基于强化学习方法的RRT全局路径规划算法OA

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.