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.
