Effectively Incorporating Weighted Cost-to-go Heuristic in Suboptimal CBS (Extended Abstract)

Abstract

Conflict-Based Search (CBS) is a popular multi-agent path finding (MAPF) solver that employs a low-level single agent planner and a high-level constraint tree to resolve conflicts. The majority of modern MAPF solvers focus on improving CBS by reducing the size of this tree through various strategies with few methods modifying the low level planner. All low level planners in existing CBS methods use an unweighted cost-to-go heuristic, with suboptimal CBS methods also using a conflict heuristic to help the high level search. Contrary to prevailing beliefs, we show that the cost-to-go heuristic can be used significantly more effectively by weighting it in a specific manner alongside the conflict heuristic. We introduce two variants of doing so and demonstrate that this change can lead to 2-100x speedups in certain scenarios. Additionally, we show the first theoretical relation of prioritized planning and bounded suboptimal CBS and demonstrate that our methods are their natural generalization.

Accelerating Search-Based Planning for Multi-Robot Manipulation by Leveraging Online-Generated Experiences

“…We commence by establishing the bounded suboptimality of the low-level planner xWA * that leverages past experiences. To allow for the use of inflated heuristics using a weighted OPEN list (Veerapaneni, Kusnur, and Likhachev 2023), which is common in manipulation, we expand our analysis to low-level planners with w 1 -admissible (Pearl and Kim 1982)…”