Lifelong multi-agent path planning in automated logistics system

Chenyi Guo; Bingtao Liu; Yuhan Gao

doi:10.1117/12.3034895

7 August 2024 Lifelong multi-agent path planning in automated logistics system

Chenyi Guo, Bingtao Liu, Yuhan Gao

Proceedings Volume 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024); 132241I (2024) https://doi.org/10.1117/12.3034895
Event: 4th International Conference on Internet of Things and Smart City, 2024, Hangzhou, China

Abstract

There are automated guided vehicles(AGVs) in automated logistics systems. Without effective on-line supervisory control strategies, there will be many conflicts and deadlock problems. Many researches concentrate on “one-shot” problem. The number of the agents is the same as the number of the tasks. But in the automated logistics system, an agent can be assigned many tasks at any time. The agent has to first move to pick up the materials and then unload the materials at another location. To solve these problems, this paper proposes a lifelong multi-agent path planning method in automated logistics system. This method doesn't entail selecting the shortest path for each agent, but instead focuses on maximizing the factory throughput by allocating tasks, choosing paths, and employing other methods. The method includes three main parts: assignment of tasks, candidate path determination and selection of the deadlock-free paths. It searches the candidate paths using the depth-first search algorithm, and avoids deadlocks and conflicts with the advanced time window algorithm. By adjusting the executing time of the task, the occupation time of path resources is reduced, which allows more AGVs travel on the path and is better than the method of AGVs wait at the nodes and edges. At the same time, the method allows the tasks executed as soon as possible, which also improves the throughput of the system. The proposed method is high-efficient by means of simulation, and the software based on the proposed method has been successfully put into practice in the material handling and distribution plant.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chenyi Guo, Bingtao Liu, and Yuhan Gao "Lifelong multi-agent path planning in automated logistics system", Proc. SPIE 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024), 132241I (7 August 2024); https://doi.org/10.1117/12.3034895

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KEYWORDS

Materials processing

Matrices

Design

Manufacturing

Systems modeling

Visualization

Mathematical modeling

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