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Research Perspectives in Collaborative Assembly: A Review

Authors :
Thierry Yonga Chuengwa
Jan Adriaan Swanepoel
Anish Matthew Kurien
Mukondeleli Grace Kanakana-Katumba
Karim Djouani
Source :
Robotics, Vol 12, Iss 2, p 37 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

In recent years, the emergence of Industry 4.0 technologies has introduced manufacturing disruptions that necessitate the development of accompanying socio-technical solutions. There is growing interest for manufacturing enterprises to embrace the drivers of the Smart Industry paradigm. Among these drivers, human–robot physical co-manipulation of objects has gained significant interest in the literature on assembly operations. Motivated by the requirement for human dyads between the human and the robot counterpart, this study investigates recent literature on the implementation methods of human–robot collaborative assembly scenarios. Using a combination of strings, the researchers performed a systematic review search, sourcing 451 publications from various databases (Science Direct (253), IEEE Xplore (49), Emerald (32), PudMed (21) and SpringerLink (96)). A coding assignment in Eppi-Reviewer helped screen the literature based on ‘exclude’ and ‘include’ criteria. The final number of full-text publications considered in this literature review is 118 peer-reviewed research articles published up until September 2022. The findings anticipate that research publications in the fields of human–robot collaborative assembly will continue to grow. Understanding and modeling the human interaction and behavior in robot co-assembly is crucial to the development of future sustainable smart factories. Machine vision and digital twins modeling begin to emerge as promising interfaces for the evaluation of tasks distribution strategies for mitigating the actual human ergonomic and safety risks in collaborative assembly solutions design.

Details

Language :
English
ISSN :
22186581
Volume :
12
Issue :
2
Database :
Directory of Open Access Journals
Journal :
Robotics
Publication Type :
Academic Journal
Accession number :
edsdoj.3141276791884bcda26b3cb61c0ed8ff
Document Type :
article
Full Text :
https://doi.org/10.3390/robotics12020037