Flow-Based Guidance Framework with Flex Distribution for $w$-Optimal Multi-Agent Path Finding

Shao-Hung Chan, Thomy Phan, Sven Koenig

Keywords: Multi-Agent Path Finding, w-Optimal Heuristic Search, Planning

TL;DR: We propose a pre-processing approach for generaing guidance heuristics based on flows of paths, which improves the efficiency of solving w-Optimal MAPF.

Abstract:

Multi-Agent Path Finding (MAPF) is the one-shot problem of finding collision-free paths in a shared environment while minimizing the sum of the agents' travel times.
Since solving MAPF optimally is NP-hard, $w$-optimal algorithms such as Explicit Estimation Conflict-Based Search (EECBS) have been used to speed up the search while providing a guarantee on the solution quality.
However, the scalability of EECBS is limited in large-scale MAPF instances.
While EECBS can be accelerated for regularly structured environments, such as Kiva warehouses, by utilizing specialized guidance heuristics, these heuristics are ineffective in more general and large-scale environments.
To fill this gap, we propose the \textit{Flow-Based Guidance Framework} (FBGF), a general two-phase process that simulates a list of paths and then generates the \textit{Flow-Based Guidance Heuristic} (FH) without making prior assumptions about the environment's structure.
We identify features that distinguish $w$-optimal MAPF from other MAPF variants and propose strategies to enhance its effectiveness for guidance, complemented by the flex distribution technique from EECBS.
The empirical evaluation demonstrates that our FH significantly reduces collisions, thereby achieving higher success rates than the state-of-the-art within 60 seconds.

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Submission Number: 12