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.

A HAPS platform may execute a neural network (a HAPSNN) as it monitors air traffic; the neural network enables it to classify, predict, and resolve events in its airspace of coverage in real time as well as learn from new events that have never before been seen or detected. The HAPSNN-equipped HAPS platform may provide surveillance of nearly 100% of air traffic in its airspace of coverage, and the HAPSNN may process data received from a drone to facilitate safe and efficient drone operation within an airspace.

An example includes a method for training an agent to control an aircraft. The method includes: selecting, by the agent, first actions for the aircraft to perform within a first environment respectively during first time intervals based on first states of the first environment during the first time intervals, updating the agent based on first rewards that correspond respectively to the first states, selecting, by the agent, second actions for the aircraft to perform within a second environment respectively during second time intervals based on second states of the second environment during the second time intervals, and updating the agent based on second rewards that correspond respectively to the second states. At least one first rule of the first environment is different from at least one rule of the second environment.

Provided is a channel monitoring method, an electronic device, and a storage medium. The method includes: obtaining scan data collected by a vehicle body collection component of an automated guided vehicle (AGV) in an area around a vehicle body; obtaining video data collected by a camera in a video collection area, where the camera is one of a plurality of cameras and is used to collect video of at least a partial area of the channel to be monitored of the plurality of channels to be monitored; in a case of determining an existence of a target object based on the scan data collected by the vehicle body collection component and/or the video data collected by the camera, obtaining a target channel where the target object is located; and generating target warning information for the target channel where the target object is located.

A method of generating a flight plan for an airborne vehicle. The method includes identifying a region of operation of the airborne vehicle and identifying high-intensity radiated field (HIRF) sources associated with the region of operation of the airborne vehicle. A radiation tolerance level is obtained for the airborne vehicle for determining a HIRF stand-off zone for each of the HIRF sources based on the radiation tolerance level of the airborne vehicle and a potential radiated field generated by the HIRF source. The flight plan is generated for the airborne vehicle based on the HIRF stand-off zones within the region of operation.

A technique relates to route planning. Points associated with a start location and a destination location are received. It is determined whether the points comprise one or more hard points, one or more soft points, or both. At least one obstruction to avoid is determined. A route is autonomously generated comprising the points and segments associated with the points, the segments comprising rhumb lines, great-circle arcs, or both. Responsive to the points comprising the one or more soft points, the at least one obstruction is avoided by permitting a distance associated with the one or more soft points to be reached and adding at least one free waypoint. Responsive to the points comprising the one or more hard points, the at least one obstruction is avoided by requiring the one or more hard points to be reached and adding the at least one free waypoint.

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.

A parallel manipulator with six degrees of freedom may include a base that attaches to a unmanned aerial vehicle and a movable gripper element that may be positioned below the UAV. The positioning of the gripper element my reduce impact of the center of gravity of the attached UAV. The gripper element may include a geometric shape that complements objects routinely used in high-throughput screening (HTS) laboratories, such as microplates. The parallel manipulator and gripper element may be used to quickly, safely, and securely move objects in HTS laboratories and/or the like.

A method for determining the orientation of a surface of an object in a detection region before or behind a vehicle by means of a camera of the vehicle comprises the following steps: detecting a first image of the detection region by means of the camera, detecting a second image of the detection region following in time on the detecting of the first image and by means of the camera, generating of first image data corresponding to the first image and second image data corresponding to the second image, determining of eight image coordinates of four pixels each in the first image and the second image, corresponding to four points on the surface of the object, by means of the first image data and the second image data, determining of a normal vector of the surface of the object by means of the eight image coordinates, determining of the orientation of the surface of the object by means of the normal vector.

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).