EECBS: A Bounded-Suboptimal Search for Multi-Agent Path Finding

TL;DR

EECBS introduces an online learning-based bounded-suboptimal search algorithm for multi-agent pathfinding, significantly improving runtime efficiency over existing methods by using inadmissible heuristics and EES-based node expansion.

Contribution

The paper proposes EECBS, a novel bounded-suboptimal MAPF algorithm that employs online learning and EES to enhance speed and scalability beyond current state-of-the-art methods.

Findings

EECBS runs significantly faster than ECBS, BCP-7, and eMDD-SAT.

Inadmissible heuristics and EES improve search efficiency.

Scalability enables broader applications for MAPF.

Abstract

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.