In multi-agent path finding (MAPF) the task is to find non-conflicting paths for multiple agents. Recently, a SAT-based approach was developed to solve this problem and proved beneficial in many cases when compared to other search-based solvers. In this paper, we introduce SAT-based unbounded- and bounded-suboptimal algorithms and compare them to relevant search-based algorithms.

The task in the multi-agent path finding problem (MAPF) is to find paths for multiple agents, each with a different start and goal position, such that agents do not collide. A successful optimal MAPF solver is the conflict-based search (CBS) algorithm. CBS is a two level algorithm where special conditions ensure it returns the optimal solution. Solving MAPF optimally is proven to be NP-hard, hence CBS and all other optimal solvers do not scale up. We propose several ways to relax the optimality conditions of CBS trading solution quality for runtime as well as bounded-suboptimal variants, where the returned solution is guaranteed to be within a constant factor from optimal solution cost. Experimental results show the benefits of our new approach; a massive reduction in running time is presented while sacrificing a minor loss in solution quality. Our new algorithms outperform other existing algorithms in most of the cases.