An intelligent cooperative mission planning scheme of UAV swarm in uncertain dynamic environment

This paper presents an intelligent cooperative mission planning scheme for unmanned aerial vehicle (UAV) swarm, to search and attack the time-sensitive moving targets in uncertain dynamic environment, by using a hybrid artificial potential field and ant colony optimization (HAPF-ACO) method. In the search-attack mission environment of UAV swarm under the dynamic topology interaction, a time-sensitive target probability map is established. Based on the HAPF, the target attraction field, threat repulsive field and repulsive field are constructed for the environmental cognition. A distributed ACO algorithm is designed to improve the UAVs' global searching capability. For this mission planning problem, four time-sensitive moving target types and four constraint types of UAV swarm are considered, which will contribute to the practical applications of the HAPF-ACO. Several simulations are carried out to exhibit the superiority on the task execution efficiency and obstacle and collision avoidance performance of the proposed intelligent cooperative mission planning scheme.