A Conflict-Based Search Framework for Multi-Objective Multi-Agent Path Finding
This work provides a more efficient approach for multi-objective path planning for multi-agent systems, which is crucial for applications requiring trade-offs between competing objectives.
This paper tackles the challenge of multi-objective multi-agent path finding, where multiple criteria like fuel consumption and completion time need simultaneous optimization. The authors propose Multi-Objective Conflict-Based Search (MO-CBS) and its variants, which are proven to compute the entire Pareto-optimal set and outperform the state-of-the-art MOM* planner.
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.