TY - JOUR
T1 - Deep learning-based human motion recognition for predictive context-aware human-robot collaboration
AU - Wang, Peng
AU - Liu, Hongyi
AU - Wang, Lihui
AU - Gao, Robert X.
N1 - Publisher Copyright:
© 2018
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Timely context awareness is key to improving operation efficiency and safety in human-robot collaboration (HRC) for intelligent manufacturing. Visual observation of human workers’ motion provides informative clues about the specific tasks to be performed, thus can be explored for establishing accurate and reliable context awareness. Towards this goal, this paper investigates deep learning as a data driven technique for continuous human motion analysis and future HRC needs prediction, leading to improved robot planning and control in accomplishing a shared task. A case study in engine assembly is carried out to validate the feasibility of the proposed method.
AB - Timely context awareness is key to improving operation efficiency and safety in human-robot collaboration (HRC) for intelligent manufacturing. Visual observation of human workers’ motion provides informative clues about the specific tasks to be performed, thus can be explored for establishing accurate and reliable context awareness. Towards this goal, this paper investigates deep learning as a data driven technique for continuous human motion analysis and future HRC needs prediction, leading to improved robot planning and control in accomplishing a shared task. A case study in engine assembly is carried out to validate the feasibility of the proposed method.
KW - Machine learning
KW - Motion
KW - Predictive model
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U2 - 10.1016/j.cirp.2018.04.066
DO - 10.1016/j.cirp.2018.04.066
M3 - Article
AN - SCOPUS:85046730213
SN - 0007-8506
VL - 67
SP - 17
EP - 20
JO - CIRP Annals - Manufacturing Technology
JF - CIRP Annals - Manufacturing Technology
IS - 1
ER -