ALBERT

Beschreibung: Unmanned aerial vehicle (UAV) detection has the advantages of flexible deployment and no casualties. It has become a force that cannot be ignored in the battlefield. Scientific and efficient mission planning can help improving the survival rate and mission completion rate of the UAV search in dynamic environments. Towards the mission planning problem of UAV collaborative search for multi-types of time-sensitive moving targets, a search algorithm based on hybrid layered artificial potential fields algorithm (HL-APF) was proposed. This method consists of two parts, a distributed artificial field algorithm and a centralized layered algorithm. In the improved artificial potential field (IAPF), this paper utilized a new target attraction field function which was segmented by the search distance to quickly search for dynamic targets. Moreover, in order to solve the problem of repeated search by the UAV in a short time interval, a search repulsion field generated by the UAV search path was proposed. Besides, in order to solve the unknown target search and improve the area coverage, a centralized layered scheduling algorithm controlled by the cloud server (CS) was added. CS divides the mission area into several sub-areas, and allocates UAV according to the priority function based on the search map. The CS activation mechanism can make full use of prior information, and the UAV assignment cool-down mechanism can avoid the repeated assignment of the same UAV. The simulation results show that compared with the hybrid artificial potential field and ant colony optimization and IAPF, HL-APF can significantly improve the number of targets and mission area coverage. Moreover, comparative experiment results of CS mechanism proved the necessity of setting CS activation and cool-down for improving the search performance. Finally, it also verified the robustness of the method under the failure of some UAVs.