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Task Time Optimization of a Robot Manipulator using Artificial Neural Network and Genetic Algorithm

by Akash Dutt Dubey, R. B. Mishra, A. K. Jha
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 51 - Number 13
Year of Publication: 2012
Authors: Akash Dutt Dubey, R. B. Mishra, A. K. Jha
10.5120/8103-1699

Akash Dutt Dubey, R. B. Mishra, A. K. Jha . Task Time Optimization of a Robot Manipulator using Artificial Neural Network and Genetic Algorithm. International Journal of Computer Applications. 51, 13 ( August 2012), 26-33. DOI=10.5120/8103-1699

@article{ 10.5120/8103-1699,
author = { Akash Dutt Dubey, R. B. Mishra, A. K. Jha },
title = { Task Time Optimization of a Robot Manipulator using Artificial Neural Network and Genetic Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { August 2012 },
volume = { 51 },
number = { 13 },
month = { August },
year = { 2012 },
issn = { 0975-8887 },
pages = { 26-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume51/number13/8103-1699/ },
doi = { 10.5120/8103-1699 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:50:18.887393+05:30
%A Akash Dutt Dubey
%A R. B. Mishra
%A A. K. Jha
%T Task Time Optimization of a Robot Manipulator using Artificial Neural Network and Genetic Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 51
%N 13
%P 26-33
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper we have proposed an evolutionary method to optimize the task time of robot manipulators. Tasks can be planned in joint space with respect to robot joints or in Cartesian space with respect to robot end effector under kinodynamic constraints. Genetic algorithm is implemented to optimize the parameters associated with the selected motion trajectory profile. These optimized results were then taken as the training data to train an artificial neural network which is used to obtain task time, velocity, accelerations and torques required by each motor to perform a given task. The method adopted in this study can be applied to any serial redundant or non-redundant manipulator that has rigid links and known kinematic and dynamic models with free motions or motions along specified paths with obstacle avoidance. The robot kinematic and dynamic models and the optimization method are developed in MATLAB.

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Index Terms

Computer Science
Information Sciences

Keywords

Pick and Place Artificial Neural Network Genetic Algorithm Robot Manipulator End effector Mobile robot

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