Multi-Agent Path Finding (MAPF) is an important core problem for many new and emerging industrial applications. Many works appear on this topic each year, and a large number of substantial advancements and performance improvements have been reported. Yet measuring overall progress in MAPF is difficult: there are many potential competitors, and the computational burden for comprehensive experimentation is prohibitively large. Moreover, detailed data from past experimentation is usually unavailable. In this work, we introduce a set of methodological and visualisation tools which can help the community establish clear indicators for state-of-the-art MAPF performance and which can facilitate large-scale comparisons between MAPF solvers. Our objectives are to lower the barrier of entry for new researchers and to further promote the study of MAPF, since progress in the area and the main challenges are made much clearer.

翻译：多智能体路径规划（MAPF）是众多新兴工业应用中的核心基础问题。每年该领域涌现大量研究成果，众多实质性进展与性能提升被相继报道。然而，全面评估MAPF领域的整体进展颇具挑战：存在大量潜在竞争者，且开展全面实验的计算成本过高。此外，过往实验的详细数据通常难以获取。本研究提出一套方法论与可视化工具，助力学界确立MAPF最先进性能的清晰指标，并促进求解器间的大规模对比。我们的目标在于降低新研究者的入门门槛，并进一步推动MAPF研究——因为领域进展与主要挑战将因此变得更为明晰。