Robotic Edge Intelligence for Energy-Efficient Human–Robot Collaboration

Zhengying Cai (), Xiangyu Du, Tianhao Huang, Tianrui Lv, Zhiheng Cai and Guoqiang Gong
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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

Sustainability, 2024, vol. 16, issue 22, 1-19

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.

Keywords: human–robot collaboration (HRC); energy-efficient; edge computing; artificial plant community algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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