基于双中心化Q网络框架的多无人机路径规划算法OA

Path planning,as the core component enabling autonomous collaborative operation in multi-UAV systems,directly determines the execution efficiency and safety of cluster missions.This paper investigates the homogeneous multi-UAV path planning problem and proposes a multi-agent reinforcement learning algorithm based on a dual-centralized Q-network framework to address this challenge.Initially,through modeling UAVs and obstacles.This paper analyzes collision risks,motion continuity constraints,and mission execution requirements during UAV flight,formulating the multi-UAV path planning problem as a multi-constrained combinatorial optimization problem with high computational complexity.Subsequently,inspired by the twin delayed deep deterministic policy gradient algorithm,this paper develops a multi-agent reinforcement learning approach employing a dual-centralized Q-network framework to automatically generate feasible and collision-free flight paths for each UAV.Simulation experiments validate the algorithm's effectiveness in multi-UAV path planning,providing an efficient solution for autonomous path planning in UAV swarms.