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An attention-based ...
An attention-based deep learning approach for inertial motion recognition and estimation in human-robot collaboration
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- Zhou, Huiying (författare)
- Zhejiang Univ, Sch Mech Engn, State Key Lab Fluid Power & Mechatron Syst, Hangzhou 310058, Peoples R China.
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- Yang, Geng (författare)
- Zhejiang Univ, Sch Mech Engn, State Key Lab Fluid Power & Mechatron Syst, Hangzhou 310058, Peoples R China.
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- Wang, Baicun (författare)
- Zhejiang Univ, Sch Mech Engn, State Key Lab Fluid Power & Mechatron Syst, Hangzhou 310058, Peoples R China.
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- Li, Xingyu (författare)
- Univ Michigan, Dept Mech Engn, Ann Arbor, MI 48109 USA.
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- Wang, Ruohan (författare)
- Zhejiang Univ, Sch Mech Engn, State Key Lab Fluid Power & Mechatron Syst, Hangzhou 310058, Peoples R China.
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- Huang, Xiaoyan (författare)
- Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Peoples R China.
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- Wu, Haiteng (författare)
- Hangzhou Shenhao Technol Co Ltd, Hangzhou 310000, Peoples R China.
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- Wang, Xi Vincent, Dr. 1985- (författare)
- KTH,Produktionsutveckling
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Zhejiang Univ, Sch Mech Engn, State Key Lab Fluid Power & Mechatron Syst, Hangzhou 310058, Peoples R China Univ Michigan, Dept Mech Engn, Ann Arbor, MI 48109 USA. (creator_code:org_t)
- Elsevier BV, 2023
- 2023
- Engelska.
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Ingår i: Journal of manufacturing systems. - : Elsevier BV. - 0278-6125 .- 1878-6642. ; 67, s. 97-110
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- In line with a human-centric smart manufacturing vision, human-robot collaboration is striving to combine robots' high efficiency and quality with humans' rapid adaptability and high flexibility. In particular, perception, recognition and estimation of human motion determine when and what robot to collaborate with humans. This work presents an attention-based deep learning approach for inertial motion recognition and estimation in order to infer when robotic assistance will be requested by the human and to allow the robot to perform partial human tasks. First, in the stage of motion perception and recognition, quaternion-based calibration and forward kinematic analysis methods enable the reconstruction of human motion based on data streaming from an inertial motion capture device. Then, in the stage of motion estimation, residual module and Bidirectional Long ShortTerm Memory module are integrated with proposed attention mechanism for estimating arm motion trajectories further. Experimental results show the effectiveness of the proposed approach in achieving better recognition and estimation in comparison with traditional approaches and existing deep learning approaches. It is experimentally verified in a laboratory environment involving a collaborative robot employed in a small part assembly task.
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
- Human motion capture
- Human-centric smart manufacturing
- Motion tracking
- Human -robot collaboration
- Human-cyber-physical systems
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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