Success/Failure Identification of Skill Movement by Neural Network Using Force Information

Koyo Sato, Masahide Oikawa, Kyo Kutsuzawa, Sho Sakaino, Toshiaki Tsuji

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

Abstract

Currently, in the field of FA(Factory Automation), automation has been accomplished for only tasks with high reproducibility. Meanwhile, there remain a range of areas where tasks cannot reliably be performed due to their complexity or larger environmental variation. In these cases, the reliability of the task can be improved by recognizing the task failure through success/failure identifications and executing the task again when it fails. However, the success/failure identification using conventional machine learning methods has not been discussed for determining the success or failure for unlearned objects. Thus, this paper examined assembly tasks and demonstrated that the success or failure for an unlearned object can be identified by taking advantage of generalized nature of the neural network using force information. The results of making success/failure identifications using information on force, image, and position were compared and the advantage of force information in tasks was demonstrated.

Original languageEnglish
Title of host publicationProceedings
Subtitle of host publicationIECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society
PublisherIEEE Computer Society
Pages3641-3646
Number of pages6
ISBN (Electronic)9781728148786
DOIs
Publication statusPublished - 2019 Oct
Externally publishedYes
Event45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019 - Lisbon, Portugal
Duration: 2019 Oct 142019 Oct 17

Publication series

NameIECON Proceedings (Industrial Electronics Conference)
Volume2019-October

Conference

Conference45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019
CountryPortugal
CityLisbon
Period19/10/1419/10/17

Keywords

  • force information
  • neural network
  • success identification

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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