Nonlinear SVM based anomaly detection for manipulator assembly task

Takayuki Matsuno, Jian Huang, Toshio Fukuda

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

Abstract

There is much attraction of automation of difficult assembly by robotic manipulator. However, robots in factory should be overseen by human workers in order to check whether task condition is anomaly or not. In order to reduce human cost, anomaly detection for assembly task is important. A task to tighten a screw as one of assembly tasks is focused on. In this paper, we propose a method to generate high confidence area in the map of features based on nonlinear support vector machine with Gaussian kernel. By proposed method, a robot system can reduce occasions to make mistake in recognition of task conditions.

Original languageEnglish
Title of host publication2012 International Symposium on Micro-NanoMechatronics and Human Science, MHS 2012
Pages364-367
Number of pages4
DOIs
Publication statusPublished - 2012
Event23rd Annual Symposium on Micro-Nano Mechatronics and Human Science, MHS 2012 - Nagoya, Japan
Duration: Nov 4 2012Nov 7 2012

Other

Other23rd Annual Symposium on Micro-Nano Mechatronics and Human Science, MHS 2012
CountryJapan
CityNagoya
Period11/4/1211/7/12

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ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Mechanical Engineering

Cite this

Matsuno, T., Huang, J., & Fukuda, T. (2012). Nonlinear SVM based anomaly detection for manipulator assembly task. In 2012 International Symposium on Micro-NanoMechatronics and Human Science, MHS 2012 (pp. 364-367). [6492439] https://doi.org/10.1109/MHS.2012.6492439