Bi-AM-RRT*: A Fast and Efficient Sampling-Based Motion Planning Algorithm in Dynamic Environments

TL;DR

This paper introduces Bi-AM-RRT*, a novel sampling-based motion planning algorithm that combines bidirectional search and an assisting metric to efficiently find near-optimal paths in dynamic environments for mobile robots.

Contribution

The paper proposes a new RRT* variant integrating an assisting metric and bidirectional search to improve planning speed and path quality in dynamic environments.

Findings

Achieves shorter path lengths compared to existing methods.

Reduces search time significantly in various environments.

Consistently finds near-optimal paths with the shortest search time.

Abstract

The efficiency of sampling-based motion planning brings wide application in autonomous mobile robots. The conventional rapidly exploring random tree (RRT) algorithm and its variants have gained significant successes, but there are still challenges for the optimal motion planning of mobile robots in dynamic environments. In this paper, based on Bidirectional RRT and the use of an assisting metric (AM), we propose a novel motion planning algorithm, namely Bi-AM-RRT*. Different from the existing RRT-based methods, the AM is introduced in this paper to optimize the performance of robot motion planning in dynamic environments with obstacles. On this basis, the bidirectional search sampling strategy is employed to reduce the search time. Further, we present a new rewiring method to shorten path lengths. The effectiveness and efficiency of the proposed Bi-AM-RRT* are proved through comparative experiments in different environments. Experimental results show that the Bi-AM-RRT* algorithm can achieve better performance in terms of path length and search time, and always finds near-optimal paths with the shortest search time when the diffusion metric is used as the AM.