Comparison between genetic algorithm and electromagnetism-like algorithm for solving inverse kinematics
A comparison study between Electromagnetism-Like Algorithm (EM) and Genetic Algorithm (GA) has been presented in this work to solve the Inverse Kinematics (IK) of a four-link planar robot manipulator. The comparison is focused on some points for both algorithms like the accuracy of the results and t...
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my.uniten.dspace-58042018-01-03T03:46:34Z Comparison between genetic algorithm and electromagnetism-like algorithm for solving inverse kinematics Abed, I.A. Koh, S.P. Mohamed Sahari, K.S. Tiong, S.K. Yap, D.F.W. A comparison study between Electromagnetism-Like Algorithm (EM) and Genetic Algorithm (GA) has been presented in this work to solve the Inverse Kinematics (IK) of a four-link planar robot manipulator. The comparison is focused on some points for both algorithms like the accuracy of the results and the speed of convergence. Different target points have been taken to check the performance of each algorithm to solve the IK problem. The results showed that EM algorithm needs less population size and number of generations to get the true solution. There are multiple robot configurations at the goal points and both algorithms are able to find these solutions at each point. Self developed software simulator is used to display some of these solutions at each goal position. © IDOSI Publications, 2012. 2017-12-08T07:26:19Z 2017-12-08T07:26:19Z 2012 Article 10.5829/idosi.wasj.2012.20.07.1771 en_US World Applied Sciences Journal Volume 20, Issue 7, 2012, Pages 946-954 |
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A comparison study between Electromagnetism-Like Algorithm (EM) and Genetic Algorithm (GA) has been presented in this work to solve the Inverse Kinematics (IK) of a four-link planar robot manipulator. The comparison is focused on some points for both algorithms like the accuracy of the results and the speed of convergence. Different target points have been taken to check the performance of each algorithm to solve the IK problem. The results showed that EM algorithm needs less population size and number of generations to get the true solution. There are multiple robot configurations at the goal points and both algorithms are able to find these solutions at each point. Self developed software simulator is used to display some of these solutions at each goal position. © IDOSI Publications, 2012. |
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Article |
author |
Abed, I.A. Koh, S.P. Mohamed Sahari, K.S. Tiong, S.K. Yap, D.F.W. |
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Abed, I.A. Koh, S.P. Mohamed Sahari, K.S. Tiong, S.K. Yap, D.F.W. Comparison between genetic algorithm and electromagnetism-like algorithm for solving inverse kinematics |
author_facet |
Abed, I.A. Koh, S.P. Mohamed Sahari, K.S. Tiong, S.K. Yap, D.F.W. |
author_sort |
Abed, I.A. |
title |
Comparison between genetic algorithm and electromagnetism-like algorithm for solving inverse kinematics |
title_short |
Comparison between genetic algorithm and electromagnetism-like algorithm for solving inverse kinematics |
title_full |
Comparison between genetic algorithm and electromagnetism-like algorithm for solving inverse kinematics |
title_fullStr |
Comparison between genetic algorithm and electromagnetism-like algorithm for solving inverse kinematics |
title_full_unstemmed |
Comparison between genetic algorithm and electromagnetism-like algorithm for solving inverse kinematics |
title_sort |
comparison between genetic algorithm and electromagnetism-like algorithm for solving inverse kinematics |
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2017 |
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1644493779762151424 |
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13.19449 |