Human motion end point prediction in human-robot collaboration

被引：0
|
作者：
Chen Y. [1 ]
Liu J. [1 ]
Hu L. [1 ]
机构：
[1] School of Mechanical Engineering and Automation, Beihang University, Beijing
来源：
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2019年 / 45卷 / 01期
关键词：
End point prediction; Human-robot collaboration (HRC); Intention recognition; Long short-term memory (LSTM); Reaching motion;
D O I：
10.13700/j.bh.1001-5965.2018.0256
中图分类号：
学科分类号：
摘要：
To realize a safe and effective human-robot collaboration (HRC), it is necessary for the robot to predict human motions in a timely manner, so as to assist human more actively in the cooperative work. In order to solve the problem of human motion prediction in HRC assembly scenario, a motion end point prediction method based on long short-term memory (LSTM) network is proposed. In the training phase, the LSTM network is trained with samples of human motion sequences and corresponding motion end points, and the mapping between motion sequences and motion end points is constructed. In the application phase, the motion end point is predicted in advance based on the initial part of the human motion sequence. The effectiveness of the proposed method is verified by predicting the end points of motion of a human grasping tool or part in an assembly scenario. When 50% of the motion fragments are observed, the accuracy rate of prediction is above 80%. © 2019, Editorial Board of JBUAA. All right reserved.
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页码：35 / 43
页数：8
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