四川轻化工大学学报（自然科学版）2024，Vol.37Issue(2)：57-63,7.DOI:10.11863/j.suse.2024.02.08
基于强化学习方法的RRT全局路径规划算法
RRT Global Path Planning Algorithm Based on Reinforcement Learning Method
摘要
Abstract
Aiming at the situation that reinforcement learning to local path planning is not clear about the target direction and easy to fall into local optimality,and the rapidly-exploring random tree(RRT)algorithm has complex planning paths and lots of redundant points,a global path planning integrating RRT algorithm and reinforcement learning(RL)ideas has been proposed.Firstly,the RRT global path planning algorithm is used to weaken and reduce the RL algorithm to avoid falling into the problem of local optimum,which can reduce the planning iteration time to some extent.Secondly,the maximum reward mechanism of the reinforcement learning algorithm is used to strengthen the purpose of the RRT algorithm when selecting child nodes in the path planning process,so as to avoid too many random points.The experimental results suggest that the proposed algorithm weakens the influence of local optimization,shortens the path length by 33.3,increases the proportion of effective nodes in uneven and convex terrain by 36.0%and 39.6%,respectively,reflecting the reduction of redundant points on the side,which verifies the feasibility of the algorithm.关键词
强化学习/快速探索随机树/回报奖励机制/全局路径规划Key words
reinforcement learning/rapidly-exploring random tree/reward mechanism/global path planning分类
信息技术与安全科学引用本文复制引用
罗国攀,张国良,杨敏豪..基于强化学习方法的RRT全局路径规划算法[J].四川轻化工大学学报（自然科学版）,2024,37(2):57-63,7.基金项目
四川省应用基础研究项目(2019YJ00413) （2019YJ00413）
