A method of determining a flight path for an aerial vehicle, includes controlling the aerial vehicle to fly along a first route, identifying, during the flight along the first route and with aid of one or more processors, a change in a state of signal transmission occurring at a first location, in response to identifying the change, determining, by the one or more processors, a second location different from the first location, determining a second route to the second location, and controlling, by the one or more processors, the aerial vehicle to fly to and land at the second location. The change of the state of signal transmission indicates an abnormal state in a signal transmission between the aerial vehicle and a control device.

The present disclosure discloses a method and system for assisting a robot to navigate in a warehouse setting. In some embodiments, robot is configured to receive a reserved route and move according to the reserved route as it navigates inside a warehouse. A reserved route includes one or more waypoints. Each waypoint represents a location and a size, the size being the space needed for accommodating a robot at the location. The one or more waypoints are listed in a sequence for a robot to follow sequentially. In some embodiments, each waypoint may further include a timestamp representing the time that the robot given the reserved route is supposed to arrive at the location.

A system and method for implementing pedestrian avoidance strategies for a mobile robot that include receiving position data of a pedestrian and the mobile robot from systems of the mobile robot and estimating positions of the pedestrian and the mobile robot based on the position data. The system and method also include determining an expected intersection point of paths of the pedestrian and the mobile robot and an estimated time for the pedestrian to reach and cross the expected intersection point of the paths. The system and method further include implementing a pedestrian avoidance strategy based on the positions of the pedestrian and the mobile robot and the expected point in time when the pedestrian will reach and cross the expected intersection point of the paths.

An autonomous running vehicle transmits a camera image around the vehicle photographed by a camera to a remote monitoring center. An obstacle is detected on the basis of information obtained from autonomous sensors including the camera. When an obstacle is detected, the autonomous running vehicle is automatically stopped. The remote monitoring center determines, when the autonomous running vehicle automatically stops, whether or not the run of the autonomous running vehicle is permitted to restart on the basis of the received camera video. When it is determined that the autonomous running vehicle can be restarted, a departure signal is transmitted to the autonomous running vehicle. When the departure signal is received from the remote monitoring center, the autonomous running vehicle restarts running.

A system and a method are disclosed that generate for display to a remote operator a user interface comprising a map, the map comprising visual representations of a source area, a plurality of candidate robots, and a plurality of candidate destination areas. The system receives, via the user interface, a selection of a visual representation of a candidate robot of the plurality of candidate robots, and detects a drag-and-drop gesture within the user interface of the visual representation of the candidate robot being dragged-and-dropped to a visual representation of a candidate destination area of the plurality of candidate destination areas. Responsive to detecting the drag-and-drop gesture, the system generates a mission, where the mission causes the candidate robot to autonomously transport an object from the source area to the candidate destination area.

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

In a general aspect, a handheld controller device includes a housing and a trigger assembly. The housing is configured to be held in the hands of a user. The trigger assembly includes a pair of triggers extending outward from a side of the handheld controller device and configured to move along respective trigger paths. A coupling assembly is disposed inside the housing and connected to the pair of triggers. The coupling assembly is configured to transfer motion between the pair of triggers such that, when either of the triggers moves towards the housing along its respective trigger path, the coupling assembly moves the other trigger an equal distance away from the housing along its trigger path. Circuitry in the housing includes one or more sensors and a microcontroller configured to receive sensor signals and, in response, generate aircraft control data (e.g., for a flight simulation or remotely controlled flyable aircraft).

Systems and methods for path planning with latent state inference and spatial-temporal relationships are provided. In one embodiment, a system includes an inference module, a policy module, a graphical representation module, and a planning module. The inference module receives sensor data associated with a plurality of agents. The inference module maps the sensor data to a latent state distribution to identify latent states of the plurality of agents. The latent states identify agents as cooperative or aggressive. The policy module predicts future trajectories of the plurality of agents at a given time based on sensor data and the latent states of the plurality of agents. The graphical representation module generates a graphical representation based on the sensor data and a graphical representation neural network. The planning module generates a motion plan for the ego agent based on the predicted future trajectories and the graphical representation.

In one embodiment, a method includes, by a computing system associated with a vehicle, determining a current location of the vehicle in a first region, identifying one or more first sets of model parameters associated with the first region and one or more second sets of model parameters associated with a second region, generating, using one or more machine-learning models based on the first sets of model parameters, one or more first inferences based on first sensor data captured by the vehicle, switching the configurations of the models from the first sets of model parameters to the second sets of model parameters, generating, using the models having configurations based on the second sets of model parameters, one or more second inferences based on second sensor data generated by the sensors of the vehicle in the second region, and causing the vehicle to perform one or more operations based on the second inferences.

An amphibious vehicle having a frame that includes a plurality of floatable members. Mounted to the frame is one or more power sources. Also mounted to the frame and connected to the power source are a plurality of propellers with each of the plurality of propellers having a thrust vector configured to be adjusted to provide agitation and propulsion. In addition, mounted to the frame are a plurality of ground engaging devices and one or more pumps.