基于 MASAC 强化学习算法的多无人机协同路径规划

This paper proposes a novel multi-agent deep reinforcement learning algorithm for the collaborative path planning problem of heterogeneous unmanned aerial vehicles (UAVs) in a dynamic uncertain environment. Firstly, a reinforcement learning environment for UAVs is developed to reach a target location in an airspace scenario, where the environment introduces the UAV dynamics equations and considers the UAV heterogeneity as well as the requirement for safe obstacle avoidance. Secondly, evaluation metrics including task completion rate, formation maintenance rate, flight time, flight trajectory, and energy consumption are designed to evaluate the algorithm performance. Then, the multi-UAV collaborative path planning problem is modeled as a partially observable Markov decision process and a multi-agent soft actor critic algorithm is proposed to seek the approximate optimal strategy for the problem. Finally, the effectiveness and superiority of the proposed algorithm are demonstrated through simulations.