Assistive robot simulator for multi-objective evolutionary algorithm application

This paper presents a new assistive robot simulator for multi-objective optimization application. The main function of the simulator is to simulate the trajectory of the robot arm when it moves from initial to a goal position in optimized manner. A multi-objective evolutionary algorithm (MOEA) is ut...

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Published in:International Journal of Engineering and Technology(UAE)
Main Author: Mohamed Z.; Ayub M.A.; Ramli M.H.M.; Shaari M.S.B.; Khusairi S.
Format: Article
Language:English
Published: Science Publishing Corporation Inc 2018
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058426024&doi=10.14419%2fijet.v7i4.27.22506&partnerID=40&md5=5c4f1995b4d4d76de7575d7972c07813
id 2-s2.0-85058426024
spelling 2-s2.0-85058426024
Mohamed Z.; Ayub M.A.; Ramli M.H.M.; Shaari M.S.B.; Khusairi S.
Assistive robot simulator for multi-objective evolutionary algorithm application
2018
International Journal of Engineering and Technology(UAE)
7
4
10.14419/ijet.v7i4.27.22506
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058426024&doi=10.14419%2fijet.v7i4.27.22506&partnerID=40&md5=5c4f1995b4d4d76de7575d7972c07813
This paper presents a new assistive robot simulator for multi-objective optimization application. The main function of the simulator is to simulate the trajectory of the robot arm when it moves from initial to a goal position in optimized manner. A multi-objective evolutionary algorithm (MOEA) is utilized to generate the robot arm motion optimizing three different objective function; optimum time, distance, and high stability. The generated neuron will be selected from the Pareto optimal based on the required objectives function. The robot will intelligently choose the best neuron for a specific task. For example, to move a glass of water required higher stability compare to move an empty mineral water bottle. The simulator will be connected to the real robot to test the performance in real environment. The kinematics, mechatronics and the real robot specification are utilized in the simulator. The performance of the simulator is presented in this paper. © 2018 Authors.
Science Publishing Corporation Inc
2227524X
English
Article
All Open Access; Bronze Open Access
author Mohamed Z.; Ayub M.A.; Ramli M.H.M.; Shaari M.S.B.; Khusairi S.
spellingShingle Mohamed Z.; Ayub M.A.; Ramli M.H.M.; Shaari M.S.B.; Khusairi S.
Assistive robot simulator for multi-objective evolutionary algorithm application
author_facet Mohamed Z.; Ayub M.A.; Ramli M.H.M.; Shaari M.S.B.; Khusairi S.
author_sort Mohamed Z.; Ayub M.A.; Ramli M.H.M.; Shaari M.S.B.; Khusairi S.
title Assistive robot simulator for multi-objective evolutionary algorithm application
title_short Assistive robot simulator for multi-objective evolutionary algorithm application
title_full Assistive robot simulator for multi-objective evolutionary algorithm application
title_fullStr Assistive robot simulator for multi-objective evolutionary algorithm application
title_full_unstemmed Assistive robot simulator for multi-objective evolutionary algorithm application
title_sort Assistive robot simulator for multi-objective evolutionary algorithm application
publishDate 2018
container_title International Journal of Engineering and Technology(UAE)
container_volume 7
container_issue 4
doi_str_mv 10.14419/ijet.v7i4.27.22506
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058426024&doi=10.14419%2fijet.v7i4.27.22506&partnerID=40&md5=5c4f1995b4d4d76de7575d7972c07813
description This paper presents a new assistive robot simulator for multi-objective optimization application. The main function of the simulator is to simulate the trajectory of the robot arm when it moves from initial to a goal position in optimized manner. A multi-objective evolutionary algorithm (MOEA) is utilized to generate the robot arm motion optimizing three different objective function; optimum time, distance, and high stability. The generated neuron will be selected from the Pareto optimal based on the required objectives function. The robot will intelligently choose the best neuron for a specific task. For example, to move a glass of water required higher stability compare to move an empty mineral water bottle. The simulator will be connected to the real robot to test the performance in real environment. The kinematics, mechatronics and the real robot specification are utilized in the simulator. The performance of the simulator is presented in this paper. © 2018 Authors.
publisher Science Publishing Corporation Inc
issn 2227524X
language English
format Article
accesstype All Open Access; Bronze Open Access
record_format scopus
collection Scopus
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