MGCBS: An Optimal and Efficient Algorithm for Solving Multi-Goal Multi-Agent Path Finding Problem

TL;DR

This paper introduces MGCBS, an optimal and efficient algorithm for multi-goal multi-agent pathfinding that outperforms existing methods in speed while guaranteeing optimal solutions.

Contribution

The paper proposes MGCBS, a novel algorithm that guarantees optimal solutions for MG-MAPF and improves efficiency using the TIS Forest data structure.

Findings

MGCBS consistently finds optimal solutions.

MGCBS is up to 7 times faster than previous methods.

The TIS Forest enhances search efficiency.

Abstract

With the expansion of the scale of robotics applications, the multi-goal multi-agent pathfinding (MG-MAPF) problem began to gain widespread attention. This problem requires each agent to visit pre-assigned multiple goal points at least once without conflict. Some previous methods have been proposed to solve the MG-MAPF problem based on Decoupling the goal Vertex visiting order search and the Single-agent pathfinding (DVS). However, this paper demonstrates that the methods based on DVS cannot always obtain the optimal solution. To obtain the optimal result, we propose the Multi-Goal Conflict-Based Search (MGCBS), which is based on Decoupling the goal Safe interval visiting order search and the Single-agent pathfinding (DSS). Additionally, we present the Time-Interval-Space Forest (TIS Forest) to enhance the efficiency of MGCBS by maintaining the shortest paths from any start point at any start time step to each safe interval at the goal points. The experiment demonstrates that our method can consistently obtain optimal results and execute up to 7 times faster than the state-of-the-art method in our evaluation.