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Multi-dimensional Error Identification during Robotic Snap Assembly

  • Yusuke HayamiEmail author
  • Peihao Shi
  • Weiwei Wan
  • Ixchel G. Ramirez-Alpizar
  • Kensuke Harada
Conference paper
Part of the Mechanisms and Machine Science book series (Mechan. Machine Science, volume 73)

Abstract

In this research, we propose a novel error identification method during robotic snap assembly aiming at automated recovery from error states. In the proposed method, we first obtain the feature quantities of force/torque from the simulated snap assembly by using functional principal component analysis (fPCA). Then, we cluster these data into success and several different error states based on the k-means clustering by using the decision tree considering the multi-dimensional feature of the force/torque signal. Furthermore, we try to predict an error state of a snap assembly task before the error actually happens. Finally, we show simulation results to show the effectiveness of the proposed method.

Keywords

Snap assembly K-means clustering Functional PCA 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yusuke Hayami
    • 1
    Email author
  • Peihao Shi
    • 1
  • Weiwei Wan
    • 1
    • 2
  • Ixchel G. Ramirez-Alpizar
    • 1
  • Kensuke Harada
    • 1
    • 2
  1. 1.Osaka UniversityToyonakaJapan
  2. 2.National Inst. of Advanced Industrial Science and TechnologyTsukubaJapan

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