Deep learning-based human motion recognition for predictive context-aware human-robot collaboration

被引：146
|
作者：
Wang, Peng [1 ]
Liu, Hongyi [2 ]
Wang, Lihui [2 ]
Gao, Robert X. [1 ]
机构：
[1] Case Western Reserve Univ, Dept Mech & Aerosp Engn, Cleveland, OH 44106 USA
[2] KTH Royal Inst Technol, Dept Prod Engn, Stockholm, Sweden
来源：
CIRP ANNALS-MANUFACTURING TECHNOLOGY | 2018年 / 67卷 / 01期
关键词：
Motion; Predictive model; Machine learning;
D O I：
10.1016/j.cirp.2018.04.066
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
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. (C) 2018 Published by Elsevier Ltd on behalf of CIRP.
引用
收藏
页码：17 / 20
页数：4
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