Multi-robot Path Planning with Rapidly-exploring Random Disjointed-Trees

TL;DR

This paper introduces MA-RRdT*, a novel multi-robot path planning algorithm that significantly improves speed and space exploration efficiency on complex maps, outperforming existing methods in computational time and effectiveness.

Contribution

The paper presents MA-RRdT*, a new multi-robot path planning algorithm based on multi-tree random sampling, enhancing speed and efficiency over previous approaches.

Findings

MA-RRdT* achieves faster planning times.

The algorithm demonstrates higher space exploration efficiency.

Experimental results show superior performance in complex environments.

Abstract

Multi-robot path planning is a computational process involving finding paths for each robot from its start to the goal while ensuring collision-free operation. It is widely used in robots and autonomous driving. However, the computational time of multi-robot path planning algorithms is enormous, resulting in low efficiency in practical applications. To address this problem, this article proposes a novel multi-robot path planning algorithm (Multi-Agent Rapidly-exploring Random Disjointed-Trees*, MA-RRdT*) based on multi-tree random sampling. The proposed algorithm is based on a single-robot path planning algorithm (Rapidly-exploring Random disjointed-Trees*, RRdT*). The novel MA-RRdT* algorithm has the advantages of fast speed, high space exploration efficiency, and suitability for complex maps. Comparative experiments are completed to evaluate the effectiveness of MA-RRdT*. The final experimental results validate the superior performance of the MA-RRdT* algorithm in terms of time cost and space exploration efficiency.