is now directed to
A Kalman gain based on the difference is adjusted (1004), wherein the Kalman gain is used in a Kalman filter that determines the attitude of the device. When the difference is less than a specified threshold, values associated with the first accelerometer measurement and the second accelerometer measurement in a measurement covariance matrix of the Kalman filter (e.g., R) are decreased so that the first accelerometer measurement and the second accelerometer measurement are given more weight in the Kalman filter relative to the magnetic field measurement than when the difference is greater than the specified threshold. When the difference is greater than a specified threshold, covariance values associated with the first accelerometer measurement and the second accelerometer measurement in a measurement covariance matrix of the Kalman filter (e.g., R) are increased so that the first accelerometer measurement and the second accelerometer measurement are given less weight in the Kalman filter relative to the magnetic field measurement than when the difference is less than the specified threshold. For example, when the difference is greater than the specified threshold, the covariance values associated with the first accelerometer measurement and the second accelerometer measurement may be increased by a factor of 100 compared with their values when the difference is less than the specified threshold. This threshold may be defined as being the same differential acceleration threshold as defined for the deadband.
An attitude of the device is determined (1006) using the Kalman filter based at least in part on the Kalman gain, the first accelerometer measurement, the second accelerometer measurement, and a magnetic field measurement received from a multi-dimensional magnetometer of the device. For example, the Kalman filter described above with reference to
This application claims priority to U.S. Provisional Application Ser. No. 61/430,106, filed Jan. 5, 2011, which application is incorporated by reference herein in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 61430106 | Jan 2011 | US |
