Streaming Multi-agent Pathfinding

TL;DR

This paper introduces Streaming Multi-agent Pathfinding (S-MAPF), a new problem formulation for periodic, long-duration scenarios like assembly lines, and proposes a specialized solver, ASCBS, that outperforms traditional MAPF methods in runtime.

Contribution

It formalizes the S-MAPF problem and develops ASCBS, a novel conflict-based search algorithm incorporating cyclic constraints for efficient long-term multi-agent path planning.

Findings

ASCBS outperforms traditional MAPF solvers in runtime.

Incorporates cyclic vertex/edge constraints to handle periodic scenarios.

Explores disjoint splitting strategy within ASCBS.

Abstract

The task of the multi-agent pathfinding (MAPF) problem is to navigate a team of agents from their start point to the goal points. However, this setup is unsuitable in the assembly line scenario, which is periodic with a long working hour. To address this issue, the study formalizes the streaming MAPF (S-MAPF) problem, which assumes that the agents in the same agent stream have a periodic start time and share the same action sequence. The proposed solution, Agent Stream Conflict-Based Search (ASCBS), is designed to tackle this problem by incorporating a cyclic vertex/edge constraint to handle conflicts. Additionally, this work explores the potential usage of the disjoint splitting strategy within ASCBS. Experimental results indicate that ASCBS surpasses traditional MAPF solvers in terms of runtime for scenarios with prolonged working hours.