Collision Avoidance Path Control of Multi-AGV Using Multi-Agent Reinforcement Learning


KIPS Transactions on Computer and Communication Systems, Vol. 11, No. 9, pp. 281-288, Sep. 2022
https://doi.org/10.3745/KTCCS.2022.11.9.281,   PDF Download:
Keywords: Fulfillment Center, Warehouse, AGV, Path Control, MARL
Abstract

AGVs are often used in industrial applications to transport heavy materials around a large industrial building, such as factories or warehouses. In particular, in fulfillment centers their usefulness is maximized for automation. To increase productivity in warehouses such as fulfillment centers, sophisticated path planning of AGVs is required. We propose a scheme that can be applied to QMIX, a popular cooperative MARL algorithm. The performance was measured with three metrics in several fulfillment center layouts, and the results are presented through comparison with the performance of the existing QMIX. Additionally, we visualize the transport paths of trained AGVs for a visible analysis of the behavior patterns of the AGVs as heat maps.


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Cite this article
[IEEE Style]
H. Choi, J. Kim, Y. Han, S. Oh, K. Kim, "Collision Avoidance Path Control of Multi-AGV Using Multi-Agent Reinforcement Learning," KIPS Transactions on Computer and Communication Systems, vol. 11, no. 9, pp. 281-288, 2022. DOI: https://doi.org/10.3745/KTCCS.2022.11.9.281.

[ACM Style]
Ho-Bin Choi, Ju-Bong Kim, Youn-Hee Han, Se-Won Oh, and Kwi-Hoon Kim. 2022. Collision Avoidance Path Control of Multi-AGV Using Multi-Agent Reinforcement Learning. KIPS Transactions on Computer and Communication Systems, 11, 9, (2022), 281-288. DOI: https://doi.org/10.3745/KTCCS.2022.11.9.281.