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Deep Learning-based Multimodal Control Interface for Human-Robot Collaboration
KTH, School of Industrial Engineering and Management (ITM), Sustainable production development.
KTH.
KTH.
KTH, School of Industrial Engineering and Management (ITM), Sustainable production development.ORCID iD: 0000-0002-4032-4830
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2018 (English)In: 51st CIRP Conference on Manufacturing Systems, Elsevier, 2018, Vol. 72, p. 3-8Conference paper, Published paper (Refereed)
Abstract [en]

In human-robot collaborative manufacturing, industrial robot is required to dynamically change its pre-programmed tasks and collaborate with human operators at the same workstation. However, traditional industrial robot is controlled by pre-programmed control codes, which cannot support the emerging needs of human-robot collaboration. In response to the request, this research explored a deep learning-based multimodal robot control interface for human-robot collaboration. Three methods were integrated into the multimodal interface, including voice recognition, hand motion recognition, and body posture recognition. Deep learning was adopted as the algorithm for classification and recognition. Human-robot collaboration specific datasets were collected to support the deep learning algorithm. The result presented at the end of the paper shows the potential to adopt deep learning in human-robot collaboration systems.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 72, p. 3-8
Series
Procedia CIRP, ISSN 2212-8271 ; 72
Keywords [en]
Deep learning, Human-robot collaboration, Robot control
National Category
Interaction Technologies
Identifiers
URN: urn:nbn:se:kth:diva-238387DOI: 10.1016/j.procir.2018.03.224Scopus ID: 2-s2.0-85049596121OAI: oai:DiVA.org:kth-238387DiVA, id: diva2:1263729
Conference
51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018; Stockholm Waterfront Congress CentreStockholm; Sweden; 16 May 2018 through 18 May 2018
Note

QC 20181116

Available from: 2018-11-16 Created: 2018-11-16 Last updated: 2018-11-16Bibliographically approved

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Wang, YuquanWang, Lihui

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Liu, HongyiFang, TongtongZhou, TianyuWang, YuquanWang, Lihui
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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
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