Multi-Robot Path Planning (MRPP) on graphs, equivalently known as Multi-Agent Path Finding (MAPF), is a well-established NP-hard problem with critically important applications. As serial computation in (near)-optimally solving MRPP approaches the computation efficiency limit, parallelization offers a promising route to push the limit further, especially in handling hard or large MRPP instances. In this study, we initiated a \emph{targeted} parallelization effort to boost the performance of conflict-based search for MRPP. Specifically, when instances are relatively small but robots are densely packed with strong interactions, we apply a decentralized parallel algorithm that concurrently explores multiple branches that leads to markedly enhanced solution discovery. On the other hand, when instances are large with sparse robot-robot interactions, we prioritize node expansion and conflict resolution. Our innovative multi-threaded approach to parallelizing bounded-suboptimal conflict search-based algorithms demonstrates significant improvements over baseline serial methods in success rate or runtime. Our contribution further pushes the understanding of MRPP and charts a promising path for elevating solution quality and computational efficiency through parallel algorithmic strategies.

翻译：多机器人路径规划（MRPP），在图上等价于多智能体路径寻找（MAPF），是一个经典且具有重要应用的NP难问题。随着串行计算方法在（近）最优求解MRPP时逐渐逼近其计算效率极限，并行化成为进一步突破这一极限的极具前景的途径，尤其是在处理困难或大规模MRPP实例时。在本研究中，我们启动了针对性的并行化工作，以提升基于冲突搜索的MRPP求解性能。具体而言，当实例规模较小但机器人密集排布且存在强交互时，我们采用一种去中心化的并行算法，同时探索多个分支，从而显著增强解发现能力。另一方面，当实例规模较大且机器人间交互稀疏时，我们优先进行节点扩展与冲突解决。我们创新的多线程方法对基于有界次优冲突搜索的算法进行并行化，在成功率或运行时间方面相较于基线串行方法展现出显著改进。我们的贡献进一步加深了对MRPP的理解，并为通过并行算法策略提升解质量与计算效率开辟了有前景的路径。