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Solving the inverse kinematics for robot manipulators using modified electromagnetism-like algorithm with record to record travel

A new modification of Electromagnetism-like (EM) algorithm which incorporating the Record-to-Record Travel (RRT) local search algorithm; namely MEMR has been developed to solve the problem of Inverse Kinematics (IK) for a four Degree-of-Freedom (DOF) manipulator. The proposed method is able to gener...

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Main Authors: Abed, I.A., Koh, S.P., Sahari, K.S.M., Tiong, S.K., Tan, N.M.L.
Format: Article
Language:en_US
Published: 2017
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spelling my.uniten.dspace-57892020-09-10T07:40:29Z Solving the inverse kinematics for robot manipulators using modified electromagnetism-like algorithm with record to record travel Abed, I.A. Koh, S.P. Sahari, K.S.M. Tiong, S.K. Tan, N.M.L. A new modification of Electromagnetism-like (EM) algorithm which incorporating the Record-to-Record Travel (RRT) local search algorithm; namely MEMR has been developed to solve the problem of Inverse Kinematics (IK) for a four Degree-of-Freedom (DOF) manipulator. The proposed method is able to generate multiple robot configurations for the IK test performed at different end effect or positions. In addition, the comparison between the proposed MEMR and Genetic Algorithm (GA) was carried out using two mathematical test functions; De Jong and Rastrigin. The tests results show that the proposed MEMR is comparable in performance to GA in terms of both convergence speed and error rate. © Maxwell Scientific Organization, 2014. 2017-12-08T07:26:12Z 2017-12-08T07:26:12Z 2014 Article https://www.scopus.com/record/display.uri?eid=2-s2.0-84901981275&origin=resultslist&sort=plf-f&src=s&sid=18113d66b4cbaf31826cebad70df5e74&sot en_US Research Journal of Applied Sciences, Engineering and Technology Volume 7, Issue 19, 2014, Pages 3986-3994
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language en_US
description A new modification of Electromagnetism-like (EM) algorithm which incorporating the Record-to-Record Travel (RRT) local search algorithm; namely MEMR has been developed to solve the problem of Inverse Kinematics (IK) for a four Degree-of-Freedom (DOF) manipulator. The proposed method is able to generate multiple robot configurations for the IK test performed at different end effect or positions. In addition, the comparison between the proposed MEMR and Genetic Algorithm (GA) was carried out using two mathematical test functions; De Jong and Rastrigin. The tests results show that the proposed MEMR is comparable in performance to GA in terms of both convergence speed and error rate. © Maxwell Scientific Organization, 2014.
format Article
author Abed, I.A.
Koh, S.P.
Sahari, K.S.M.
Tiong, S.K.
Tan, N.M.L.
spellingShingle Abed, I.A.
Koh, S.P.
Sahari, K.S.M.
Tiong, S.K.
Tan, N.M.L.
Solving the inverse kinematics for robot manipulators using modified electromagnetism-like algorithm with record to record travel
author_facet Abed, I.A.
Koh, S.P.
Sahari, K.S.M.
Tiong, S.K.
Tan, N.M.L.
author_sort Abed, I.A.
title Solving the inverse kinematics for robot manipulators using modified electromagnetism-like algorithm with record to record travel
title_short Solving the inverse kinematics for robot manipulators using modified electromagnetism-like algorithm with record to record travel
title_full Solving the inverse kinematics for robot manipulators using modified electromagnetism-like algorithm with record to record travel
title_fullStr Solving the inverse kinematics for robot manipulators using modified electromagnetism-like algorithm with record to record travel
title_full_unstemmed Solving the inverse kinematics for robot manipulators using modified electromagnetism-like algorithm with record to record travel
title_sort solving the inverse kinematics for robot manipulators using modified electromagnetism-like algorithm with record to record travel
publishDate 2017
_version_ 1677776754906759168
score 13.214268

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