Abstract: Various embodiments include processing devices and methods for managing cleaning robot behavior. In some embodiments, a processor of the cleaning robot may determine operational information about operations of a heating, ventilation, and air conditioning (HVAC) system for at least one room in a structure. The processor may determine a time when operation of the HVAC system will end based on the determined operational information. The processor may generate an instruction for the cleaning robot to schedule an operation of the cleaning robot for a time after operation of the HVAC system will end. The processor may execute the generated instruction to perform the operation of the cleaning robot after operation of the HVAC system ends.

Abstract: Various embodiments include processing devices and methods for managing cleaning behavior by a cleaning robot. In some embodiments, a processor of the cleaning robot may obtain user planning information and user location information from one or more information sources external to the cleaning robot. The processor may analyze the user planning information and the user location information. The processor may determine one or more cleaning parameters for the cleaning robot based on the analysis of the user planning information and the user location information. The processor may generate an instruction for the cleaning robot to schedule an operation of the cleaning robot based on the one or more cleaning parameters. The processor may execute the generated instruction to perform the operation of the cleaning robot.

Abstract: Various embodiments include processing devices and methods for managing cleaning robot behavior. In some embodiments, a processor of the cleaning robot may obtain information about one or more cleaning operations in one or more locations of a structure. The processor may analyze the information about the one or more cleaning operations in the one or more locations. The processor may determine one or more cleaning parameters for the cleaning robot based on the analysis of the information about the one or more cleaning operations. Processor may generate an instruction for the cleaning robot to schedule an operation of the cleaning robot based on the one or more cleaning parameters. The processor may execute the generated instruction to perform the operation of the cleaning robot.

Abstract: Various embodiments include processing devices and methods for managing cleaning robot behavior. In some embodiments, a processor of the cleaning robot may obtain one or more images of the location of a structure from a camera external to the cleaning robot. The processor may analyze the one or more images of the location. The processor may determine one or more activity parameters of the location based on the analysis of the one or more images of the location. The processor may generate an instruction for the cleaning robot to schedule an operation of the cleaning robot based on the one or more activity parameters. The processor may execute the generated instruction to perform the operation of the cleaning robot.

Abstract: A lighting system for an unmanned autonomous vehicle (UAV) adapts to the environment around the UAV to ensure status notification lights are visible to an operator and/or abide by regulatory lighting requirements. A processor of the UAV may receive information from various sensors regarding environmental conditions and location of the UAV, and adjust a UAV lighting system to ensure visibility under the environmental conditions. Adjustments to the lighting system may include selection of light sources that are illuminated, the illumination intensity of particular light sources, the colors emitted by various light sources and other lighting configurations.

Abstract: Various embodiments include devices and methods for navigating a robotic vehicle within an environment. In various embodiments, a first image frame is captured using a first exposure setting and a second image frame is captured using a second exposure setting. A plurality of points may be identified from the first image frame and the second image frame. A first visual tracker may be assigned to a first set of the plurality of points and a second visual tracker may be assigned to a second set of the plurality of points. Navigational data may be generated based on results of the first visual tracker and the second visual tracker. The robotic vehicle may be controlled to navigate within the environment using the navigation data.

Abstract: A charging station for a robotic vehicle includes a base configured for use on a body of water; a docking terminal supported on the base, the docking terminal including a charger configured to charge a robotic vehicle docked on the docking terminal; and a renewable energy harvesting device coupled to the charger to provide power to the charger.

Abstract: Some embodiments include methods for customizing operation of the robotic vehicle for an operator. Such embodiments may include identifying a current operator of the robotic vehicle, configuring the robotic vehicle based on metadata associated with an operator profile for the operator, determining whether the operator has changed, and if so, identifying the new operator, deriving updated preference-based settings and performance-based settings for the new operator, and updating configurations of the robotic vehicle accordingly.

Abstract: Embodiments include devices and methods operating a robotic vehicle. A robotic vehicle processor may detect an object posing an imminent risk of collision with the robotic vehicle. The robotic vehicle processor may determine a classification of the detected object. The robotic vehicle processor may manage a rotation of a rotor of the robotic vehicle prior to a collision based on the classification of the object.

Abstract: Various embodiments include methods that may be implemented in a processor or processing device of an aerial robotic vehicle for generating three-dimensional terrain map based on the plurality of altitude above ground level values generated using visual-inertial odometry, and using such terrain maps to control the altitude of the aerial robotic vehicle. Some methods may include using the generated three-dimensional terrain map during landing. Such embodiment may further include refining the three-dimensional terrain map using visual-inertial odometry as the vehicle approaches the ground and using the refined terrain maps during landing. Some embodiments may include using the three-dimensional terrain map to select a landing site for the vehicle.

Abstract: Embodiments include methods performed by a processor of a robotic vehicle for detecting and responding to obstructions to an on-board imaging device that includes an image sensor. Various embodiments may include causing the imaging device to capture at least one image, determining whether an obstruction to the imaging device is detected based at least in part on the at least one captured image, and, in response to determining that an obstruction to the imaging device is detected, identifying an area of the image sensor corresponding to the obstruction and masking image data received from the identified area of the image sensor.

Abstract: A lighting system for an unmanned autonomous vehicle (UAV) adapts to the environment around the UAV to ensure status notification lights are visible to an operator and/or abide by regulatory lighting requirements. A processor of the UAV may receive information from various sensors regarding environmental conditions and location of the UAV, and adjust a UAV lighting system to ensure visibility under the environmental conditions. Adjustments to the lighting system may include selection of light sources that are illuminated, the illumination intensity of particular light sources, the colors emitted by various light sources and other lighting configurations.

Abstract: Various embodiments include dynamically controlling one or more parameters for obtaining and/or processing sensor data received from a sensor on a vehicle based on the speed of the vehicle. In some embodiments, parameters for obtaining and/or processing sensor data may be individually tuned (e.g., decreased, increased, or maintained) by leveraging differences in the level of quality, accuracy, confidence and/or other criteria in sensor data associated with particular missions/tasks performed using the sensor data. For example, the sensor data resolution required for collision avoidance may be less than the sensor data resolution required for inspection tasks, while the update rate required for inspection tasks may be less than the update rate required for collision avoidance. Parameters for obtaining and/or processing sensor data may be individually tuned based on the speed of the vehicle and/or the task or mission to improve consumption of power and/or other resources.