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Adopting a Digital Twin Framework for Autonomous Machine Operation at Construction Sites
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.ORCID iD: 0000-0001-5488-2799
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.ORCID iD: 0000-0003-1597-6738
2023 (English)In: Proc. CAA Int. Conf. Veh. Control Intell., CVCI, Institute of Electrical and Electronics Engineers Inc. , 2023Conference paper, Published paper (Refereed)
Abstract [en]

Autonomous machines are expected to be vastly used at construction sites as they can efficiently perform repetitive and dangerous tasks. However, ensuring the operational safety of such autonomous machines in a highly dynamic environment is challenging. Although autonomous machines usually are equipped with a perception system that permits them to navigate locally, there is a need to share a global view of the construction site to reduce the risk of accidents or errors. A digital twin of the construction site map has the potential of fusing the real-time perception from different sources at the site, such as different autonomous machines working at the construction site, analysing them and sharing the needed information to operate safely and effectively at the site. This paper proposes the adoption of the recently published standard, ISO 23247 digital twin framework for manufacturing, to implement and maintain a dynamic map of construction sites. The proposed framework will enable safe and efficient operation of autonomous machines on construction sites.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2023.
Keywords [en]
Construction site, Digital twin, Maps, Operational Design Domain, Safety, Traversability
National Category
Civil Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-66153DOI: 10.1109/CVCI59596.2023.10397254Scopus ID: 2-s2.0-85185388766ISBN: 9798350340488 (print)OAI: oai:DiVA.org:mdh-66153DiVA, id: diva2:1841235
Conference
Proceedings of the 2023 7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023
Available from: 2024-02-28 Created: 2024-02-28 Last updated: 2024-02-28Bibliographically approved

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Fattouh, AnasAfshar, Sara ZargariBohlin, Markus

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf