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.
Accelerating Focal Search in Multi-Agent Path Finding with Tighter Lower Bounds
Thomas Lord Department of Computer Science, University of Southern California（美国南加州大学计算机科学系）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；Carnegie Mellon University（卡内基梅隆大学）；University of California, Irvine（加州大学尔湾分校）
Federal Research Center for Computer Science and Control of Russian Academy of Sciences（俄罗斯科学院联邦计算机科学与控制研究中心）；National Research University Higher School of Economics（国立研究型大学高等经济学院）；Peoples’ Friendship University of Russia (RUDN University)（俄罗斯人民友谊大学（卢蒙巴大学））；Ben-Gurion University of the Negev（内盖夫本-古里安大学）；Palo Alto Research Center (PARC)（帕洛阿尔托研究中心（PARC））
