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Conference Paper/Proceeding/Abstract 463 views

Evolutionary Design of Controllers for Autonomous Line Tracking Mobile Robots Using Population Based Incremental Learning

Shahab Kalantar, Parisa Eslambolchilar Orcid Logo, Majid Nili

9th Int. Conf. Of Neural Information processing

Swansea University Author: Parisa Eslambolchilar Orcid Logo

Abstract

This paper concerns the design of sensor topology and reaction controllers for autonomous mobile robots following a line. Artificial evolution (1,2) is used as the design methodology. Here, it is shown PBIL (3) is powerful enough to evolve autonomous creatures exhibiting complex behavior. Designing...

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Published in: 9th Int. Conf. Of Neural Information processing
Published: 2002
Online Access: http://www.cs.swan.ac.uk/~csparisa/publications/KaEsNi_PBIL.pdf
URI: https://cronfa.swan.ac.uk/Record/cronfa20554
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first_indexed 2015-03-27T03:05:44Z
last_indexed 2018-02-09T04:57:12Z
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spelling 2015-03-26T09:21:24.1462363 v2 20554 2015-03-26 Evolutionary Design of Controllers for Autonomous Line Tracking Mobile Robots Using Population Based Incremental Learning 82ddb5ec487e50883f14e2ea583ef6db 0000-0003-4610-1643 Parisa Eslambolchilar Parisa Eslambolchilar true false 2015-03-26 SCS This paper concerns the design of sensor topology and reaction controllers for autonomous mobile robots following a line. Artificial evolution (1,2) is used as the design methodology. Here, it is shown PBIL (3) is powerful enough to evolve autonomous creatures exhibiting complex behavior. Designing optimal sensor topology, symmetric and asymmetric control mechanisms, reactive controllers with feedback, and suitable criteria for evaluating evolved robots are among the topics discussed in this paper. Intuitive insight into the nature of the problem proved to be a crucial determinant of the success of evolution. Defining some measure of stability turned out to be very useful. The idea can certainly be extended to other benchmark problems. PBIL is demonstrated to be much more effective than GA in exploiting the salient features in both fitness criteria and controller architectures. All the simulations were carried out by the EVO-ROB system, an open architecture, component based software framework especially designed for ER experiments. Conference Paper/Proceeding/Abstract 9th Int. Conf. Of Neural Information processing 31 12 2002 2002-12-31 http://www.cs.swan.ac.uk/~csparisa/publications/KaEsNi_PBIL.pdf COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2015-03-26T09:21:24.1462363 2015-03-26T09:20:33.3861141 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Shahab Kalantar 1 Parisa Eslambolchilar 0000-0003-4610-1643 2 Majid Nili 3
title Evolutionary Design of Controllers for Autonomous Line Tracking Mobile Robots Using Population Based Incremental Learning
spellingShingle Evolutionary Design of Controllers for Autonomous Line Tracking Mobile Robots Using Population Based Incremental Learning
Parisa Eslambolchilar
title_short Evolutionary Design of Controllers for Autonomous Line Tracking Mobile Robots Using Population Based Incremental Learning
title_full Evolutionary Design of Controllers for Autonomous Line Tracking Mobile Robots Using Population Based Incremental Learning
title_fullStr Evolutionary Design of Controllers for Autonomous Line Tracking Mobile Robots Using Population Based Incremental Learning
title_full_unstemmed Evolutionary Design of Controllers for Autonomous Line Tracking Mobile Robots Using Population Based Incremental Learning
title_sort Evolutionary Design of Controllers for Autonomous Line Tracking Mobile Robots Using Population Based Incremental Learning
author_id_str_mv 82ddb5ec487e50883f14e2ea583ef6db
author_id_fullname_str_mv 82ddb5ec487e50883f14e2ea583ef6db_***_Parisa Eslambolchilar
author Parisa Eslambolchilar
author2 Shahab Kalantar
Parisa Eslambolchilar
Majid Nili
format Conference Paper/Proceeding/Abstract
container_title 9th Int. Conf. Of Neural Information processing
publishDate 2002
institution Swansea University
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url http://www.cs.swan.ac.uk/~csparisa/publications/KaEsNi_PBIL.pdf
document_store_str 0
active_str 0
description This paper concerns the design of sensor topology and reaction controllers for autonomous mobile robots following a line. Artificial evolution (1,2) is used as the design methodology. Here, it is shown PBIL (3) is powerful enough to evolve autonomous creatures exhibiting complex behavior. Designing optimal sensor topology, symmetric and asymmetric control mechanisms, reactive controllers with feedback, and suitable criteria for evaluating evolved robots are among the topics discussed in this paper. Intuitive insight into the nature of the problem proved to be a crucial determinant of the success of evolution. Defining some measure of stability turned out to be very useful. The idea can certainly be extended to other benchmark problems. PBIL is demonstrated to be much more effective than GA in exploiting the salient features in both fitness criteria and controller architectures. All the simulations were carried out by the EVO-ROB system, an open architecture, component based software framework especially designed for ER experiments.
published_date 2002-12-31T03:24:20Z
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score 11.0128355

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