An unmanned aerial vehicle (UAV) or drone executes a neural network to assist with detecting and responding to attacks. The neural network may monitor, in real time, the data stream from a plurality of onboard sensors during navigation and may communicate with a high-altitude pseudosatellite (HAPS) platform. For example, if the neural network detects a cyber-attack but determines that it does not interfere with external communications, it may shift navigation control of the drone to the HAPS.

The present disclosure relates to a method for forecasting a motion trajectory, a computer-readable storage medium, and a computer device. The method includes: obtaining an observed past trajectory of an object; obtaining a spatial pointwise feature of each trajectory point in the observed past trajectory; obtaining a temporal pointwise feature of the trajectory point according to the spatial pointwise feature of the trajectory points within a preset observation time interval; and performing motion trajectory prediction on the object according to the spatial pointwise feature and the temporal pointwise feature of the trajectory points. The present disclosure promotes the accuracy of the motion trajectory prediction.

A robot includes an image sensor that captures the environment of a storage site. The robot visually recognizes regularly shaped structures to navigate through the storage site using various object detection and image segmentation techniques. In response to receiving a target location in the storage site, the robot moves to the target location along a path. The robot receives the images as the robot moves along the path. The robot analyzes the images captured by the image sensor to determine the current location of the robot in the path by tracking a number of regularly shaped structures in the storage site passed by the robot. The regularly shaped structures may be racks, horizontal bars of the racks, and vertical bars of the racks. The robot can identify the target location by counting the number of rows and columns that the robot has passed.

A robot includes an image sensor that captures the environment of a storage site. The robot visually recognizes regularly shaped structures to navigate through the storage site using various object detection and image segmentation techniques. In response to receiving a target location in the storage site, the robot moves to the target location along a path. The robot receives the images as the robot moves along the path. The robot analyzes the images captured by the image sensor to determine the current location of the robot in the path by tracking a number of regularly shaped structures in the storage site passed by the robot. The regularly shaped structures may be racks, horizontal bars of the racks, and vertical bars of the racks. The robot can identify the target location by counting the number of rows and columns that the robot has passed.

The present application is to provide a system for sensing and responding to a lateral blind spot of a mobile carrier and method thereof, which is applied for a mobile carrier during moving to a parking place. Firstly, a light scan unit and a depth image capture unit are used to scan a plurality of surrounding objects and capture a plurality of object depth images of the surrounding objects, and then a plurality of screened images are obtained according to a moving route of the mobile carrier for further obtaining correspondingly a plurality of forecasted lines to generate corresponded notice message for noting driver or ADAS. Due to the objects corresponding to the screened images and located on a blind position which is at one side of the mobile carrier, the notice message provides the driver preventing from the ignored danger by ignoring the blind position.

A control system for an autonomous work machine includes a robotic controller, a position detection system coupled to the robotic controller, and a sensor coupled to the robotic controller and configured to provide a sensor signal. A controlled system is coupled to the robotic controller to receive control signals from the robotic controller. The robotic controller is configured to generate an event relative to an object in an environment around the autonomous work machine or a machine health/job quality issue, document the event, and store the documented event. The robotic controller is further configured to selectively generate a communication containing at least some information relative to the documented event to a supervisor and to receive user input from the supervisor and take responsive action based on the user input.

Methods and devices for actively modifying a field of view of an autonomous vehicle in view of constraints are disclosed. In one embodiment, an example method is disclosed that includes causing a sensor in an autonomous vehicle to sense information about an environment in a first field of view, where a portion of the environment is obscured in the first field of view. The example method further includes determining a desired field of view in which the portion of the environment is not obscured and, based on the desired field of view and a set of constraints for the vehicle, determining a second field of view in which the portion of the environment is less obscured than in the first field of view. The example method further includes modifying a position of the vehicle, thereby causing the sensor to sense information in the second field of view.

An unmanned aerial vehicle (UAV) or drone executes a neural network to assist with inspection, surveillance, reporting, and other missions. The drone inspection neural network may monitor, in real time, the data stream from a plurality of onboard sensors during navigation to an asset along a preprogrammed flight path and/or during its mission (e.g., as it scans and inspects an asset).

An information processing device includes a route information acquisition unit configured to acquire information about a route on which a movable apparatus moves; an environment information acquisition unit configured to acquire environment information on the route for moving the movable apparatus; an estimation unit configured to estimate a self-position and orientation of the movable apparatus and store a result of the estimation in the environment information; an accuracy calculation unit configured to calculate an accuracy for estimating a self-position and orientation of the movable apparatus on the basis of the environment information; and a route setting unit configured to update a route of the movable apparatus according to an accuracy calculated by the accuracy calculation unit.

According to the embodiments described herein, system and methods for determining relative pose of materials handling vehicles in an industrial environment may include utilizing ultra-wideband (UWB) antenna array systems respective mounted on the materials handling vehicles to send mutually received information to determine the relative pose between the vehicles, determining one or more fields of each materials handling vehicle, and determining one or more overlapping fields between the materials handling vehicles based on the determined one or more fields and the relative pose. A vehicle control may be implemented based on the determined relative pose and the overlapping fields as a field enforcement, such as a control action to avoid collision between the vehicles.