  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.
