Neural network approach to stiffness based touch sense storage and reproduction

Baris Yalcin; Kouhei Ohnishi

doi:10.1109/ICIT.2006.372664

Neural network approach to stiffness based touch sense storage and reproduction

Baris Yalcin, Kouhei Ohnishi

Department of System Design Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	2006 IEEE International Conference on Industrial Technology, ICIT
Pages	2884-2889
Number of pages	6
DOIs	https://doi.org/10.1109/ICIT.2006.372664
Publication status	Published - 2006 Dec 1
Event	2006 IEEE International Conference on Industrial Technology, ICIT - Mumbai, India Duration: 2006 Dec 15 → 2006 Dec 17

Publication series

Name	Proceedings of the IEEE International Conference on Industrial Technology

Other

Other	2006 IEEE International Conference on Industrial Technology, ICIT
Country/Territory	India
City	Mumbai
Period	06/12/15 → 06/12/17

ASJC Scopus subject areas

Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/ICIT.2006.372664

Cite this

Neural network approach to stiffness based touch sense storage and reproduction. / Yalcin, Baris; Ohnishi, Kouhei.
2006 IEEE International Conference on Industrial Technology, ICIT. 2006. p. 2884-2889 4237986 (Proceedings of the IEEE International Conference on Industrial Technology).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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., 4237986, Proceedings of the IEEE International Conference on Industrial Technology, pp. 2884-2889, 2006 IEEE International Conference on Industrial Technology, ICIT, Mumbai, India, 06/12/15. https://doi.org/10.1109/ICIT.2006.372664

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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.",

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AB - 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.

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ER -

Neural network approach to stiffness based touch sense storage and reproduction

Abstract

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