期刊
CIRP ANNALS-MANUFACTURING TECHNOLOGY
卷 69, 期 1, 页码 9-12出版社
ELSEVIER
DOI: 10.1016/j.cirp.2020.04.077
关键词
Assembly; Motion; Machine learning
资金
- US National Science Foundation [CMMI-1830295]
Effective and safe human-robot collaboration in assembly requires accurate prediction of human motion trajectory, given a sequence of past observations such that a robot can proactively provide assistance to improve operation efficiency while avoiding collision. This paper presents a deep learning-based method to parse visual observations of human actions in an assembly setting, and forecast the human operator's future motion trajectory for online robot action planning and execution. The method is built upon a recurrent neural network (RNN) that can learn the time dependent mechanisms underlying the human motions. The effectiveness of the developed method is demonstrated for an engine assembly. (C) 2020 CIRP. Published by Elsevier Ltd. All rights reserved.
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