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A Cognitive Digital Twins Framework for Human-Robot Collaboration
Dalian Univ Technol, Dept Mech Engn, Dalian, Peoples R China..
Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan, Peoples R China..
KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Sustainable Production Systems.ORCID iD: 0000-0001-8679-8049
Univ Calabria, Dept Mech Energy & Management Engn DIMEG, Arcavacata Di Rende, Italy..
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2022 (English)In: 3Rd International Conference On Industry 4.0 And Smart Manufacturing / [ed] Longo, F Affenzeller, M Padovano, A, Elsevier BV , 2022, Vol. 200, p. 1867-1874Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes a cognitive digital twin framework for smart manufacturing, and especially for human-robot-collaboration cases. The proposed framework comprises three layers (field, edge, and cloud layers) based on the 5G communication network. In the field layer, the physical twin's data from the physical machine and human operators are transmitted through the edge layer and then to the cloud layer to virtualize the digital twin. The cloud layer generates inference model generation by deep learning training and updates the inference model in the edge layer to make the field's machine smart. Especially, human operators' models are built based on the multimodal fusion in the cloud layer for cognitive function. Also, edge-cloud collaborative computing is presented to implement the proposed framework. Finally, the study is validated with a human-robot-collaboration case involving 5G edge computing.

Place, publisher, year, edition, pages
Elsevier BV , 2022. Vol. 200, p. 1867-1874
Series
Procedia Computer Science, ISSN 1877-0509
Keywords [en]
Digital twin, human-robot collaboration, human cyber-physical system, 5G communication network, edge-cloud collaboration
National Category
Computer Systems Computer Sciences Communication Systems
Identifiers
URN: urn:nbn:se:kth:diva-312223DOI: 10.1016/j.procs.2022.01.387ISI: 000777601300190Scopus ID: 2-s2.0-85127779613OAI: oai:DiVA.org:kth-312223DiVA, id: diva2:1658273
Conference
3rd International Conference on Industry 4.0 and Smart Manufacturing (ISM), NOV 17-19, 2021, Upper Austria Univ Appl Sci, Hagenberg Campus, Linz, AUSTRIA
Note

QC 20220516

Available from: 2022-05-16 Created: 2022-05-16 Last updated: 2022-06-25Bibliographically approved

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Wang, Lihui

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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  • de-DE
  • en-GB
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Output format
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