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)

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
• Typology: 1.01 - Original Scientific Article
• Organization: FS - Faculty of Mechanical Engineering
• Publication status: Published
• Publication version: Author Accepted Manuscript
• Year: 2020
• Number of pages: Str. 397-400
• Numbering: Vol. 69, iss. 1
• PID: 20.500.12556/RUL-117881
• UDC: 681.5(045)
• ISSN on article: 0007-8506
• DOI: 10.1016/j.cirp.2020.04.001
• COBISS.SI-ID: 24176643
• Publication date in RUL: 31.07.2020

Record is a part of a journal

• Title: CIRP annals
• Shortened title: CIRP ann.
• Publisher: Technische Rundschau, Hallwag Verlag, Colibri, Elsevier
• ISSN: 0007-8506
• COBISS.SI-ID: 170267

Secondary language

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

Projects

• Funder: ARRS - Slovenian Research Agency
• Project number: P2-0270
• Name: Proizvodni sistemi, laserske tehnologije in spajanje materialov