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Real-time order dispatching for a fleet of autonomous mobile robots using multi-agent reinforcement learning
Malus, Andreja (Author), Kozjek, Dominik (Author), Vrabič, Rok (Author)

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Abstract
Autonomous mobile robots (AMRs) are increasingly being used to enable efficient material flow in dynamic production environments. Dispatching transport orders in such environments is difficult due to the complexity arising from the rapid changes in the environment as well as due to a tight coupling between dispatching, path planning, and route execution. For order dispatching, an approach is proposed that uses multi-agent reinforcement learning, where AMR agents learn to bid on orders based on their individual observations. The approach is investigated in a robot simulation environment. The results show a more efficient order allocation compared to commonly used dispatching rules.

Language:English
Keywords:logistics, machine learning, distributed control
Work type:Article (dk_c)
Tipology:1.01 - Original Scientific Article
Organization:FS - Faculty of Mechanical Engineering
Year:2020
Number of pages:Str. 397-400
Numbering:Vol. 69, iss. 1
UDC:681.5(045)
ISSN on article:0007-8506
DOI:10.1016/j.cirp.2020.04.001 This link opens in a new window
COBISS.SI-ID:24176643 This link opens in a new window
Views:231
Downloads:51
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Record is a part of a journal

Title:CIRP annals
Shortened title:CIRP ann.
Publisher:Elsevier
ISSN:0007-8506
COBISS.SI-ID:170267 This link opens in a new window

Document is financed by a project

Funding Programme:MIZŠ
Project no.:C3330-16-529000
Name:
Acronym:GOSTOP

Funder:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije (ARRS)
Project no.:P2-0270
Name:Proizvodni sistemi, laserske tehnologije in spajanje materialov

Secondary language

Language:Slovenian
Keywords:logistika, strojno učenje, porazdeljeno krmiljenje

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