An AR-assisted Deep Reinforcement Learning-based approach towards mutual-cognitive safe human-robot interaction
With the emergence of Industry 5.0, the human-centric manufacturing paradigm requires manufacturing equipment (robots, etc.) interactively assist human workers to deal with dynamic and complex production tasks. To achieve symbiotic human–robot interaction (HRI), the safety issue serves as a prerequi...
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| Published in | Robotics and computer-integrated manufacturing Vol. 80; p. 102471 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.04.2023
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| Subjects | |
| Online Access | Get full text |
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| Abstract | With the emergence of Industry 5.0, the human-centric manufacturing paradigm requires manufacturing equipment (robots, etc.) interactively assist human workers to deal with dynamic and complex production tasks. To achieve symbiotic human–robot interaction (HRI), the safety issue serves as a prerequisite foundation. Regarding the growing individualized demand of manufacturing tasks, the conventional rule-based safe HRI measures could not well address the safety requirements due to inflexibility and lacking synergy. To fill the gap, this work proposes a mutual-cognitive safe HRI approach including worker visual augmentation, robot velocity control, Digital Twin-enabled motion preview and collision detection, and Deep Reinforcement Learning-based robot collision avoidance motion planning in the Augmented Reality-assisted manner. Finally, the feasibility of the system design and the performance of the proposed approach are validated by establishing and executing the prototype HRI system in a practical scene.
•Introduced an AR-assisted system architecture for mutual-cognitive safe HRI.•Implemented distance-based robot velocity control and area-based workers’ visual aids functions.•Developed the robot DT-enabled motion preview for workers to enhance safe cognition and for robots to detect collision.•Proposed a curriculum learning-based DRL motion planning policy for collision avoidance. |
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| AbstractList | With the emergence of Industry 5.0, the human-centric manufacturing paradigm requires manufacturing equipment (robots, etc.) interactively assist human workers to deal with dynamic and complex production tasks. To achieve symbiotic human–robot interaction (HRI), the safety issue serves as a prerequisite foundation. Regarding the growing individualized demand of manufacturing tasks, the conventional rule-based safe HRI measures could not well address the safety requirements due to inflexibility and lacking synergy. To fill the gap, this work proposes a mutual-cognitive safe HRI approach including worker visual augmentation, robot velocity control, Digital Twin-enabled motion preview and collision detection, and Deep Reinforcement Learning-based robot collision avoidance motion planning in the Augmented Reality-assisted manner. Finally, the feasibility of the system design and the performance of the proposed approach are validated by establishing and executing the prototype HRI system in a practical scene.
•Introduced an AR-assisted system architecture for mutual-cognitive safe HRI.•Implemented distance-based robot velocity control and area-based workers’ visual aids functions.•Developed the robot DT-enabled motion preview for workers to enhance safe cognition and for robots to detect collision.•Proposed a curriculum learning-based DRL motion planning policy for collision avoidance. |
| ArticleNumber | 102471 |
| Author | Li, Chengxi Yin, Yue Huo, Shengzeng Zheng, Pai Pang, Yat Ming |
| Author_xml | – sequence: 1 givenname: Chengxi orcidid: 0000-0003-1921-7448 surname: Li fullname: Li, Chengxi organization: Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China – sequence: 2 givenname: Pai orcidid: 0000-0002-2329-8634 surname: Zheng fullname: Zheng, Pai email: pai.zheng@polyu.edu.hk organization: Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China – sequence: 3 givenname: Yue surname: Yin fullname: Yin, Yue organization: Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China – sequence: 4 givenname: Yat Ming surname: Pang fullname: Pang, Yat Ming organization: Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China – sequence: 5 givenname: Shengzeng surname: Huo fullname: Huo, Shengzeng organization: Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China |
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| Keywords | Deep reinforcement learning Manufacturing safety Human robot interaction Augmented reality Smart manufacturing |
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| SubjectTerms | Augmented reality Deep reinforcement learning Human robot interaction Manufacturing safety Smart manufacturing |
| Title | An AR-assisted Deep Reinforcement Learning-based approach towards mutual-cognitive safe human-robot interaction |
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