Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.4323
Publisher DOI: 10.1016/j.procir.2022.02.184
Title: A methods-time-measurement based approach to enable action recognition for multi-variant assembly in Human-Robot Collaboration
Language: English
Authors: Koch, Julian  
Büsch, Lukas 
Gomse, Martin 
Schüppstuhl, Thorsten  
Keywords: 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
Issue Date: 10-Mar-2022
Publisher: Elsevier
Source: Procedia CIRP 106: 233-238 (2022)
Journal: Procedia CIRP 
Abstract (english): 
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.
Conference: 9th CIRP Conference on Assembly Technology and Systems, CATS 2022 
URI: http://hdl.handle.net/11420/12418
DOI: 10.15480/882.4323
ISSN: 2212-8271
Institute: Flugzeug-Produktionstechnik M-23 
Document Type: Chapter/Article (Proceedings)
License: CC BY-NC-ND 4.0 (Attribution-NonCommercial-NoDerivatives) CC BY-NC-ND 4.0 (Attribution-NonCommercial-NoDerivatives)
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