Abstract: Aspects of the disclosure provide a of generating and following planned trajectories for an autonomous vehicle. For instance, a baseline for a planned trajectory that the autonomous vehicle can use to follow a route to a destination may be determined. A stopping point corresponding to a traffic control that will cause the autonomous vehicle to come to a stop using the baseline may be determined. Sensor data identifying objects and their locations may be received. A plurality of constraints may be generated based on the sensor data. A planned trajectory may be generated using the baseline, the stopping point, and the plurality of constraints, wherein constraints beyond the stopping point are ignored.

Abstract: A robotic cleaner includes a cleaning assembly for cleaning a surface and a main robot body. The main robot body houses a drive system to cause movement of the robotic cleaner and a microcontroller to control the movement of the robotic cleaner. The cleaning assembly is located in front of the drive system and a width of the cleaning assembly is greater than a width of the main robot body. A robotic cleaning system includes a main robot body and a plurality of cleaning assemblies for cleaning a surface. The main robot body houses a drive system to cause movement of the robotic cleaner and a microcontroller to control the movement of the robotic cleaner. The cleaning assembly is located in front of the drive system and each of the cleaning assemblies is detachable from the main robot body and each of the cleaning assemblies has a unique cleaning function.

Abstract: A method of cleaning a floor near a vertical surface with a mobile robot. The robot includes a cleaning assembly and a drive assembly having a first wheel and a second wheel. The method includes aligning the robot such that the first wheel and second wheel are configured to roll in a direction substantially parallel to the surface. The method includes driving the robot forward in a direction substantially parallel to the surface. The method further includes: i) turning the first wheel, proximate to the surface, with a first angular velocity, and ii) turning the second wheel, further from the surface, with a second angular velocity. The second angular velocity is greater than the first angular velocity. The robot pushes against the surface while sliding along the same surface.

Abstract: A mobile robot system is provided that includes a docking station having at least two pose-defining fiducial markers. The pose-defining fiducial markers have a predetermined spatial relationship with respect to one another and/or to a reference point on the docking station such that a docking path to the base station can be determined from one or more observations of the at least two pose-defining fiducial markers. A mobile robot in the system includes a pose sensor assembly. A controller is located on the chassis and is configured to analyze an output signal from the pose sensor assembly. The controller is configured to determine a docking station pose, to locate the docking station pose on a map of a surface traversed by the mobile robot and to path plan a docking trajectory.

Abstract: A mobile robot system is provided that includes a docking station having at least two pose-defining fiducial markers. The pose-defining fiducial markers have a predetermined spatial relationship with respect to one another and/or to a reference point on the docking station such that a docking path to the base station can be determined from one or more observations of the at least two pose-defining fiducial markers. A mobile robot in the system includes a pose sensor assembly. A controller is located on the chassis and is configured to analyze an output signal from the pose sensor assembly. The controller is configured to determine a docking station pose, to locate the docking station pose on a map of a surface traversed by the mobile robot and to path plan a docking trajectory.

Abstract: A robot having a signal sensor configured to measure a signal, a motion sensor configured to measure a relative change in pose, a local correlation component configured to correlate the signal with the position and/or orientation of the robot in a local region including the robot's current position, and a localization component configured to apply a filter to estimate the position and optionally the orientation of the robot based at least on a location reported by the motion sensor, a signal detected by the signal sensor, and the signal predicted by the local correlation component. The local correlation component and/or the localization component may take into account rotational variability of the signal sensor and other parameters related to time and pose dependent variability in how the signal and motion sensor perform. Each estimated pose may be used to formulate new or updated navigational or operational instructions for the robot.

Abstract: A robot having a signal sensor configured to measure a signal, a motion sensor configured to measure a relative change in pose, a local correlation component configured to correlate the signal with the position and/or orientation of the robot in a local region including the robot's current position, and a localization component configured to apply a filter to estimate the position and optionally the orientation of the robot based at least on a location reported by the motion sensor, a signal detected by the signal sensor, and the signal predicted by the local correlation component. The local correlation component and/or the localization component may take into account rotational variability of the signal sensor and other parameters related to time and pose dependent variability in how the signal and motion sensor perform. Each estimated pose may be used to formulate new or updated navigational or operational instructions for the robot.

Abstract: A mobile robot system is provided that includes a docking station having at least two pose-defining fiducial markers. The pose-defining fiducial markers have a predetermined spatial relationship with respect to one another and/or to a reference point on the docking station such that a docking path to the base station can be determined from one or more observations of the at least two pose-defining fiducial markers. A mobile robot in the system includes a pose sensor assembly. A controller is located on the chassis and is configured to analyze an output signal from the pose sensor assembly. The controller is configured to determine a docking station pose, to locate the docking station pose on a map of a surface traversed by the mobile robot and to path plan a docking trajectory.

Abstract: Vector Field SLAM is a method for localizing a mobile robot in an unknown environment from continuous signals such as WiFi or active beacons. Disclosed is a technique for localizing a robot in relatively large and/or disparate areas. This is achieved by using and managing more signal sources for covering the larger area. One feature analyzes the complexity of Vector Field SLAM with respect to area size and number of signals and then describe an approximation that decouples the localization map in order to keep memory and run-time requirements low. A tracking method for re-localizing the robot in the areas already mapped is also disclosed. This allows to resume the robot after is has been paused or kidnapped, such as picked up and moved by a user. Embodiments of the invention can comprise commercial low-cost products including robots for the autonomous cleaning of floors.

Abstract: A mobile robot system is provided that includes a docking station having at least two pose-defining fiducial markers. The pose-defining fiducial markers have a predetermined spatial relationship with respect to one another and/or to a reference point on the docking station such that a docking path to the base station can be determined from one or more observations of the at least two pose-defining fiducial markers. A mobile robot in the system includes a pose sensor assembly. A controller is located on the chassis and is configured to analyze an output signal from the pose sensor assembly. The controller is configured to determine a docking station pose, to locate the docking station pose on a map of a surface traversed by the mobile robot and to path plan a docking trajectory.

Abstract: A robot having a signal sensor configured to measure a signal, a motion sensor configured to measure a relative change in pose, a local correlation component configured to correlate the signal with the position and/or orientation of the robot in a local region including the robot's current position, and a localization component configured to apply a filter to estimate the position and optionally the orientation of the robot based at least on a location reported by the motion sensor, a signal detected by the signal sensor, and the signal predicted by the local correlation component. The local correlation component and/or the localization component may take into account rotational variability of the signal sensor and other parameters related to time and pose dependent variability in how the signal and motion sensor perform. Each estimated pose may be used to formulate new or updated navigational or operational instructions for the robot.

Abstract: A mobile robot system is provided that includes a docking station having at least two pose-defining fiducial markers. The pose-defining fiducial markers have a predetermined spatial relationship with respect to one another and/or to a reference point on the docking station such that a docking path to the base station can be determined from one or more observations of the at least two pose-defining fiducial markers. A mobile robot in the system includes a pose sensor assembly. A controller is located on the chassis and is configured to analyze an output signal from the pose sensor assembly. The controller is configured to determine a docking station pose, to locate the docking station pose on a map of a surface traversed by the mobile robot and to path plan a docking trajectory.

Abstract: Vector Field SLAM is a method for localizing a mobile robot in an unknown environment from continuous signals such as WiFi or active beacons. Disclosed is a technique for localizing a robot in relatively large and/or disparate areas. This is achieved by using and managing more signal sources for covering the larger area. One feature analyzes the complexity of Vector Field SLAM with respect to area size and number of signals and then describe an approximation that decouples the localization map in order to keep memory and run-time requirements low. A tracking method for re-localizing the robot in the areas already mapped is also disclosed. This allows to resume the robot after is has been paused or kidnapped, such as picked up and moved by a user. Embodiments of the invention can comprise commercial low-cost products including robots for the autonomous cleaning of floors.

Abstract: An information processing apparatus that executes processing for creating an environmental map includes a camera that photographs an image, a self-position detecting unit that detects a position and a posture of the camera on the basis of the image, an image-recognition processing unit that detects an object from the image, a data constructing unit that is inputted with information concerning the position and the posture of the camera and information concerning the object and executes processing for creating or updating the environmental map, and a dictionary-data storing unit storing dictionary data in which object information is registered. The image-recognition processing unit executes processing for detecting an object from the image with reference to the dictionary data. The data constructing unit applies the three-dimensional shape data to the environmental map and executes object arrangement on the environmental map.

Abstract: A robot having a signal sensor configured to measure a signal, a motion sensor configured to measure a relative change in pose, a local correlation component configured to correlate the signal with the position and/or orientation of the robot in a local region including the robot's current position, and a localization component configured to apply a filter to estimate the position and optionally the orientation of the robot based at least on a location reported by the motion sensor, a signal detected by the signal sensor, and the signal predicted by the local correlation component. The local correlation component and/or the localization component may take into account rotational variability of the signal sensor and other parameters related to time and pose dependent variability in how the signal and motion sensor perform. Each estimated pose may be used to formulate new or updated navigational or operational instructions for the robot.

Abstract: A robotic cleaner includes a cleaning assembly for cleaning a surface and a main robot body. The main robot body houses a drive system to cause movement of the robotic cleaner and a microcontroller to control the movement of the robotic cleaner. The cleaning assembly is located in front of the drive system and a width of the cleaning assembly is greater than a width of the main robot body. A robotic cleaning system includes a main robot body and a plurality of cleaning assemblies for cleaning a surface. The main robot body houses a drive system to cause movement of the robotic cleaner and a microcontroller to control the movement of the robotic cleaner. The cleaning assembly is located in front of the drive system and each of the cleaning assemblies is detachable from the main robot body and each of the cleaning assemblies has a unique cleaning function.

Abstract: A robotic cleaner includes a cleaning assembly for cleaning a surface and a main robot body. The main robot body houses a drive system to cause movement of the robotic cleaner and a microcontroller to control the movement of the robotic cleaner. The cleaning assembly is located in front of the drive system and a width of the cleaning assembly is greater than a width of the main robot body. A robotic cleaning system includes a main robot body and a plurality of cleaning assemblies for cleaning a surface. The main robot body houses a drive system to cause movement of the robotic cleaner and a microcontroller to control the movement of the robotic cleaner. The cleaning assembly is located in front of the drive system and each of the cleaning assemblies is detachable from the main robot body and each of the cleaning assemblies has a unique cleaning function.

Abstract: A robot having a signal sensor configured to measure a signal, a motion sensor configured to measure a relative change in pose, a local correlation component configured to correlate the signal with the position and/or orientation of the robot in a local region including the robot's current position, and a localization component configured to apply a filter to estimate the position and optionally the orientation of the robot based at least on a location reported by the motion sensor, a signal detected by the signal sensor, and the signal predicted by the local correlation component. The local correlation component and/or the localization component may take into account rotational variability of the signal sensor and other parameters related to time and pose dependent variability in how the signal and motion sensor perform. Each estimated pose may be used to formulate new or updated navigational or operational instructions for the robot.

Abstract: An information processing apparatus that executes processing for creating an environmental map includes a camera that photographs an image, a self-position detecting unit that detects a position and a posture of the camera on the basis of the image, an image-recognition processing unit that detects an object from the image, a data constructing unit that is inputted with information concerning the position and the posture of the camera and information concerning the object and executes processing for creating or updating the environmental map, and a dictionary-data storing unit storing dictionary data in which object information is registered. The image-recognition processing unit executes processing for detecting an object from the image with reference to the dictionary data. The data constructing unit applies the three-dimensional shape data to the environmental map and executes object arrangement on the environmental map.

Abstract: An information processing apparatus that executes processing for creating an environmental map includes a camera that photographs an image, a self-position detecting unit that detects a position and a posture of the camera on the basis of the image, an image-recognition processing unit that detects an object from the image, a data constructing unit that is inputted with information concerning the position and the posture of the camera and information concerning the object and executes processing for creating or updating the environmental map, and a dictionary-data storing unit storing dictionary data in which object information is registered. The image-recognition processing unit executes processing for detecting an object from the image with reference to the dictionary data. The data constructing unit applies the three-dimensional shape data to the environmental map and executes object arrangement on the environmental map.