Abstract: An energy supply station can include a mechanical arm, an energy delivery receptacle, and one or more processors. The processors can be configured to detect an arrival of a vehicle at the energy supply station; transmit an indication of the arrival to a remote computer, causing activation of a virtual interface; and move the mechanical arm, based on input at the virtual interface, to cause the energy delivery receptacle to contact or couple with an energy input receptacle of the vehicle.

Abstract: Aspects of this technical solution can include obtaining, from a first vehicle at a second vehicle by a communication interface coupled with the first vehicle and the second vehicle, first data indicating a positioning of an antenna of a location sensor of the first vehicle, obtaining, from the first vehicle at the second vehicle by the communication interface, second data of the first vehicle indicating a physical location of the first vehicle, during movement of the first vehicle caused by the second vehicle, and generating, at the second vehicle during the movement of the first vehicle and based on the first data and the second data, third data corresponding to a calibration of the physical location of the first vehicle.

Abstract: An autonomous vehicle can include first one or more sensors located on a first side of the autonomous vehicle; second one or more sensors located on a second side of the autonomous vehicle; and one or more processors. The can processors be configured to automatically control, using images generated by the first one or more sensors, the autonomous vehicle to an energy supply station responsive to determining to resupply energy to the autonomous vehicle; detect, using the images generated by the first one or more sensors, an arrival of the autonomous vehicle at the energy supply station; and responsive to detecting the arrival of the autonomous vehicle at the energy supply station, switch from processing images generated by the first one or more sensors to processing images generated by the second one or more sensors.

Abstract: Disclosed are a method and an apparatus for predicting vehicle trajectory, and a method and an apparatus for training a neural network prediction model, relating to the field of intelligent driving technology. The method for predicting vehicle trajectory including: determining raster image data of a vehicle at a current time point based on ego-vehicle travelling data of the vehicle during travelling and environmental data of the vehicle; processing the raster image data based on a neural network prediction model, and predicting a trajectory of the vehicle in a future predetermined time period based on the multidimensional feature map, ego-vehicle size data and the environmental data.

Abstract: A system can include one or more processors. The processors can be configured to receive, from a vehicle via first one or more sensors of the vehicle, first images or video of a physical environment surrounding the vehicle; receive, from an energy supply station via second one or more sensors of the energy supply station, second images or video of the physical environment surrounding the vehicle; and responsive to receiving the first images or video and the second images or video, generate a virtual interface depicting the physical environment based on the first images or video and the second images or video, the virtual interface enabling a user to provide input to control a mechanical arm of the energy supply station to supply energy to the vehicle.

Abstract: Systems and methods for automated cooking are described. In one embodiment, an automated kitchen system includes a robotic device and a computing device having a memory storing instructions and a processor. The instructions cause the processor to identify a recipe associated with an order for at least one food item. The instructions cause the processor to identify an ingredient based on the set of instructions of the recipe. The instructions cause the processor to determine a measurement associated with the ingredient based on the set of instructions of the recipe. The instructions cause the processor to cause the robotic device to retrieve a measured portion of the ingredient based on the measurement and deliver the measured portion of the ingredient to cookware. The instructions cause the processor to cause the robotic device to execute a task in response to the measured portion of the ingredient being delivered to the cookware.

Abstract: Various aspects include a robot and method of using the robot, which includes a chassis and a forward propulsion auger. The chassis may include a forward section a rear section; and a maneuvering gimbal. The forward propulsion auger may be positioned on a leading end of the forward section and coupled to a first drive motor. The forward propulsion auger may include at least one fluid nozzle configured to eject a fluid therefrom.

Abstract: A prism for reflecting a laser includes: a single mounting cap at a first end of the prism, and first to seventh trihedral corner (TC) reflectors, each including a reflective surface including: three side edges, and three corners at respective intercept points between the side edges, wherein the seventh TC reflector, among the first to seventh TC reflectors, is on a second end of the prism opposite to the first end of the prism.

Abstract: The foodstuff assembly system can include: a robot arm, a frame, a set of foodstuff bins, a sensor suite, a set of food utensils, and a computing system. The system can optionally include: a container management system, a human machine interface (HMI). However, the foodstuff assembly system 100 can additionally or alternatively include any other suitable set of components. The system functions to enable picking of foodstuff from a set of foodstuff bins and placement into a container (such as a bowl, tray, or other foodstuff receptacle). Additionally or alternatively, the system can function to facilitate transferal of bulk material (e.g., bulk foodstuff) into containers, such as containers moving along a conveyor line.

Abstract: Methods and systems are described herein for hosting and arbitrating algorithms for the generation of structured frames of data from one or more sources of unstructured input frames. A plurality of frames may be received from a recording device and a plurality of object types to be recognized in the plurality of frames may be determined. A determination may be made of multiple machine learning models for recognizing the object types. The frames may be sequentially input into the machine learning models to obtain a plurality of sets of objects from the plurality of machine learning models and object indicators may be received from those machine learning models. A set of composite frames with the plurality of indicators corresponding to the plurality of objects may be generated, and an output stream may be generated including the set of composite frames to be played back in chronological order.

Abstract: A mobility platform is configured to execute one or more tasks in a worksite including a passive landmark. A mobility platform may include a first laser rangefinder and at least one processor configured to sweep the first passive landmark with the first laser rangefinder to collect a first plurality of distance measurements for a first plurality of yaw angles, fit a first shape to the first plurality of distance measurements based on a predetermined shape of the first passive landmark, and determine a position of a geometric center of the first passive landmark relative to the first location of the first laser rangefinder based on the fit first shape.

Abstract: Inspection robots with independent, swappable, drive modules are described. An example inspection robot may have a center body with a plurality of power interfaces, a plurality of communication interfaces, and a plurality of cooling interfaces. The example inspection robot may have a plurality of drive modules, where each drive module is structured to be coupled to a power interface, a communication interface, and a cooling interface.

Abstract: Devices configured to operate on an angled surface (e.g., a roof), and associated systems and methods are disclosed herein. In some embodiments, representative systems include an apparatus with a body assembly, an arm assembly coupled to the body assembly, and a material handling assembly coupled to the arm assembly. The body assembly can include a body frame, and a plurality of positioning assemblies coupleable to cables and configured to position and/or orient the body frame on the surface. The arm assembly includes a proximal end portion rotatably coupled to the body portion and a distal end portion opposite the proximal end portion. The material handling assembly is coupled to the distal end portion of the arm assembly and is configured to carry a surface material to be positioned on the angled surface.

Abstract: In some examples, an autonomous robotic welding system comprises a workspace including a part having a seam, a sensor configured to capture multiple images within the workspace, a robot configured to lay weld along the seam, and a controller. The controller is configured to identify the seam on the part in the workspace based on the multiple images, plan a path for the robot to follow when welding the seam, the path including multiple different configurations of the robot, and instruct the robot to weld the seam according to the planned path.

Abstract: Inspection robot and methods utilizing coolant for temperature management are described. An example inspection robot may include a housing with a couplant retaining chamber, and an electronic board selectively thermally coupled to the couplant retaining chamber. The inspection robot may include a couplant input port coupling a couplant source to a couplant flow path, a drive module coupled to the housing, and a payload with at least one sensor, where the payload is coupled to the housing. The couplant flow path is fluidly coupling the couplant input port to the couplant retaining chamber.

Abstract: Data management of vehicle data for communication to a remote location is provided, including obtaining one or more sensor data feeds from one or more onboard sensors of a vehicle, generating a forecast network condition for a network with which the vehicle is in operative communication, the forecast network condition representing predicted network resources the network is predicted to provide to the vehicle, providing the forecast network condition to a compression engine, and compressing the one or more sensor data feeds into a compressed data feed for communication over the network to a remote location, wherein the compressed data feed is compressed based on the forecast network condition.

Abstract: A mobile printing robot includes a windbreak to reduce wind-induced deflection of ink droplets emitted from a printhead of the mobile printing robot. The printhead may have a comparatively large throw height to aid in permitting obstacles, such as particles from safely passing under the printhead without damaging the printhead or cause the printhead to become stuck. The windbreak may be implemented using resiliently compliant sections that block the wind but accommodate the passage of particles or other obstacles.

Abstract: A vehicle can receive audio data indicating speech from a citizens band (CB) radio channel. The vehicle can determine a location corresponding to information in the speech. The vehicle can determine an event corresponding to the location based on the information in the speech. The vehicle can automatically adjust operation according to the event and the location.

Abstract: A system can receive audio data indicating speech from a citizens band (CB) radio channel. The system can determine a location corresponding to first information in the speech. The system can determine an event at the location based on the first information. The system can predict one or more autonomous vehicles that have a path including the location. The system can transmit second information containing the event and the location to the one or more autonomous vehicles. The second information can cause the one or more autonomous vehicles to adjust operation according to the event and the location.

Abstract: Embodiments are directed to parameter adjustment for sensors. A calibration model and a calibration profile for a sensor may be provided. Calibration parameters associated with the sensor may be determined based on the calibration profile. The sensor may be configured to use a value of the calibration parameter based on the calibration profile. Trajectories may be generated based on a stream of events from the sensor. Metrics associated with the sensor events or the trajectories may be determined. If a metric value may be outside of a control range, further actions may be iteratively performed, including: modifying the value of the calibration parameter based on the calibration model; configuring the sensor to use the modified value of the calibration parameter; redetermining the metrics based on additional trajectories; if the metric is within a control range, the iteration may be terminated and the calibration profile may be updated.