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Sökning: id:"swepub:oai:DiVA.org:kth-345692" > Unleashing mixed-re...

Unleashing mixed-reality capability in Deep Reinforcement Learning-based robot motion generation towards safe human–robot collaboration

Li, Chengxi (författare)
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China; Laboratory for Artificial Intelligence in Design, Hong Kong Science Park, Hong Kong Special Administrative Region, China; Department of Aerospace and Mechanical Engineering, Viterbi School of Engineering, University of Southern California, L.A., United States
Zheng, Pai (författare)
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China; Laboratory for Artificial Intelligence in Design, Hong Kong Science Park, Hong Kong Special Administrative Region, China
Zhou, Peng (författare)
Department of Computer Science, The University of Hong Kong, Hong Kong Special Administrative Region, China
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Yin, Yue (författare)
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
Lee, Carman K.M. (författare)
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China; Laboratory for Artificial Intelligence in Design, Hong Kong Science Park, Hong Kong Special Administrative Region, China
Wang, Lihui (författare)
KTH,Produktionsutveckling
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Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China; Laboratory for Artificial Intelligence in Design, Hong Kong Science Park, Hong Kong Special Administrative Region, China; Department of Aerospace and Mechanical Engineering, Viterbi School of Engineering, University of Southern California, LA., United States Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China; Laboratory for Artificial Intelligence in Design, Hong Kong Science Park, Hong Kong Special Administrative Region, China (creator_code:org_t)
Elsevier B.V. 2024
2024
Engelska.
Ingår i: Journal of manufacturing systems. - : Elsevier B.V.. - 0278-6125 .- 1878-6642. ; 74, s. 411-421
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • The integration of human–robot collaboration yields substantial benefits, particularly in terms of enhancing flexibility and efficiency within a range of mass-personalized manufacturing tasks, for example, small-batch customized product inspection and assembly/disassembly. Meanwhile, as human–robot collaboration lands broader in manufacturing, the unstructured scene and operator uncertainties are increasingly involved and considered. Consequently, it becomes imperative for robots to execute in a safe and adaptive manner rather than solely relying on pre-programmed instructions. To tackle it, a systematic solution for safe robot motion generation in human–robot collaborative activities is proposed, leveraging mixed-reality technologies and Deep Reinforcement Learning. This solution covers the entire process of collaboration starting with an intuitive interface that facilitates bare-hand task command transmission and scene coordinate transformation before the collaboration begins. In particular, mixed-reality devices are employed as effective tools for representing the state of humans, robots, and scenes. This enables the learning of an end-to-end Deep Reinforcement Learning policy that addresses both the uncertainties in robot perception and decision-making in an integrated manner. The proposed solution also implements policy simulation-to-reality deployment, along with motion preview and collision detection mechanisms, to ensure safe robot motion execution. It is hoped that this work could inspire further research in human–robot collaboration to unleash and exploit the powerful capabilities of mixed reality.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Robotteknik och automation (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Robotics (hsv//eng)

Nyckelord

Deep Reinforcement Learning
Human–robot collaboration
Manufacturing safety
Mixed reality
Smart manufacturing

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