Fault detection algorithm for external thread fastening by robotic manipulator using linear support vector machine classifier

Takayuki Matsuno, Jian Huang, Toshio Fukuda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Fault detection functions with learning method of a robotic manipulator are very useful for factory automation. All production has the possibility to fail due to unexpected accidents. To reduce the fatigue of human workers, small errors automatically should be corrected by a robot system. Also a learning method is important for fault detection, because labor of system integrator should be reduced. In this paper, an external thread fastening task by a robotic manipulator is investigated. To discriminate the four states of a task, linear support vector machine methods with two feature parameters are introduced. The effectiveness of the proposed algorithm is confirmed through an experiment and recognition examination. Finally, the ability of linear SVM is compared with artificial neural network method.

Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Robotics and Automation
Pages3443-3450
Number of pages8
DOIs
Publication statusPublished - 2013
Event2013 IEEE International Conference on Robotics and Automation, ICRA 2013 - Karlsruhe, Germany
Duration: May 6 2013May 10 2013

Other

Other2013 IEEE International Conference on Robotics and Automation, ICRA 2013
CountryGermany
CityKarlsruhe
Period5/6/135/10/13

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ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Matsuno, T., Huang, J., & Fukuda, T. (2013). Fault detection algorithm for external thread fastening by robotic manipulator using linear support vector machine classifier. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 3443-3450). [6631058] https://doi.org/10.1109/ICRA.2013.6631058