TY - JOUR
T1 - An AR-assisted Deep Reinforcement Learning-based approach towards mutual-cognitive safe human-robot interaction
AU - Li, Chengxi
AU - Zheng, Pai
AU - Yin, Yue
AU - Pang, Yat Ming
AU - Huo, Shengzeng
N1 - Funding Information:
The work described in this paper was partially supported by grants from the Research Grants Council of the Hong Kong SAR, China (Project No. PolyU 15210222); the Laboratory for Artificial Intelligence in Design (Project Code: RP2-1), Innovation and Technology Fund, Hong Kong SAR, China; and the Collaborative Departmental General Research Fund (G-UAMS) from the Hong Kong Polytechnic University, Hong Kong SAR, China.
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2023/4
Y1 - 2023/4
N2 - 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.
AB - 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.
KW - Augmented reality
KW - Deep reinforcement learning
KW - Human robot interaction
KW - Manufacturing safety
KW - Smart manufacturing
UR - http://www.scopus.com/inward/record.url?scp=85139873087&partnerID=8YFLogxK
U2 - 10.1016/j.rcim.2022.102471
DO - 10.1016/j.rcim.2022.102471
M3 - Journal article
AN - SCOPUS:85139873087
SN - 0736-5845
VL - 80
JO - Robotics and Computer-Integrated Manufacturing
JF - Robotics and Computer-Integrated Manufacturing
M1 - 102471
ER -