Research on mobile robot path planning based on improved RRT algorithm
Xiang Wang, Sheng Quan Xie, Hongxu Li, YuKe Xie · 2025
To address the limitations of the traditional Rapidly Exploring Random Tree* (RRT*) algorithm, such as slow convergence and poor adaptability to dynamic environments, an improved dynamic path planning method is proposed by integrating the RRT* algorithm with the Dynamic Window Approach (DWA) to achieve efficient obstacle avoidance for mobile robots in complex dynamic environments. The improved RRT* algorithm generates a globally optimal and safe path based on known environmental information. The proposed method optimizes sampling points to ensure path optimality while reducing search time. It introduces the concept of maximum path nodes to eliminate redundant nodes in the extended tree, enhancing algorithm efficiency. Additionally, the safety of the global path is ensured by removing dangerous nodes generated by the RRT* algorithm and applying a greedy algorithm to remove redundant nodes, shortening the global path length.