A Path Planning Algorithm Based on Improved RRT

Abstract

To address the problems of long search time and non-optimal generated paths when the original fast expanding random tree (RRT) algorithm is used for path planning, this paper proposes an improved RRT algorithm based on greedy strategy combined with adaptive sampling area and node approaching to obstacles. Firstly, the greedy idea is introduced on the basis of the original RRT algorithm to improve the node expansion strategy; secondly, the sampling area is limited by introducing the adaptive sampling factor to optimize the search time and improve the efficiency; finally, the path length is further optimized by removing the redundant nodes of the path and using the optimization algorithm to make the nodes approach the obstacles. The simulation results show that the improved algorithm can solve a better path with fewer expansions and faster convergence speed for different complexity scenarios.