Decentralised Approach for Multi Agent Path Finding

TL;DR

This paper introduces DeMAPF, a decentralized multi-agent pathfinding method for spatially-extended agents, enabling scalable, faster conflict resolution in complex real-world scenarios like convoy and train scheduling.

Contribution

The paper presents a novel decentralized approach for MAPF that improves scalability and convergence speed compared to existing distributed methods.

Findings

Faster convergence to conflict-free solutions than other distributed approaches

Requires less memory for conflict resolution

Applicable to real-world problems like convoy and train scheduling

Abstract

Multi Agent Path Finding (MAPF) requires identification of conflict free paths for agents which could be point-sized or with dimensions. In this paper, we propose an approach for MAPF for spatially-extended agents. These find application in real world problems like Convoy Movement Problem, Train Scheduling etc. Our proposed approach, Decentralised Multi Agent Path Finding (DeMAPF), handles MAPF as a sequence of pathplanning and allocation problems which are solved by two sets of agents Travellers and Routers respectively, over multiple iterations. The approach being decentralised allows an agent to solve the problem pertinent to itself, without being aware of other agents in the same set. This allows the agents to be executed on independent machines, thereby leading to scalability to handle large sized problems. We prove, by comparison with other distributed approaches, that the approach leads to a faster convergence to a conflict-free solution, which may be suboptimal, with lesser memory requirement.