Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks

TL;DR

This paper introduces a lifelong multi-agent pathfinding problem tailored for online pickup and delivery tasks, proposing two algorithms that are theoretically sound and efficient in large-scale simulated warehouse environments.

Contribution

It formulates the lifelong MAPD problem, presents two decoupled algorithms with theoretical guarantees, and evaluates their performance in simulated warehouse scenarios.

Findings

TP is efficient for large-scale, real-time applications.

TPTS balances communication and efficiency.

Both algorithms outperform non-guaranteed centralized methods.

Abstract

The multi-agent path-finding (MAPF) problem has recently received a lot of attention. However, it does not capture important characteristics of many real-world domains, such as automated warehouses, where agents are constantly engaged with new tasks. In this paper, we therefore study a lifelong version of the MAPF problem, called the multi-agent pickup and delivery (MAPD) problem. In the MAPD problem, agents have to attend to a stream of delivery tasks in an online setting. One agent has to be assigned to each delivery task. This agent has to first move to a given pickup location and then to a given delivery location while avoiding collisions with other agents. We present two decoupled MAPD algorithms, Token Passing (TP) and Token Passing with Task Swaps (TPTS). Theoretically, we show that they solve all well-formed MAPD instances, a realistic subclass of MAPD instances. Experimentally, we compare them against a centralized strawman MAPD algorithm without this guarantee in a simulated warehouse system. TP can easily be extended to a fully distributed MAPD algorithm and is the best choice when real-time computation is of primary concern since it remains efficient for MAPD instances with hundreds of agents and tasks. TPTS requires limited communication among agents and balances well between TP and the centralized MAPD algorithm.