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Robotic Edge Intelligence for Energy-Efficient Human–Robot Collaboration

Author

Listed:
  • Zhengying Cai

    (Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipments, College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China)

  • Xiangyu Du

    (Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipments, College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China)

  • Tianhao Huang

    (Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipments, College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China)

  • Tianrui Lv

    (Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipments, College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China)

  • Zhiheng Cai

    (Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipments, College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China)

  • Guoqiang Gong

    (Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipments, College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China)

Abstract

Energy-efficient human–robot collaboration poses significant challenges to the sustainable operation of production systems. Therefore, our work proposes novel robotic edge intelligence to address the issue. First, robotic edge intelligence is proposed to fully utilize the embedded computing capabilities of edge robots, and the state transition diagrams are developed for jobs, humans, and robots, respectively. Second, a multi-objective model is designed for the energy-efficient human–robot scheduling problem to evaluate the production performance and energy efficiency as a whole. Third, a heuristic algorithm is developed to search for the optimal solutions based on an artificial plant community, which is lightweight enough to be run on edge robots. Finally, a benchmark data set is developed, and a series of benchmark experiments are implemented to test the proposed system. The results demonstrate that the proposed method can effectively enhance energy efficiency and production performance with satisfying solution performance.

Suggested Citation

  • Zhengying Cai & Xiangyu Du & Tianhao Huang & Tianrui Lv & Zhiheng Cai & Guoqiang Gong, 2024. "Robotic Edge Intelligence for Energy-Efficient Human–Robot Collaboration," Sustainability, MDPI, vol. 16(22), pages 1-19, November.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:22:p:9788-:d:1517516
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    References listed on IDEAS

    as
    1. Zhengying Cai & Xiaolu Wang & Rui Li & Qi Gao, 2023. "An Artificial Physarum polycephalum Colony for the Electric Location-Routing Problem," Sustainability, MDPI, vol. 15(23), pages 1-29, November.
    2. Vitali Czymmek & Carolin Köhn & Leif Ole Harders & Stephan Hussmann, 2023. "Review of Energy-Efficient Embedded System Acceleration of Convolution Neural Networks for Organic Weeding Robots," Agriculture, MDPI, vol. 13(11), pages 1-19, November.
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