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A methods-time-measurement based approach to enable action recognition for multi-variant assembly in Human-Robot Collaboration
Citation Link: https://doi.org/10.15480/882.4323
Publikationstyp
Conference Paper
Publikationsdatum
2022-03-10
Sprache
English
Institut
Enthalten in
Volume
106
Start Page
233
End Page
238
Citation
Procedia CIRP 106: 233-238 (2022)
Contribution to Conference
Publisher DOI
Scopus ID
Publisher
Elsevier
Action Recognition (AR) has become a popular approach to ensure efficient and safe Human-Robot Collaboration. Current research approaches are mostly optimized for specific assembly processes and settings. This paper introduces a novel approach to extend the field of AR to multi-variant assembly processes. The approach is based on generalized action primitives derived from Methods-Time-Measurement (MTM) analysis that are detected by an AR system using skeletal data. Subsequently a search algorithm combines the information from AR and MTM to provide an estimate of the assembly progress. One possible implementation is shown in a proof of concept and results as well as future work are discussed.
Schlagworte
Artificial Neural Network
Assembly
Assembly Step Recognition
Azure Kinect
Human Action Recognition
Human-Robot Collaboration
Industry 4.0
Methods-Time-Measurement
Particle Swarm Optimization
Skeleton Based Action Recognition
DDC Class
600: Technik
Publication version
publishedVersion
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