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Conflict-Based Search (CBS) is a popular multi-agent path finding (MAPF)solver that employs a low-level single agent planner and a high-levelconstraint tree to resolve conflicts. The vast majority of modern MAPF solversfocus on improving CBS by reducing the size of this tree through variousstrategies with few methods modifying the low level planner. Typically lowlevel planners in existing CBS methods use an unweighted cost-to-go heuristic,with suboptimal CBS methods also using a conflict heuristic to help the highlevel search. In this paper, we show that, contrary to prevailing CBS beliefs,a weighted cost-to-go heuristic can be used effectively alongside the conflictheuristic in two possible variants. In particular, one of these variants canobtain large speedups, 2-100x, across several scenarios and suboptimal CBSmethods. Importantly, we discover that performance is related not to theweighted cost-to-go heuristic but rather to the relative conflict heuristicweight's ability to effectively balance low-level and high-level work, implyingthat existing suboptimal CBS work misses this subtlety. Additionally, to thebest of our knowledge, we show the first theoretical relation of prioritizedplanning and bounded suboptimal CBS and demonstrate that our methods are theirnatural generalization.

Effective Integration of Weighted Cost-to-go and Conflict Heuristic within Suboptimal CBS