A neuro-genetic-simulated annealing approach to the inverse kinematics solution of robots: a simulation based study

In this study, a hybrid intelligent solution system including neural networks, genetic algorithms and simulated annealing has been proposed for the inverse kinematics solution of robotic manipulators. The main purpose of the proposed system is to decrease the end effector error of a neural network b...

Full description

Saved in:
Bibliographic Details
Published in:Engineering with computers 2016-10, Vol.32 (4), p.553-565
Main Authors: Köker, Raşit, Çakar, Tarık
Format: Article
Language:English
Subjects:
CAE) and Design
Calculus of Variations and Optimal Control
Optimization
Classical Mechanics
Computer Science
Computer simulation
Computer-Aided Engineering (CAD
Control
Genetic algorithms
Hybrid systems
Inverse kinematics
Kinematics
Manipulators
Math. Applications in Chemistry
Mathematical and Computational Engineering
Neural networks
Original Article
Robot arms
Simulated annealing
Systems Theory
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this study, a hybrid intelligent solution system including neural networks, genetic algorithms and simulated annealing has been proposed for the inverse kinematics solution of robotic manipulators. The main purpose of the proposed system is to decrease the end effector error of a neural network based inverse kinematics solution. In the designed hybrid intelligent system, simulated annealing algorithm has been used as a genetic operator to decrease the process time of the genetic algorithm to find the optimum solution. Obtained best solution from the neural network has been included in the initial solution of genetic algorithm with randomly produced solutions. The end effector error has been reduced micrometer levels after the implementation of the hybrid intelligent solution system.
ISSN:0177-0667
1435-5663
DOI:10.1007/s00366-015-0432-z