A Conflict-Based Search Framework for Multi-Objective Multi-Agent Path Finding

TL;DR

This paper introduces MO-CBS, a conflict-based search framework for multi-objective multi-agent pathfinding that efficiently finds the entire Pareto-optimal set, outperforming existing methods.

Contribution

It presents MO-CBS, a novel algorithm combining conflict-based search with multi-objective optimization principles to handle high-dimensional search spaces effectively.

Findings

MO-CBS computes the entire Pareto-optimal set.

MO-CBS outperforms the state-of-the-art MOM* in numerical tests.

Variants of MO-CBS improve performance further.

Abstract

Conventional multi-agent path planners typically compute an ensemble of paths while optimizing a single objective, such as path length. However, many applications may require multiple objectives, say fuel consumption and completion time, to be simultaneously optimized during planning and these criteria may not be readily compared and sometimes lie in competition with each other. The goal of the problem is thus to find a Pareto-optimal set of solutions instead of a single optimal solution. Naively applying existing multi-objective search algorithms, such as multi-objective A* (MOA*), to multi-agent path finding may prove to be inefficient as the dimensionality of the search space grows exponentially with the number of agents. This article presents an approach named Multi-Objective Conflict-Based Search (MO-CBS) that attempts to address this so-called curse of dimensionality by leveraging prior Conflict-Based Search (CBS), a well-known algorithm for single-objective multi-agent path finding, and principles of dominance from multi-objective optimization literature. We also develop several variants of MO-CBS to improve its performance. We prove that MO-CBS and its variants can compute the entire Pareto-optimal set. Numerical results show that MO-CBS outperforms MOM*, a recently developed state-of-the-art multi-objective multi-agent planner.