A drone comprising a camera and a controller. The camera is configured to output data representing an object within a field of view of the camera. The controller is configured to attempt to maintain a visual line of sight with the object. The controller is also configured to cause control equipment of an operator of the drone to notify the operator of the drone, visually, audibly and/or haptically, as to whether or not the object is being tracked by the drone.

Techniques for traversing in an environment that includes at least one obstacle, by a mobile autonomous system, to a destination in the environment, are presented. The techniques can include generating, prior to the mobile autonomous system commencing activity in the environment, a graph including a plurality of vertices representing positions in the environment and a plurality of edges between vertices representing feasible transitions by the mobile autonomous vehicle in the environment; annotating the graph with at least one edge connecting a representation of a present position of the mobile autonomous system to a vertex of the graph; determining, based on the graph, a path from the present position of the mobile autonomous system in the environment to the destination; and traversing the environment to the destination, by the mobile autonomous system, based on the path.

Flight type determination unit determines a flight type of drone on the basis of parameters obtained by flight schedule obtainment unit. The flight type includes a travel type, which uses a destination as a parameter, a touring type, which uses a range in space (a touring range) as a parameter, and a hovering type, which uses a position in space (a hovering position) as a parameter. Allocation rule storage unit stores allocation rules for flight airspace corresponding to each of the plurality of flight types. Flight airspace allocation unit allocates flight airspace to drone on the basis of allocation rules corresponding to the flight type of that drone as indicated by the parameters obtained for that drone.

The present disclosure relates to a dataflow optimization method for low-power operation of a multicore system, the dataflow optimization method including: a step (a) of creating an FSM including a plurality of system states in consideration of dynamic factors that trigger a transition in system states for original dataflow; and a step (b) of optimizing the original dataflow through optimization of the created FSM.

The disclosure provides a dynamic recovery method and system for UAVs and storage medium. On the basis of obtaining the environmental information of UAVs, combined with different factors such as the following task quantity of each UAV, the distance from the current position of each UAV to the recovery point, current performance of each UAV, total cost function is constructed, and optimization is carried out through improved particle swarm optimization algorithm. Then on the premise of the lowest recovery cost, the priority of each UAV is obtained. For the ordered UAV, the route to the recovery point is determined. At the same time, it is necessary to consider conflict resolution of UAVs when encountering obstacles in the flight process, as well as the possible failure in line recovery. At this time, it is necessary to reorder some hovering UAVs to ensure flight safety of UAVs and robustness of the algorithm.

A system and method for cooperative aerial vehicle collision avoidance provides an ESA-based sensor network capable of high-resolution threat proximity measurements and cooperative and non-cooperative collision avoidance in the full spherical volume surrounding an aerial vehicle. The system incorporates a plurality of ESA panels onto the airframe where the conical scan volumes overlap leaving no gaps in spherical proximity coverage. The resulting received data is stitched together between the neighboring ESA panels and used to determine a position and vector for each threat aerial vehicle within range. The data is transmitted through a cooperative collision avoidance network to nearby aerial vehicles, and presented to the autopilot and flight crew to increase situational awareness. The system determines a maneuver for the aerial vehicle and a maneuver for the threat aerial vehicle based on relative maneuvering capabilities to maintain desired separation.

A Internet protocol-based unmanned aerial vehicle (UAV) management system and method is disclosed that includes UAVs including a communication chip, a user computer, a cloud-based service for performing a virtual UAV. The method includes storing a plurality of pre-planned missions, controlling communication between the UAV and the user computer, mapping the UAV to the virtual UAV, assigning a mission to include multiple waypoints, and allocating the task at the multiple waypoints. Dynamic mission planning of the assigned mission is performed to generate planned paths for performing the task. Operation of the UAV is controlled by way of the corresponding virtual UAV including receiving messages and commands for the mission from the user computer, sending control commands to the UAV, receiving data signals from the UAV, and transmitting location and status of performance of the task for the UAV. The cloud-based service performs image processing using the received data signals.

A configuration is achieved in which collision risk information is received from a mobile device such as a drone, a modified path with a low collision risk is generated, and movement according to the modified path is performed. Existence of a second mobile device or pedestrian exposed to a collision risk is checked on the basis of collision risk information received from the mobile device such as a drone, and in a case where the existence of the second mobile device or pedestrian exposed to the collision risk is confirmed, collision risk information received from a first mobile device or modified safe circuit information is transmitted to the second mobile device exposed to the collision risk, or transmitted to a user terminal held by the pedestrian exposed to the collision risk. The collision risk information received from the mobile device such as a drone is risk information with which a collision risk corresponding to a three-dimensional spatial position can be analyzed.

Method and device for determining trajectory to optimal position of a follower aircraft with respect to vortices generated by a leader aircraft. The method includes controlling trajectory of a follower aircraft to an optimal position where the follower aircraft benefits from effects of at least one of the vortices of a leader aircraft. A first section control step controls flight of the follower aircraft using current measurements of flight parameters, from a safety position to a search position, along an approach section passing through an approach zone. A second section control step controls flight of the follower aircraft using current measurements of flight parameters, from the search position to a precision position, along a search section passing through a search zone, and a third section control step controls flight of the follower aircraft, from the precision position to the optimal position, along an optimization section passing through an optimization zone.

A distribution device (10) is provided with an acquisition unit (11) which acquires position information of an obstacle, and a distribution unit (12) which wirelessly distributes, from the obstacle to an aircraft, obstacle information including the acquired position information of the obstacle and identification information of the obstacle. The distribution device (10) distributes the identification information of the obstacle along with the position information of the obstacle to the aircraft, and the aircraft interprets the position, etc., of the specific obstacle.