By Topic

A neural estimator of object stiffness applied to force control of a robotic finger with opponent artificial muscles

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

4 Author(s)
Pedreno-Molina, J.L. ; Dept. of Inf. Technol. & Commun., Politechnical Univ. of Cartagena, Spain ; Guerrero-Gonzalez, A. ; Garcia-Cordova, F. ; López-Coronado, J.

We present a solution for real-time neural estimation of the stiffness characteristics of objects which are pressed with a predefined force threshold by an anthropomorphic robotic finger provided with opponent movement of their artificial muscles. The proposed architecture links three neural models in order to satisfy the requirements in our control system. This model based on adaptive learning allows the controller to grasp any object with different stiffness characteristics in a smooth way and with the desired final force

Published in:

Systems, Man, and Cybernetics, 2001 IEEE International Conference on  (Volume:5 )

Date of Conference:

2001