Abstract: To address the problem of path planning for multiple unmanned aerial vehicles(UAVs) in three-dimensional unknown obstacle environments when pursuing dynamic targets,this paper proposes a path planning algorithm based on an improved due-ling deep Q network(Dueling-DQN) combined with the artificial potential field method and deep reinforcement learning algorithm.This is aimed at solving the problem of path planning for multiple UAVs cooperating to capture dynamic targets.Firstly,it incorporates the idea of the artificial potential field method into the training reward function for multiple UAVs cooperating to capture dynamic targets,which not only addresses the shortcomings of traditional artificial potential field methods in complex environments where they are prone to local optima,but also solves the problems of multi-UAV cooperation and UAV obstacle avoidance in complex environments.Additionally,to facilitate better cooperation among UAVs in capturing dynamic targets,a strategy for the capture and escape of dynamic targets by multiple UAVs is designed.Simulation results demonstrate that compared to Dueling-DQN algorithm,the proposed APF-Dueling-DQN algorithm effectively reduces the probability of collisions du-ring UAV trajectory planning tasks and shortens the planned path length required to capture dynamic targets.

Key words: UAVs, Dueling-DQN, Path planning, Obstacle avoidance, Artificial potential field, Capture and escape