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: Various embodiments may include methods of using image data to estimate motion of a vehicle observed within camera images, such as images captured by a vehicle navigation system of a host vehicle. Various embodiments may include a camera capturing a sequence of images including the observed vehicle, and a processor performing image processing to identify a wheel of the observed vehicle, and determining a rate of rotation of the wheel based on changes in orientation of the wheel between at least two images within the sequence of images. The processor may further determine a speed of the observed vehicle based on the wheel's rate of rotation and diameter. The processor may further determine a direction of travel and/or turning rate of the observed vehicle by determining relative angles of wheels of the observed vehicle in at least one image.

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: 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. Adjusting a driving parameter may include adjusting one or more of a minimum separation distance, a minimum following distance, a speed parameter, or an acceleration rate parameter.

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 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 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, 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 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: 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: Methods, systems, and devices for robotic navigation are described. A robotic device such as a robotic vacuum or a robotic assistant may navigate a first surface. In some cases, navigating the first surface may include removing debris from the first surface. The robotic device may identify a location of a track that connects the first surface to a second surface that is vertically displaced from the first surface. The robotic device may engage the track based at least in part on the identified location. The robotic device may ascend to the second surface by activating an actuator and navigate the second surface (e.g., may remove debris from the second surface, may map the second surface, etc.).

Abstract: Aspects may augment a robotic vehicle with one or more virtual features. In some implementations, streaming video including a first-person view (FPV) of a robotic vehicle is presented on a display of a controller as the robotic vehicle traverses a course. A virtual object may be presented on the display of the vehicle controller, and a virtual contact between the robotic vehicle and the virtual object may be detected. If the virtual object is a virtual obstacle, the robotic vehicle may be penalized for making virtual contact with the virtual obstacle. If the virtual object is a virtual reward, the robotic vehicle may be rewarded for making virtual contact with the virtual reward.

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: 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: Various embodiments may include methods of using image data to estimate motion of a vehicle observed within camera images, such as images captured by a vehicle navigation system of a host vehicle. Various embodiments may include a camera capturing a sequence of images including the observed vehicle, and a processor performing image processing to identify a wheel of the observed vehicle, and determining a rate of rotation of the wheel based on changes in orientation of the wheel between at least two images within the sequence of images. The processor may further determine a speed of the observed vehicle based on the wheel's rate of rotation and diameter. The processor may further determine a direction of travel and/or turning rate of the observed vehicle by determining relative angles of wheels of the observed vehicle in at least one image.

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: Embodiments include devices and methods for vehicle collision avoidance. A processor of the vehicle may receive lidar sensor data comprising one or more points including a distance and a direction from the vehicle to the one or more points. The processor may determine a velocity constraint for each of the one or more points based on the distance and direction from the vehicle to the one or more points. Based on a navigation instruction, the processor may determine one or more velocity solutions that satisfy the one or more velocity constraints. The processor may select a velocity solution from the determined one or more velocity solutions based on the navigation instruction, and may adjust a vehicle velocity based on the selected velocity solution.