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

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. 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 size of the space of possible solutions, i.e., the Pareto-optimal set, can grow exponentially with the number of agents (the dimension of the search space). This article presents an approach named Multi-Objective Conflict-Based Search (MO-CBS) that bypasses 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 further improve its performance. We prove that MO-CBS and its variants are able to compute the entire Pareto-optimal set. Numerical results show that MO-CBS outperforms both MOA* as well as MOM*, a recently developed state-of-the-art multi-objective multi-agent planner.