Abstract: Methods, devices and systems enable controlling an autonomous vehicle by identifying vehicles that are within a threshold distance of the autonomous vehicle, determining an autonomous capability metric of each of the identified vehicles, and adjusting a driving parameter of the autonomous vehicle based on the determined autonomous capability metric of each of the identified vehicles. Embodiments may further include determining, based on the determined ACMs, whether one or more identified vehicles would provide an operational advantage to the autonomous vehicle in a cooperative driving engagement, and initiating a cooperative driving engagement with the one or more identified vehicles in response to determining that the one or more identified vehicles would provide an operational advantage to the autonomous vehicle in a cooperative driving engagement.

Abstract: Various embodiments include methods and interactive traffic control devices for interactively controlling traffic, which may include receiving refined location and state information associated with individual vehicles on a roadway, and determining customized dynamic traffic control instructions for a first one or more of the individual vehicles. The determined customized dynamic traffic control instructions may be based on the received refined location and state information and offer an optional route alternative to a set limited number of the individual vehicles. The first customized dynamic traffic control instructions may be transmitted by the interactive traffic control device to the first one or more of the individual vehicles.

Abstract: Various embodiments include methods and interactive traffic control devices implementing such methods to receive refined location and state information associated with individual vehicles and determine first customized dynamic traffic control instructions for a first one or more of the individual vehicles and second customized dynamic traffic control instructions for a second one or more of the individual vehicles different from the first one or more of the individual vehicles. The first customized dynamic traffic control instructions may be transmitted to the first one or more of the individual vehicles, and the second customized dynamic traffic control instructions may be transmitted to the second one or more of the individual vehicles.

Abstract: Various embodiments include methods, systems, and devices for interactively controlling traffic. The method, which may be performed by operations of the systems and/or devices, may include receiving, for example by an interactive traffic control device, refined location and state information associated with a first vehicle on a roadway. The interactive traffic control device may also determine at least one notable element in the refined location and state information, customized dynamic traffic control instructions based on the refined location and state information, and whether the customized dynamic traffic control instructions conflict with the at least one notable element. In addition, the interactive traffic control device may transmit the customized dynamic traffic control instructions to the first vehicle in response to determining the customized dynamic traffic control instructions do not conflict with the at least one notable element.

Abstract: Various aspects may include methods enabling a vehicle to broadcast intentions and/or motion plans to surrounding vehicles. Various aspects include methods for using intentions and/or motion plans received from one or more surrounding vehicles.

Abstract: Various embodiments may include methods of limiting a steering command angle during operation of a vehicle. Various embodiments may include determining a speed of the vehicle, applying the determined speed to a dynamic model of the autonomous vehicle to determine a steering wheel command angle limit. Embodiments may further include determining whether a received or commanded steering command angle exceeds the steering wheel command angle limit and altering the steering command angle to an angle no greater than the maximum steering command angle if the received/commanded steering command angle exceeds the steering wheel command angle limit.

Abstract: Various embodiments include methods and aerial robotic vehicles that adjust a flight control parameter based on whether propeller guards are installed. An aerial robotic vehicle processor may determine whether a propeller guard is installed, set a flight parameter based on the determination, and control one or more motors of the aerial robotic vehicle using the flight parameter. When propeller guards are installed, the flight parameter may be set to a value appropriate for controlling the aerial robotic vehicle when the propeller guard is installed. The flight parameter may be one or more of control gains, drag profile control settings, a maximum rotor speed, maximum speed of the aerial robotic vehicle, maximum power usage, restrictions to select modes of operation, visual algorithm settings, or a flight plan. Data from one or more sensors and/or motor controllers may be used to determine the presence of a propeller guard.

Abstract: Various embodiments include devices and methods for controlling a robotic vehicle. Each electronic speed controller (ESC) of the robotic vehicle may receive open loop flight control information from a flight controller or another processing device of the robotic vehicle. In some embodiments, each ESC may store the provided open loop flight control information in a memory. In response to detecting a loss of control signals from the flight controller, each ESC may access the stored open loop flight control information and perform control of a motor associated with each ESC based on the open loop flight control information. The open loop flight control information may be a sequence of motor control instructions to be performed over a period of time, or parameterized information or vehicle state information that enables each ESC to generate a sequence of motor control instructions.

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: 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 mess parameters of a mess in 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 mess 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 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: Aspects may provide navigation assistance to guide a robotic vehicle through a course defined by a plurality of gates each including a fiducial marker that encodes a location, an ordering, and a pose of the corresponding gate. In some implementations, an optimal trajectory may be generated through the course and used to determine whether to provide navigation assistance to the robotic vehicle. The optimal trajectory may include a reference path that extends through openings formed in center portions of the gates, and may be used to create a virtual tunnel indicating a maximum distance that the robotic vehicle may deviate from various points along the reference path. If the robotic vehicle deviates from the optimal trajectory by more than the distance while traversing the course, navigation assistance may be provided to the robotic vehicle.

Abstract: Aspects may define a race course using a plurality of gates each including a fiducial marker that encodes a location, an ordering, and a pose of the corresponding gate. Each of the gates may include an opening through which robotic vehicles participating in a race may traverse, and a flight path may be defined through the opening of the gates. Each fiducial marker may be displayed around a perimeter of the opening of a corresponding gate, and may include a unique pattern that conveys the location, ordering, and pose of the corresponding gate to video cameras provided on the robotic vehicles. A pilot may use the fiducial markers presented on the gates to navigate the robotic vehicle through the race course, for example, so that the pilot may not need to rely solely upon the first-person view provided by the streaming video transmitted from the robotic vehicle.

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 methods for providing adaptive voxels for an aerial light show may include determining a physical location of a robotic vehicle with respect to the aerial display, determining an appropriate light emission for the aerial light show based on the physical location of the robotic vehicle with respect to the aerial display, and adjusting a light emission of a light source of the robotic vehicle accordingly.

Abstract: Various embodiments include methods for performing temperature calibration of a first temperature sensitive unit with an electronic device having a first processing unit that is thermally coupled to the first temperature sensitive unit. Various embodiments may include determining a current temperature of the first temperature sensitive unit, determining a processing load for the first processing unit based on the current temperature and a target temperature, applying the determined processing load to the first processing unit to vary a temperature of the first temperature sensitive unit, and determining a temperature bias for the first temperature sensitive unit at the temperature of the first temperature sensitive unit based on an output of the first temperature sensitive unit.

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: Various embodiments include methods, devices, and aerial robotic vehicles for adjusting a proximity threshold implemented in a collision avoidance system based on whether propeller guards are installed. Methods may include an aerial robotic vehicle processor determining whether a propeller guard is installed, setting, a proximity threshold for collision avoidance based on the determination as to whether propeller guard(s) is/are installed on the aerial robotic vehicle, and controlling one or more motors of the aerial robotic vehicle using the proximity threshold for collision avoidance. When propeller guards are installed, the proximity threshold may be set at a smaller distance than when propeller guards are not installed. The determination of whether a propeller guard is installed may be based on sensor data from one or more sensors configured to detect or indicate the presence of a propeller guard, and/or based on rotor revolution rates determined from a motor or motor controller.