动态环境下基于人工势场引导的RRT路径规划算法.

Most of the existing dynamic RRT path planning algorithms cannot keep the planned path away from obstacles,which may cause the robot to have insufficient time to avoid obstacles.To solve this problem,this paper proposed an improved RRT,denoted as APFG-RRT,which utilized artificial potential fields to guide the RRT grow to goal and away from obstacles.In order to further increase the convergence rate and speed up the jump out of local minima,it introduced a strategy of selecting the goal as the random sample at an adaptive probability.Finally,it adopted global planning combined with local replanning to improve its real-time performance in dynamic environment.Simulation experiments show that APFG-RRT has higher computational efficiency and lower memory requirements compared with the initial RRT and Goal-bias RRT,and the given path can be effectively away from obstacles,which improves the success rate of dynamic path planning. [ABSTRACT FROM AUTHOR]