Multi-Agent Terraforming: Efficient Multi-Agent Path Finding via Environment Manipulation

TL;DR

This paper introduces Terraforming MAPF (tMAPF), a new multi-agent pathfinding extension where agents can move obstacles, improving efficiency in environments like warehouses with dynamic obstacle manipulation.

Contribution

It extends existing MAPF algorithms CBS and PBS to handle movable obstacles, enabling agents to manipulate their environment for better routing.

Findings

tMAPF outperforms static obstacle MAPF solutions.

Extended algorithms successfully handle obstacle manipulation.

Demonstrates improved routing efficiency in dynamic environments.

Abstract

Multi-agent pathfinding (MAPF) is concerned with planning collision-free paths for a team of agents from their start to goal locations in an environment cluttered with obstacles. Typical approaches for MAPF consider the locations of obstacles as being fixed, which limits their effectiveness in automated warehouses, where obstacles (representing pods or shelves) can be moved out of the way by agents (representing robots) to relieve bottlenecks and introduce shorter routes. In this work we initiate the study of MAPF with movable obstacles. In particular, we introduce a new extension of MAPF, which we call Terraforming MAPF (tMAPF), where some agents are responsible for moving obstacles to clear the way for other agents. Solving tMAPF is extremely challenging as it requires reasoning not only about collisions between agents, but also where and when obstacles should be moved. We present extensions of two state-of-the-art algorithms, CBS and PBS, in order to tackle tMAPF, and demonstrate that they can consistently outperform the best solution possible under a static-obstacle setting.