Abstract: [Purposes]To optimize the task completion time of multi-UAV collaborative operations and enhance the efficiency of path planning,a multi-UAV collaborative path planning method based on the double deep Q-network(DDQN)algorithm is proposed.[Methods]Firstly,an efficient environmental information map fusion technology is designed,which can quickly and efficiently mark the coverage records of each UAV and detect the positions of obstacles.Secondly,the DDQN algorithm is introduced to minimize the task time,avoid path overlap,area omission and potential collisions.Additionally,a new collaborative learning mechanism is constructed,which can efficiently plan the globally optimal path.Finally,a simulation platform is built to simulate the proposed method and two typical methods are selected for comparative analysis.[Findings]The experimental results show that compared with the other two comparison methods,the proposed method demonstrates superior performance in terms of task completion time and coverage efficiency.[Conclusions]The proposed method can quickly adapt to unknown obstacles and complex environments,achieve efficient and comprehensive coverage of the target area in various task scenarios,and has good response speed and coverage rate.

Key words: path planning, multi-UAV, double deep Q-network algorithm, global search