Multi-Agent Path Finding (MAPF), i.e., finding collision-free paths for multiple robots, is important for many applications where small runtimes are necessary, including the kind of automated warehouses operated by Amazon. CBS is a leading two-level search algorithm for solving MAPF optimally. ECBS is a bounded-suboptimal variant of CBS that uses focal search to speed up CBS by sacrificing optimality and instead guaranteeing that the costs of its solutions are within a given factor of optimal. In this paper, we study how to decrease its runtime even further using inadmissible heuristics. Motivated by Explicit Estimation Search (EES), we propose Explicit Estimation CBS (EECBS), a new bounded-suboptimal variant of CBS, that uses online learning to obtain inadmissible estimates of the cost of the solution of each high-level node and uses EES to choose which high-level node to expand next. We also investigate recent improvements of CBS and adapt them to EECBS. We find that EECBS with the improvements runs significantly faster than the state-of-the-art bounded-suboptimal MAPF algorithms ECBS, BCP-7, and eMDD-SAT on a variety of MAPF instances. We hope that the scalability of EECBS enables additional applications for bounded-suboptimal MAPF algorithms.

翻译：多种代理路径定位(MAPF),即寻找多机器人的无碰撞路径,对于许多需要小型运行时间的应用程序,包括亚马逊操作的自动仓库等,都很重要。 CBS是最佳解决MAPF的双层搜索算法。 CBS是CBS中一个封闭的亚最佳变体,它利用焦点搜索来加速CBS的速度,牺牲最佳性,而不是保证其解决方案的成本在一个特定的最佳因素范围内。在本文中,我们研究如何进一步减少其运行时间,甚至进一步使用不可接受的超常学。我们发现,ECBS的改进速度大大快于MAPS-MAPS-MAPS-MLAF 的州际、MAPS-MAPS-MAPS-MDMAF 限值。我们发现,S-MAPFS-MAPS-MAPS-MAPF 的州-MAPFS-MDMAF 限值,我们州-MAPS-MAPS-AF-DF 的州际应用软件。