北京航空航天大学学报 ›› 2019, Vol. 45 ›› Issue (1): 35-43.doi: 10.13700/j.bh.1001-5965.2018.0256

• 论文 • 上一篇    下一篇

人机协作中人的动作终点预测

陈友东, 刘嘉蕾, 胡澜晓   

  1. 北京航空航天大学 机械工程及自动化学院, 北京 100083
  • 收稿日期:2018-05-04 修回日期:2018-06-08 出版日期:2019-01-20 发布日期:2019-01-28
  • 通讯作者: 陈友东 E-mail:chenyd@buaa.edu.cn
  • 作者简介:陈友东,男,博士,副教授。主要研究方向:机器人控制、人机协作;刘嘉蕾,男,硕士研究生。主要研究方向:人机协作;胡澜晓,男,硕士研究生。主要研究方向:机器人动力学与控制。
  • 基金资助:
    国家科技支撑计划(2015BAF01B04);北京市科技计划(D161100003116002)

Human motion end point prediction in human-robot collaboration

CHEN Youdong, LIU Jialei, HU Lanxiao   

  1. School of Mechanical Engineering and Automation, Beihang University, Beijing 100083, China
  • Received:2018-05-04 Revised:2018-06-08 Online:2019-01-20 Published:2019-01-28
  • Supported by:
    National Key Technology Research and Development Program of China (2015BAF01B04); Beijing Science and Technology Plan (D161100003116002)

摘要: 为实现安全高效的人机协作(HRC),需要机器人及时对人的动作做出预测,从而积极主动地辅助人工作。为解决在HRC装配场景中机器人对人的动作终点预测问题,提出了一种基于长短时记忆(LSTM)网络的动作终点预测方法。在训练阶段,用人的动作序列与对应的动作终点组成的样本训练LSTM网络,构建动作序列与动作终点之间的映射。在应用阶段,根据人的动作的初始部分对动作终点提前做出预测。通过在装配场景中,对人抓取工具或零件的动作终点进行预测,验证了所提方法的有效性。在观测到50%的动作片段时,预测准确率达到80%以上。

关键词: 人机协作(HRC), 终点预测, 伸及动作, 长短时记忆(LSTM), 意图识别

Abstract: 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%.

Key words: human-robot collaboration (HRC), end point prediction, reaching motion, long short-term memory (LSTM), intention recognition

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