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Digital Twin-Assisted Controlling of AGVs in Flexible Manufacturing Environments
Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland.
Department of Informatics, University of Oslo, Oslo, Norway.
Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland.ORCID iD: 0000-0002-9315-9920
2021 (English)In: 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE), IEEE, 2021Conference paper, Published paper (Refereed)
Abstract [en]

Digital Twins are increasingly being introduced for smart manufacturing systems to improve the efficiency of the main disciplines of such systems. Formal techniques, such as graphs, are a common way of describing Digital Twin models, allowing broad types of tools to provide Digital Twin based services such as fault detection in production lines. Obtaining correct and complete formal Digital Twins of physical systems can be a complicated and time consuming process, particularly for manufacturing systems with plenty of physical objects and the associated manufacturing processes. Automatic generation of Digital Twins is an emerging research field and can reduce time and costs. In this paper, we focus on the generation of Digital Twins for flexible manufacturing systems with Automated Guided Vehicles (AGVs) on the factory floor. In particular, we propose an architectural framework and the associated design choices and software development tools that facilitate automatic generation of Digital Twins for AGVs. Specifically, the scope of the generated digital twins is controlling AGVs in the factory floor. To this end, we focus on different control levels of AGVs and utilize graph theory to generate the graph-based Digital Twin of the factory floor.

Place, publisher, year, edition, pages
IEEE, 2021.
Series
Proceedings of the IEEE International Symposium on Industrial Electronics, E-ISSN 2163-5145
Keywords [en]
industry 4.0, agile manufacturing, AGV, digital twin, graph theory, multi-layer control
National Category
Computer Sciences Production Engineering, Human Work Science and Ergonomics
Research subject
Dependable Communication and Computation Systems
Identifiers
URN: urn:nbn:se:ltu:diva-90560DOI: 10.1109/ISIE45552.2021.9576361ISI: 000779299900169Scopus ID: 2-s2.0-85118783858OAI: oai:DiVA.org:ltu-90560DiVA, id: diva2:1656950
Conference
30th IEEE International Symposium on Industrial Electronics (ISIE), Kyoto, Japan, [ONLINE], June 20-23, 2021
Note

ISBN för värdpublikation: 978-1-7281-9023-5

Available from: 2022-05-09 Created: 2022-05-09 Last updated: 2023-10-16Bibliographically approved

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Vyatkin, Valeriy

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