Proactive human–robot collaboration: Mutual-cognitive, predictable, and self-organising perspectives

Human–Robot Collaboration (HRC) has a pivotal role in smart manufacturing for strict requirements of human-centricity, sustainability, and resilience. However, existing HRC development mainly undertakes either a human-dominant or robot-dominant manner, where human and robotic agents reactively perfo...

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Published inRobotics and computer-integrated manufacturing Vol. 81; p. 102510
Main Authors Li, Shufei, Zheng, Pai, Liu, Sichao, Wang, Zuoxu, Wang, Xi Vincent, Zheng, Lianyu, Wang, Lihui
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.06.2023
Subjects
Human-centric manufacturing
Human-robot collaboration
Industrial Internet-of-Things
Industry 5
Industry 5.0
Smart manufacturing
Industrial Internet-of-Things
Industry 5.0
Human-centric manufacturing
Human–robot collaboration
Smart manufacturing
Online AccessGet full text

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Abstract Human–Robot Collaboration (HRC) has a pivotal role in smart manufacturing for strict requirements of human-centricity, sustainability, and resilience. However, existing HRC development mainly undertakes either a human-dominant or robot-dominant manner, where human and robotic agents reactively perform operations by following pre-defined instructions, thus far from an efficient integration of robotic automation and human cognition. The stiff human–robot relations fail to be qualified for complex manufacturing tasks and cannot ease the physical and psychological load of human operators. In response to these realistic needs, this paper presents our arguments on the obvious trend, concept, systematic architecture, and enabling technologies of Proactive HRC, serving as a prospective vision and research topic for future work in the human-centric smart manufacturing era. Human–robot symbiotic relation is evolving with a 5C intelligence — from Connection, Coordination, Cyber, Cognition to Coevolution, and finally embracing mutual-cognitive, predictable, and self-organising intelligent capabilities, i.e., the Proactive HRC. With proactive robot control, multiple human and robotic agents collaboratively operate manufacturing tasks, considering each others’ operation needs, desired resources, and qualified complementary capabilities. This paper also highlights current challenges and future research directions, which deserve more research efforts for real-world applications of Proactive HRC. It is hoped that this work can attract more open discussions and provide useful insights to both academic and industrial practitioners in their exploration of human–robot flexible production.
AbstractList Human-Robot Collaboration (HRC) has a pivotal role in smart manufacturing for strict requirements of human -centricity, sustainability, and resilience. However, existing HRC development mainly undertakes either a human-dominant or robot-dominant manner, where human and robotic agents reactively perform operations by following pre-defined instructions, thus far from an efficient integration of robotic automation and human cognition. The stiff human-robot relations fail to be qualified for complex manufacturing tasks and cannot ease the physical and psychological load of human operators. In response to these realistic needs, this paper presents our arguments on the obvious trend, concept, systematic architecture, and enabling technologies of Proactive HRC, serving as a prospective vision and research topic for future work in the human-centric smart manufacturing era. Human-robot symbiotic relation is evolving with a 5C intelligence - from Connection, Coordination, Cyber, Cognition to Coevolution, and finally embracing mutual-cognitive, predictable, and self -organising intelligent capabilities, i.e., the Proactive HRC. With proactive robot control, multiple human and robotic agents collaboratively operate manufacturing tasks, considering each others' operation needs, desired resources, and qualified complementary capabilities. This paper also highlights current challenges and future research directions, which deserve more research efforts for real-world applications of Proactive HRC. It is hoped that this work can attract more open discussions and provide useful insights to both academic and industrial practitioners in their exploration of human-robot flexible production.
ArticleNumber 102510
Author Wang, Lihui
Zheng, Lianyu
Zheng, Pai
Li, Shufei
Liu, Sichao
Wang, Xi Vincent
Wang, Zuoxu
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  organization: Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China
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  givenname: Pai
  surname: Zheng
  fullname: Zheng, Pai
  email: pai.zheng@polyu.edu.hk
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  givenname: Sichao
  surname: Liu
  fullname: Liu, Sichao
  organization: Department of Production Engineering, KTH Royal Institute of Technology, Sweden
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  givenname: Zuoxu
  surname: Wang
  fullname: Wang, Zuoxu
  organization: School of Mechanical Engineering and Automation, Beihang University, Beijing, China
– sequence: 5
  givenname: Xi Vincent
  surname: Wang
  fullname: Wang, Xi Vincent
  organization: Department of Production Engineering, KTH Royal Institute of Technology, Sweden
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  fullname: Zheng, Lianyu
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  givenname: Lihui
  surname: Wang
  fullname: Wang, Lihui
  email: lihuiw@kth.se
  organization: Department of Production Engineering, KTH Royal Institute of Technology, Sweden
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Keywords Industrial Internet-of-Things
Industry 5.0
Human-centric manufacturing
Human–robot collaboration
Smart manufacturing
Language English
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Snippet Human–Robot Collaboration (HRC) has a pivotal role in smart manufacturing for strict requirements of human-centricity, sustainability, and resilience. However,...
Human-Robot Collaboration (HRC) has a pivotal role in smart manufacturing for strict requirements of human -centricity, sustainability, and resilience....
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StartPage 102510
SubjectTerms Human-centric manufacturing
Human-robot collaboration
Industrial Internet-of-Things
Industry 5
Industry 5.0
Smart manufacturing
Title Proactive human–robot collaboration: Mutual-cognitive, predictable, and self-organising perspectives
URI https://dx.doi.org/10.1016/j.rcim.2022.102510
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