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Optimizing human–robot task allocation using a simulation tool based on standardized work descriptions

Author

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  • Timo Bänziger

    (Smart Production Lab)

  • Andreas Kunz

    (ETH Zürich)

  • Konrad Wegener

    (ETH Zürich)

Abstract

Human–robot collaboration is enabled by the digitization of production and has become a key technology for the factory of the future. It combines the strengths of both the human worker and the assistant robot and allows the implementation of an varying degree of automation in workplaces in order to meet the increasing demand of flexibility of manufacturing systems. Intelligent planning and control algorithms are needed for the organization of the work in hybrid teams of humans and robots. This paper introduces an approach to use standardized work description for automated procedure generation of mobile assistant robots. A simulation tool is developed that implements the procedure model and is therefore capable of calculating different objective parameters like production time or ergonomics during a production cycle as a function of the human–robot task allocation. The simulation is validated with an existing workplace in an assembly line at the Volkswagen plant in Wolfsburg, Germany. Furthermore, a new method is presented to optimize the task allocation in human–robot teams for a given workplace, using the simulation as fitness function in a genetic algorithm. The advantage of this new approach is the possibility to evaluate different distributions of the tasks, while considering the dynamics of the interaction between the worker and the robot in their shared workplace. Using the presented approach for a given workplace, an optimized human–robot task allocation is found, in which the tasks are allocated in an intelligent and comprehensible way.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:7:d:10.1007_s10845-018-1411-1
    DOI: 10.1007/s10845-018-1411-1
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    References listed on IDEAS

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    1. 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.
    2. Andrew Kusiak, 2017. "Smart manufacturing must embrace big data," Nature, Nature, vol. 544(7648), pages 23-25, April.
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    Cited by:

    1. 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.
    2. Ferreira, Cristiane & Figueira, Gonçalo & Amorim, Pedro, 2021. "Scheduling Human-Robot Teams in collaborative working cells," International Journal of Production Economics, Elsevier, vol. 235(C).

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