一种基于动态步长的AAPF-RRT*移动机器人路径规划新算法

A novel path planning method of mobile robot based on AAPF-RRT*with dynamic step

针对改进快速搜索随机树(rapidly-exploring random tree,RRT*)算法中节点盲目扩展和收敛速率慢的问题,提出了一种基于RRT*的路径规划新算法。首先,通过改进相对距离势场法,提出了自适应人工势场(adaptive artificial potential field,AAPF)法,既克服了相对距离势场法中引力与斥力过大的问题,又解决了目标点不可达的问题。然后,将RRT*算法与AAPF法相结合,并将固定步长改为动态步长,从而既克服了RRT*算法中节点盲目扩展的问题,又显著提高了移动机器人路径规划效率和避障灵活性,同时兼顾路径平滑性。最后,基于MATLAB进行仿真,验证了所提算法的有效性和实用性。

Aiming at the problem of blind exploring and low rate of convergence in improved rapidly-exploring random tree(RRT*)algorithm,a novel path planning method for mobile robot was proposed by improving RRT*algorithm.Firstly,adaptive artificial potential field(AAPF)algorithm was proposed on the basis of improved potential field with relative distance.AAPF algorithm not only overcomes the defections of excessive attractive and repulsive forces,but also solves the problem of goals non-reachable with obstacles nearby.Secondly,in order to avoid the blind explorations of nodes,RRT*algorithm was combined with AAPF algorithm,and fixed step of the combined algorithm was changed into dynamic step.The new path planning method not only avoids the blind explorations of nodes,but also effectively improves the efficiency of path planning and flexibility of obstacle avoidance.Meanwhile,smoothness of planned path could be guaranteed.Finally,a simulation based on MATLAB was conducted,and the utility of the method proposed was checked by the results in this paper.