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Development of a Bioinspired optimization algorithm for the automatic generation of multiple distinct behaviors in simulated mobile robots

In this study, the utilization of a multi-objective approach in evolving artificial neural networks (ANNs) for an autonomous mobile robot is investigated. The ANN acts as a controller for radio frequency (RF)-Iocalization behavior of a Khepera robot simulated in a 3D physics-based environment. A fi...

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Main Authors: Hanafi Ahmad Hijazi, Patricia Anthony
Format: Research Report
Language:English
Published: Universiti Malaysia Sabah 2006
Subjects:
TJ Mechanical engineering and machinery
Online Access:https://eprints.ums.edu.my/id/eprint/23200/1/Development%20of%20a%20Bioinspired%20optimization%20algorithm%20for%20the%20automatic%20generation%20of%20multiple%20distinct%20behaviors%20in%20simulated%20mobile%20robots.pdf
https://eprints.ums.edu.my/id/eprint/23200/
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spelling my.ums.eprints.232002019-08-01T00:30:35Z https://eprints.ums.edu.my/id/eprint/23200/ Development of a Bioinspired optimization algorithm for the automatic generation of multiple distinct behaviors in simulated mobile robots Hanafi Ahmad Hijazi Patricia Anthony TJ Mechanical engineering and machinery In this study, the utilization of a multi-objective approach in evolving artificial neural networks (ANNs) for an autonomous mobile robot is investigated. The ANN acts as a controller for radio frequency (RF)-Iocalization behavior of a Khepera robot simulated in a 3D physics-based environment. A fitness function has been created from the preliminary experiment with optimized two conflicting objectives: (1) maximize the virtual Khepera robot's behavior for homing towards a RF signal source and (2) minimize the number of hidden neurons used in its ANNs controller by the utilization of Pareto-frontier Differential Evolutionary Multi-objective (POE) algorithm. Bootstrap problem found during the fitness function optimization. Thus, another component has been included into the fitness function in order to overcome the bootstrap problem, so called obstacle avoidance component. Furthermore, the performance of the generated robot controll~r has been improved with integrated three extra components; average wheels speed component, maximize wheels speed component and shortest time component. The testing results showed the controller performed better after the hybridization of the mentioned components. Further, a comparison between elitism and non-elitism used has been conducted attributable to no study has been conducted yet in comparing the application of elitism and non-elitism. As a result, this study has thus shown that the multi-objective approach to evolutionary robotics in the form of the elitist PDE-EMO algorithm can be practically used to automatically generate controllers for RF-Iocalization behavior in autonomous mobile robots. Universiti Malaysia Sabah 2006 Research Report NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/23200/1/Development%20of%20a%20Bioinspired%20optimization%20algorithm%20for%20the%20automatic%20generation%20of%20multiple%20distinct%20behaviors%20in%20simulated%20mobile%20robots.pdf Hanafi Ahmad Hijazi and Patricia Anthony (2006) Development of a Bioinspired optimization algorithm for the automatic generation of multiple distinct behaviors in simulated mobile robots. (Unpublished)
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Hanafi Ahmad Hijazi
Patricia Anthony
Development of a Bioinspired optimization algorithm for the automatic generation of multiple distinct behaviors in simulated mobile robots
description In this study, the utilization of a multi-objective approach in evolving artificial neural networks (ANNs) for an autonomous mobile robot is investigated. The ANN acts as a controller for radio frequency (RF)-Iocalization behavior of a Khepera robot simulated in a 3D physics-based environment. A fitness function has been created from the preliminary experiment with optimized two conflicting objectives: (1) maximize the virtual Khepera robot's behavior for homing towards a RF signal source and (2) minimize the number of hidden neurons used in its ANNs controller by the utilization of Pareto-frontier Differential Evolutionary Multi-objective (POE) algorithm. Bootstrap problem found during the fitness function optimization. Thus, another component has been included into the fitness function in order to overcome the bootstrap problem, so called obstacle avoidance component. Furthermore, the performance of the generated robot controll~r has been improved with integrated three extra components; average wheels speed component, maximize wheels speed component and shortest time component. The testing results showed the controller performed better after the hybridization of the mentioned components. Further, a comparison between elitism and non-elitism used has been conducted attributable to no study has been conducted yet in comparing the application of elitism and non-elitism. As a result, this study has thus shown that the multi-objective approach to evolutionary robotics in the form of the elitist PDE-EMO algorithm can be practically used to automatically generate controllers for RF-Iocalization behavior in autonomous mobile robots.
format Research Report
author Hanafi Ahmad Hijazi
Patricia Anthony
author_facet Hanafi Ahmad Hijazi
Patricia Anthony
author_sort Hanafi Ahmad Hijazi
title Development of a Bioinspired optimization algorithm for the automatic generation of multiple distinct behaviors in simulated mobile robots
title_short Development of a Bioinspired optimization algorithm for the automatic generation of multiple distinct behaviors in simulated mobile robots
title_full Development of a Bioinspired optimization algorithm for the automatic generation of multiple distinct behaviors in simulated mobile robots
title_fullStr Development of a Bioinspired optimization algorithm for the automatic generation of multiple distinct behaviors in simulated mobile robots
title_full_unstemmed Development of a Bioinspired optimization algorithm for the automatic generation of multiple distinct behaviors in simulated mobile robots
title_sort development of a bioinspired optimization algorithm for the automatic generation of multiple distinct behaviors in simulated mobile robots
publisher Universiti Malaysia Sabah
publishDate 2006
url https://eprints.ums.edu.my/id/eprint/23200/1/Development%20of%20a%20Bioinspired%20optimization%20algorithm%20for%20the%20automatic%20generation%20of%20multiple%20distinct%20behaviors%20in%20simulated%20mobile%20robots.pdf
https://eprints.ums.edu.my/id/eprint/23200/
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score 13.211869

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