无线电工程2024，Vol.54Issue(7)：1816-1823,8.DOI:10.3969/j.issn.1003-3106.2024.07.025
基于改进MAAC算法的多无人机自主路径规划
Multi-UAV Autonomous Path Planning Based on Improved MAAC Algorithm
摘要
Abstract
Deep reinforcement learning methods are used in multi-UAV autonomous path planning in threat area environments.In order to solve the common problem of difficult convergence in reinforcement learning algorithms,an improved Actor-Attention-Critic for Multi-Agent Reinforcement Learning(MAAC)algorithm is proposed for multi-UAV autonomous path planning.The Markov decision process of reinforcement learning is defined by modeling the multi-UAV potential field environment to provide a reasonable collision-free path planning in dynamic environment.Simulation experiments validate the effectiveness of the proposed algorithm,and verify its superior performance in terms of convergence speed and collision avoidance through comparative simulations.关键词
无人机/多智能体深度强化学习/自主路径规划/MAAC算法Key words
UAV/multi-agent deep reinforcement learning/autonomous path planning/MAAC algorithm分类
航空航天引用本文复制引用
周从航,李建兴,石宇静,林致睿..基于改进MAAC算法的多无人机自主路径规划[J].无线电工程,2024,54(7):1816-1823,8.基金项目
福建省自然科学基金(2020J01876) （2020J01876）
福建工程学院科研启动基金(GY-Z21215,GY-Z21216)Fujian Provincial Natural Science Foundation of China(2020J01876) （GY-Z21215,GY-Z21216）
Science Research Foundation for Introduced Talents,Fujian Province of China(GY-Z21215,GY-Z21216) （GY-Z21215,GY-Z21216）
