By Topic

Neural Network Approach to Stiffness Based Touch Sense Storage and Reproduction

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Yalcin, B. ; Keio Univ., Yokohama ; Ohnishi, K.

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.

Published in:

Industrial Technology, 2006. ICIT 2006. IEEE International Conference on

Date of Conference:

15-17 Dec. 2006