Neural network approach to firm grip in the presence of small slips
type: Item
instance of: scholarly article
title: Neural network approach to firm grip in the presence of small slips
zbMATH Open document ID: 1031.70006
author: A. M. Al-Fahed Nuseirat
author: R. Abu-Zitar
published in: Journal of Robotic Systems
publication date: 2004-03-04T00:00:00Z
Mathematics Subject Classification ID: 70E60
Mathematics Subject Classification ID: 92B20
zbMATH DE Number: 1625341
zbMATH Keywords: slip contact
zbMATH Keywords: genetic algorithms
zbMATH Keywords: firm grip
zbMATH Keywords: static mapping
zbMATH Keywords: neural network
zbMATH Keywords: global optimization
zbMATH Keywords: controller
zbMATH Keywords: robot gripper
MaRDI profile type: Publication
full work available at URL: rob.1025
OpenAlex ID: W2108136788
cites work: Q4846263
cites work: New models for a class of adhesive grippers. The hemivariational inequality approach
cites work: A neural network approach to the frictionless grasping problem
cites work: Q4845459
cites work: Q4039868
cites work: Q4226179
cites work: The ''brain-state-in-a-box'' neural model is a gradient descent algorithm
cites work: Q3992731
DOI: 10.1002/ROB.1025
review text: The authors deal with a two-stage method to examine a firm grip that can tolerate small slips of finger tips of frictionless contact type. The interaction in the gripper-object system is used as a static mapping in training of a neural network. Genetic algorithms have been used in training another neural network by applying global optimization techniques. Hence, the resulting neural network becomes a robust, reliable and stable controller for rigid bodies handled by a robot gripper.
reviewed by: Petre P. Teodorescu
description: scientific article; zbMATH DE number 1625341