Neural network approach to stiffness based touch sense storage and reproduction

Baris Yalcin, Kouhei Ohnishi

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

In this paper, a sliding mode neural network is utilized to learn environmental conditions during haptic touch of bilaterally controlled robot to an unknown environment. Learning of environmental conditions is based on obtaining the highly nonlinear data mapping between force and position dimensions by the neural network. The environment identifier network is then utilized to reproduce the environmental conditions in the absence of the environment. The exact feeling of touch is reproduced by means of environmental conditions. Real time experiments on haptic forceps robot that is controlled by a hybrid force-position controller are carried out to verify the viability of neural network approach to recording and reproduction of haptic touch sense which is based on evaluation of stiffness.

本文言語English
ホスト出版物のタイトル2006 IEEE International Conference on Industrial Technology, ICIT
ページ2884-2889
ページ数6
DOI
出版ステータスPublished - 2006 12月 1
イベント2006 IEEE International Conference on Industrial Technology, ICIT - Mumbai, India
継続期間: 2006 12月 152006 12月 17

出版物シリーズ

名前Proceedings of the IEEE International Conference on Industrial Technology

Other

Other2006 IEEE International Conference on Industrial Technology, ICIT
国/地域India
CityMumbai
Period06/12/1506/12/17

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 電子工学および電気工学

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