Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks
Hang Ma, Jiaoyang Li, T. K. Satish Kumar, Sven Koenig

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,…
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Taxonomy
TopicsRobotic Path Planning Algorithms · Optimization and Search Problems · Vehicle Routing Optimization Methods
