Neural network approach to stiffness based touch sense storage and reproduction

Baris Yalcin, Kouhei Ohnishi

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

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2006 IEEE International Conference on Industrial Technology, ICIT
Pages2884-2889
Number of pages6
DOIs
Publication statusPublished - 2006 Dec 1
Event2006 IEEE International Conference on Industrial Technology, ICIT - Mumbai, India
Duration: 2006 Dec 152006 Dec 17

Publication series

NameProceedings of the IEEE International Conference on Industrial Technology

Other

Other2006 IEEE International Conference on Industrial Technology, ICIT
CountryIndia
CityMumbai
Period06/12/1506/12/17

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering

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  • Cite this

    Yalcin, B., & Ohnishi, K. (2006). Neural network approach to stiffness based touch sense storage and reproduction. In 2006 IEEE International Conference on Industrial Technology, ICIT (pp. 2884-2889). [4237986] (Proceedings of the IEEE International Conference on Industrial Technology). https://doi.org/10.1109/ICIT.2006.372664