Andreja Malus (Author), Dominik Kozjek (Author), Rok Vrabič (Author)

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.

Keywords

logistics;machine learning;distributed control;

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UL FS - Faculty of Mechanical Engineering
UDC: 681.5(045)
COBISS: 24176643 Link will open in a new window
ISSN: 0007-8506
Views: 489
Downloads: 82
Average score: 0 (0 votes)
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Other data

Secondary language: Slovenian
Secondary keywords: logistika;strojno učenje;porazdeljeno krmiljenje;
Type (COBISS): Article
Embargo end date (OpenAIRE): 2022-05-17
Pages: str. 397-400
Volume: ǂVol. ǂ69
Issue: ǂiss. ǂ1
Chronology: 2020
DOI: 10.1016/j.cirp.2020.04.001
ID: 11928334