Research on path planning based on improved RRT-Connect algorithm
Hongtao Yang, Huanyu Li, Keping Liu, Weibo Yu, Xiaoyuan Li
Abstract
Aiming at the low efficiency of path planning of RRT-Connect algorithm and random sampling, an improved algorithm based on RRT-Connect is proposed. The algorithm introduces a target bias strategy on the original RRT-Connect algorithm, guides the sampling point to expand in the direction of the target point, and changes the length of the step during the random tree expansion process, thereby increasing the speed of the random tree exploring the space, and finally adopts the greedy algorithm Prune the random tree to achieve path smoothing. The improved RRT-Connect algorithm is compared with the RRT and RRT-Connect algorithms respectively, and the results show that the algorithm proposed in this paper is significantly better than the compared algorithm in terms of path length and execution time.