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Collaborative robots in manufacturing and assembly systems: literature review and future research agenda

Author

Listed:
  • Ali Keshvarparast

    (University of Padua)

  • Daria Battini

    (University of Padua)

  • Olga Battaia

    (KEDGE Business School Campus Bordeaux)

  • Amir Pirayesh

    (KEDGE Business School Campus Bordeaux)

Abstract

Nowadays, considering the constant changes in customers’ demands, manufacturing systems tend to move more and more towards customization while ensuring the expected reactivity. In addition, more attention is given to the human factors to, on the one hand, create opportunities for improving the work conditions such as safety and, on the other hand, reduce the risks brought by new technologies such as job cannibalization. Meanwhile, Industry 4.0 offers new ways to facilitate this change by enhancing human–machine interactions using Collaborative Robots (Cobots). Recent research studies have shown that cobots may bring numerous advantages to manufacturing systems, especially by improving their flexibility. This research investigates the impacts of the integration of cobots in the context of assembly and disassembly lines. For this purpose, a Systematic Literature Review (SLR) is performed. The existing contributions are classified on the basis of the subject of study, methodology, methodology, performance criteria, and type of Human-Cobot collaboration. Managerial insights are provided, and research perspectives are discussed.

Suggested Citation

  • Ali Keshvarparast & Daria Battini & Olga Battaia & Amir Pirayesh, 2024. "Collaborative robots in manufacturing and assembly systems: literature review and future research agenda," Journal of Intelligent Manufacturing, Springer, vol. 35(5), pages 2065-2118, June.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:5:d:10.1007_s10845-023-02137-w
    DOI: 10.1007/s10845-023-02137-w
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    References listed on IDEAS

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    1. Patrik Fager & Fabio Sgarbossa & Martina Calzavara, 2021. "Cost modelling of onboard cobot-supported item sorting in a picking system," International Journal of Production Research, Taylor & Francis Journals, vol. 59(11), pages 3269-3284, June.
    2. Bogataj, David & Battini, Daria & Calzavara, Martina & Persona, Alessandro, 2019. "The ageing workforce challenge: Investments in collaborative robots or contribution to pension schemes, from the multi-echelon perspective," International Journal of Production Economics, Elsevier, vol. 210(C), pages 97-106.
    3. Christian Weckenborg & Karsten Kieckhäfer & Christoph Müller & Martin Grunewald & Thomas S. Spengler, 2020. "Balancing of assembly lines with collaborative robots," Business Research, Springer;German Academic Association for Business Research, vol. 13(1), pages 93-132, April.
    4. Giovanni Boschetti & Matteo Bottin & Maurizio Faccio & Riccardo Minto, 2021. "Multi-robot multi-operator collaborative assembly systems: a performance evaluation model," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1455-1470, June.
    5. Luca Gualtieri & Ilaria Palomba & Fabio Antonio Merati & Erwin Rauch & Renato Vidoni, 2020. "Design of Human-Centered Collaborative Assembly Workstations for the Improvement of Operators’ Physical Ergonomics and Production Efficiency: A Case Study," Sustainability, MDPI, vol. 12(9), pages 1-23, April.
    6. Ahmed R. Sadik & Bodo Urban, 2017. "An Ontology-Based Approach to Enable Knowledge Representation and Reasoning in Worker–Cobot Agile Manufacturing," Future Internet, MDPI, vol. 9(4), pages 1-23, November.
    7. Miguel Vieira & Samuel Moniz & Bruno S. Gonçalves & Tânia Pinto-Varela & Ana Paula Barbosa-Póvoa & Pedro Neto, 2022. "A two-level optimisation-simulation method for production planning and scheduling: the industrial case of a human–robot collaborative assembly line," International Journal of Production Research, Taylor & Francis Journals, vol. 60(9), pages 2942-2962, May.
    8. Shirine El Zaatari & Yuqi Wang & Yudie Hu & Weidong Li, 2022. "An improved approach of task-parameterized learning from demonstrations for cobots in dynamic manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1503-1519, June.
    9. Guanghui Zhou & Chao Zhang & Zhi Li & Kai Ding & Chuang Wang, 2020. "Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 58(4), pages 1034-1051, February.
    10. Ahmed R. Sadik & Bodo Urban, 2017. "Flow Shop Scheduling Problem and Solution in Cooperative Robotics—Case-Study: One Cobot in Cooperation with One Worker," Future Internet, MDPI, vol. 9(3), pages 1-15, August.
    11. Timo Bänziger & Andreas Kunz & Konrad Wegener, 2020. "Optimizing human–robot task allocation using a simulation tool based on standardized work descriptions," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1635-1648, October.
    12. Mirco Peron & Fabio Sgarbossa & Jan Ola Strandhagen, 2022. "Decision support model for implementing assistive technologies in assembly activities: a case study," International Journal of Production Research, Taylor & Francis Journals, vol. 60(4), pages 1341-1367, February.
    13. Yuval Cohen & Shraga Shoval & Maurizio Faccio & Riccardo Minto, 2022. "Deploying cobots in collaborative systems: major considerations and productivity analysis," International Journal of Production Research, Taylor & Francis Journals, vol. 60(6), pages 1815-1831, March.
    14. Maurizio Faccio & Irene Granata & Alberto Menini & Mattia Milanese & Chiara Rossato & Matteo Bottin & Riccardo Minto & Patrik Pluchino & Luciano Gamberini & Giovanni Boschetti & Giulio Rosati, 2023. "Human factors in cobot era: a review of modern production systems features," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 85-106, January.
    15. Niloofar Katiraee & Martina Calzavara & Serena Finco & Daria Battini & Olga Battaïa, 2021. "Consideration of workers’ differences in production systems modelling and design: State of the art and directions for future research," International Journal of Production Research, Taylor & Francis Journals, vol. 59(11), pages 3237-3268, June.
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