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A methodology for implementation of mobile robot in adaptive manufacturing environments

Author

Listed:
  • Izabela Nielsen

    (Aalborg University)

  • Quang-Vinh Dang

    (Vietnamese-German University)

  • Grzegorz Bocewicz

    (Koszalin University of Technology)

  • Zbigniew Banaszak

    (Warsaw University of Technology)

Abstract

With the rapid development of technologies, many production systems and modes has been advanced with respect to manufacturing, management and information fields. The paper deals with the problem of the implementation of an autonomous industrial mobile robot in real-world industrial applications in which all these fields are considered, namely mobile robot technology, planning and scheduling and communication. A methodology for implementation consisting of: a mobile robot system design (Little Helper prototype), an appropriate industrial application (multiple-part feeding), an implementation concept for the industrial application (the Bartender Concept), a mathematical model and a genetic algorithm-based heuristic is proposed. Furthermore, in order for the mobile robot to work properly in a flexible (cloud-based) manufacturing environment, the communications and exchange of data between the mobile robot with other manufacturing systems and shop-floor operators are addressed in the methodology. The proposed methodology provides insight into how mobile robot technology and abilities contribute to cloud manufacturing systems. A real-world demonstration at an impeller production line in a factory and computational experiments are conducted to demonstrate the effectiveness of the proposed methodology.

Suggested Citation

  • Izabela Nielsen & Quang-Vinh Dang & Grzegorz Bocewicz & Zbigniew Banaszak, 2017. "A methodology for implementation of mobile robot in adaptive manufacturing environments," Journal of Intelligent Manufacturing, Springer, vol. 28(5), pages 1171-1188, June.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:5:d:10.1007_s10845-015-1072-2
    DOI: 10.1007/s10845-015-1072-2
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    References listed on IDEAS

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    Cited by:

    1. Giuseppe Fragapane & Dmitry Ivanov & Mirco Peron & Fabio Sgarbossa & Jan Ola Strandhagen, 2022. "Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics," Annals of Operations Research, Springer, vol. 308(1), pages 125-143, January.
    2. Congcong Ye & Jixiang Yang & Han Ding, 2022. "Bagging for Gaussian mixture regression in robot learning from demonstration," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 867-879, March.
    3. Anja Poberznik & Mirka Leino & Jenni Huhtasalo & Taina Jyräkoski & Pauli Valo & Tommi Lehtinen & Joonas Kortelainen & Sari Merilampi & Johanna Virkki, 2021. "Mobile Robots and RFID Technology-Based Smart Care Environment for Minimizing Risks Related to Employee Turnover during Pandemics," Sustainability, MDPI, vol. 13(22), pages 1-12, November.
    4. 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.
    5. Morett, Emilio & Tappia, Elena & Melacini, Marco, 2021. "Scheduling mobile robots in part feeding systems," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 129-149, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.

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