Contact State Clustering Analysis Based on Multivariate Time Series

被引：0

作者：

Liu N.-L. ^{[1
,2
]}

Zhou X.-D. ^{[3
,4
]}

Liu Z.-M. ^{[1
]}

Cui L. ^{[1
]}

机构：

[1] State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang

[2] Institutes for Robotics and Intelligent Manufacturing, University of Chinese Academy of Sciences, Shenyang

[3] Beijing Key Laboratory of Long-life Technology of Precise Rotation and Transmission Mechanisms, Beijing Institute of Control Engineering, Haidian, Beijing

[4] Science and Technology on Space Intelligent Control Laboratory, Haidian, Beijing

来源：

Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2020年 / 49卷 / 05期

关键词：

Cluster analysis; Contact state; Multivariate time series; Robot assembly; Unsupervised;

D O I：

10.12178/1001-0548.2020192

中图分类号：

学科分类号：

摘要：

Aiming at the classification problem of the contact state in the robot peg-in-hole task, this paper proposes a clustering method based on the multivariate time series. This method uses a deep temporal clustering network to encode the contact state variables in the assembly process, and then a complexity-invariant distance measure is used to classify the time series fragments. This method avoids the quasi-static analysis of the contact process and thus has a certain generality in practice. And the use of time series is beneficial to extract the time-related characteristics of contact state variables, which can make the clustering results more robust. The experimental results are consistent with expectations, indicating the theoretical correctness and effectiveness of the proposed algorithm. © 2020, Editorial Board of Journal of the University of Electronic Science and Technology of China. All right reserved.

引用

页码：660 / 665

页数：5

共 16 条

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