Mission Planning for Unmanned Aerial Vehicles Based on Voronoi Diagram‐Tabu Genetic Algorithm
Wei Tan, Yong-jiang Hu, Yuefei Zhao, Wenguang Li, Xiaomeng Zhang, Yong-ke Li
Abstract
Unmanned aerial vehicles (UAVs) are increasingly used in different military missions. In this paper, we focus on the autonomous mission allocation and planning abilities for the UAV systems. Such abilities enable adaptation to more complex and dynamic mission environments. We first examine the mission planning of a single unmanned aerial vehicle. Based on that, we then investigate the multi‐UAV cooperative system under the mission background of cooperative target destruction and show that it is a many‐to‐one rendezvous problem. A heterogeneous UAV cooperative mission planning model is then proposed where the mission background is generated based on the Voronoi diagram. We then adopt the tabu genetic algorithm (TGA) to obtain multi‐UAV mission planning. The simulation results show that the single‐UAV and multi‐UAV mission planning can be effectively realized by the Voronoi diagram‐TGA (V‐TGA). It is also shown that the proposed algorithm improves the performance by 3% in comparison with the Voronoi diagram‐particle swarm optimization (V‐PSO) algorithm.