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Sustainable Human–Robot Collaboration Based on Human Intention Classification

Author

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  • Chiuhsiang Joe Lin

    (Department of Industrial Management, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Rd., Da’an District, Taipei City 106, Taiwan)

  • Rio Prasetyo Lukodono

    (Department of Industrial Management, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Rd., Da’an District, Taipei City 106, Taiwan)

Abstract

Sustainable manufacturing plays a role in ensuring products’ economic characteristics and reducing energy and resource consumption by improving the well-being of human workers and communities and maintaining safety. Using robots is one way for manufacturers to increase their sustainable manufacturing practices. Nevertheless, there are limitations to directly replacing humans with robots due to work characteristics and practical conditions. Collaboration between robots and humans should accommodate human capabilities while reducing loads and ineffective human motions to prevent human fatigue and maximize overall performance. Moreover, there is a need to establish early and fast communication between humans and machines in human–robot collaboration to know the status of the human in the activity and make immediate adjustments for maximum performance. This study used a deep learning algorithm to classify muscular signals of human motions with accuracy of 88%. It indicates that the signal could be used as information for the robot to determine the human motion’s intention during the initial stage of the entire motion. This approach can increase not only the communication and efficiency of human–robot collaboration but also reduce human fatigue by the early detection of human motion patterns. To enhance human well-being, it is suggested that a human–robot collaboration assembly line adopt similar technologies for a sustainable human–robot collaboration workplace.

Suggested Citation

  • Chiuhsiang Joe Lin & Rio Prasetyo Lukodono, 2021. "Sustainable Human–Robot Collaboration Based on Human Intention Classification," Sustainability, MDPI, vol. 13(11), pages 1-26, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:11:p:5990-:d:562489
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    References listed on IDEAS

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    1. Quan Liu & Zhihao Liu & Wenjun Xu & Quan Tang & Zude Zhou & Duc Truong Pham, 2019. "Human-robot collaboration in disassembly for sustainable manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 57(12), pages 4027-4044, June.
    2. Jia, Shun & Yuan, Qinghe & Lv, Jingxiang & Liu, Ying & Ren, Dawei & Zhang, Zhongwei, 2017. "Therblig-embedded value stream mapping method for lean energy machining," Energy, Elsevier, vol. 138(C), pages 1081-1098.
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    Cited by:

    1. Zhujun Zhang & Gaoliang Peng & Weitian Wang & Yi Chen, 2022. "Real-Time Human Fault Detection in Assembly Tasks, Based on Human Action Prediction Using a Spatio-Temporal Learning Model," Sustainability, MDPI, vol. 14(15), pages 1-26, July.

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