A UAV includes a body and rotor coupled to the body. The UAV may include a boom coupled to the body, and a nozzle coupled to a distal end of the boom, wherein an operational configuration of the nozzle is responsive to a second control signal. The rotor, boom, and nozzle are arranged such that the nozzle is disposed further away from the body than the rotor. The UAV may further include a sensor disposed on either the body or the boom, wherein the sensor is configured to generate a detection signal associated with a distance between the sensor and a surface disposed proximate to the sensor.

Provided are a method, system and device for global path planning for an unmanned vehicle in an off-road environment. The method includes: obtaining satellite elevation data and a satellite remote sensing image of a current off-road environment; constructing a digital elevation model (DEM); determining slope and land surface relief of each grid in the current off-road environment; performing gray processing on the satellite remote sensing image to obtain grayscale values of the grids; determining traversal costs of the grids corresponding to different ground types; constructing a global grid map based on the slope and the land surface relief of each grid, as well as the traversal costs corresponding to the different ground types; determining a rugged terrain potential field and path costs; and searching for paths using a Bresenham's line algorithm and Theta* algorithm based on the rugged terrain potential field and the path costs, to generate a global path.

The disclosure relates to an autonomously driving vehicle for traveling on an unpaved terrain section, comprising an assessment device and a control device, wherein the assessment device has a soil condition determination device for determining a condition parameter which is representative of the current soil condition of the terrain section, a storage device for storing a drivability dependence of acquired historic slipping data of the vehicle on the condition parameter, and an evaluation device, which determines a discrete drivability prediction of the terrain section by the vehicle based on the drivability dependence and the determined condition parameter. The control device is designed to control the vehicle based on the drivability prediction to drive on the terrain section. Furthermore, the disclosure relates to a computer-implemented control method for controlling the autonomously driving vehicle and a computer program product.

A method for controlling an off-road vehicle relative to a secondary off-road vehicle. The method, executed by a processor of the vehicle, includes the steps of receiving an input from the secondary off-road vehicle, determining a predicted trajectory path of the vehicle; determining a trajectory position of the vehicle, the trajectory position corresponding to a point of interest related to a distance between the vehicle and the secondary off-road vehicle on the predicted trajectory path; determining a separation distance between the secondary off-road vehicle and the trajectory position; and in response to the separation distance being less than a distance threshold, controlling a speed of the vehicle.

An autonomous environmental perception, path planning and dynamic landing method includes: obtaining three-dimensional environment information in real time; determining a global starting point and a global end point, and generating an initial path; optimizing the initial path based on a local path optimization algorithm to obtain a first optimized path; when a perception threshold of the current position of the unmanned aerial vehicle is greater than a preset threshold, optimizing the initial path based on a frontier-perceived path optimization method to obtain a second optimized path and a local end point; when the unmanned aerial vehicle advances to the local end point, switching to optimizing the initial path in real time based on the local path optimization algorithm; and when the unmanned aerial vehicle arrives at the global end point, carrying out dynamic landing based on a deep reinforcement learning algorithm.

Systems and methods for controlling an aerial drone for exploring off-road trails are disclosed herein. In one example, a system includes a processor and a memory in communication with the processor. The memory includes instructions that cause the processor to determine, based on images of an off-road trail captured by an aerial drone in communication with a vehicle, whether the off-road trail has a positive condition indicating that the vehicle can traverse the off-road trail or an abandonment condition indicating that the vehicle cannot traverse the off-road trail. Depending on whether the off-road trail has a positive condition or an abandonment condition, the processor either controls the aerial drone to navigate along the off-road trail or controls the aerial drone to navigate to a different off-road trail.

According to an embodiment, a control device controls a user-wearable flight device and includes a processing unit configured to acquire state data related to a state of the flight device and manipulation data related to a manipulation of the flight device, input the acquired state data and the acquired manipulation data to a model trained using deep reinforcement learning, and control the flight device on the basis of an output result of the model to which the state data and the manipulation data are input.

A global path generation method for a wide-area off-road environment in which an unmanned vehicle performs autonomous driving includes generating an occupancy grid map for a driving area through a sensor, converting the occupancy grid map into a distance map, generating a plurality of nodes by sampling unit grids that are are randomly and uniformly distributed in the driving area of the distance map, generating a plurality of links connecting the plurality of nodes, receiving a destination position of the unmanned vehicle, and generating a global path by connecting optimal links for arriving at the destination position among the plurality of links.

A ride system may include a track including uneven terrain, a plurality of ride vehicles positioned on the track, and a fleet controller (e.g., a wayside controller). The ride vehicles may be adapted to traverse the uneven terrain, such as along a respective chosen path of multiple paths based on respective user control. The user control may be configured to select the chosen path and adjust a speed and a direction of the ride vehicle along the chosen path. The fleet controller may provide an override control of the ride vehicles along the track based on the chosen path, the speed, and the direction of the ride vehicles. The fleet controller may define a default position and pacing for the ride vehicles. The user control may be configured to adjust the position and pacing of an associated ride vehicle from the default position and pacing, respectively.

A mapping system for an autonomous mobile robot includes a 3D convolutional encoder network that generates 3D feature maps from 3D point cloud data. The network sequentially compresses the feature dimension of the 3D input data to reduce the computational complexity and enable feature extraction to be performed in substantially real-time. Skip connections connect the outputs of the encoder layers of the convolutional encoder network to counterpart decoder layers of a 2D convolutional decoder network. An attention-based 3D to 2D projection layer receives the 3D feature maps generated by the encoder layers via the skip connections and projects the 3D feature maps onto 2D BEV feature maps which are provided to the counterpart decoder layers as input. The projection layer automatically estimates ground level of 3D feature maps and filters out overhanging objects that are irrelevant to ground-level navigation.