Behavioral control through evolutionary neurocontrollers for autonomous mobile robot navigation

Autores
Fernandez Leon, Jose Alberto; Acosta, Gerardo Gabriel; Mayosky, Miguel Angel
Año de publicación
2009
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper deals with the study of scaling up behaviors in evolutive robotics (ER). Complex behaviors were obtained from simple ones. Each behavior is supported by an artificial neural network (ANN)-based controller or neurocontroller. Hence, a method for the generation of a hierarchy of neurocontrollers, resorting to the paradigm of Layered Evolution (LE), is developed and verified experimentally through computer simulations and tests in a Khepera® micro-robot. Several behavioral modules are initially evolved using specialized neurocontrollers based on different ANN paradigms. The results show that simple behaviors coordination through LE is a feasible strategy that gives rise to emergent complex behaviors. These complex behaviors can then solve real-world problems efficiently. From a pure evolutionary perspective, however, the methodology presented is too much dependent on user’s prior knowledge about the problem to solve and also that evolution take place in a rigid, prescribed framework. Mobile robot’s navigation in an unknown environment is used as a test bed for the proposed scaling strategies.
Fil: Fernandez Leon, Jose Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina. University of Sussex; Reino Unido
Fil: Acosta, Gerardo Gabriel. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ingeniería Olavarría. Departamento de Electromecánica. Grupo INTELYMEC; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina
Fil: Mayosky, Miguel Angel. Universidad Nacional de La Plata. Facultad de Ingeniería. Departamento de Electrotecnia. Laboratorio de Electrónica Industrial, Control e Instrumentación; Argentina
Materia
autonomous navigation
robotics
neural networks
bio-inspiration
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/240005
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Behavioral control through evolutionary neurocontrollers for autonomous mobile robot navigationFernandez Leon, Jose AlbertoAcosta, Gerardo GabrielMayosky, Miguel Angelautonomous navigationroboticsneural networksbio-inspirationhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1This paper deals with the study of scaling up behaviors in evolutive robotics (ER). Complex behaviors were obtained from simple ones. Each behavior is supported by an artificial neural network (ANN)-based controller or neurocontroller. Hence, a method for the generation of a hierarchy of neurocontrollers, resorting to the paradigm of Layered Evolution (LE), is developed and verified experimentally through computer simulations and tests in a Khepera® micro-robot. Several behavioral modules are initially evolved using specialized neurocontrollers based on different ANN paradigms. The results show that simple behaviors coordination through LE is a feasible strategy that gives rise to emergent complex behaviors. These complex behaviors can then solve real-world problems efficiently. From a pure evolutionary perspective, however, the methodology presented is too much dependent on user’s prior knowledge about the problem to solve and also that evolution take place in a rigid, prescribed framework. Mobile robot’s navigation in an unknown environment is used as a test bed for the proposed scaling strategies.Fil: Fernandez Leon, Jose Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina. University of Sussex; Reino UnidoFil: Acosta, Gerardo Gabriel. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ingeniería Olavarría. Departamento de Electromecánica. Grupo INTELYMEC; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; ArgentinaFil: Mayosky, Miguel Angel. Universidad Nacional de La Plata. Facultad de Ingeniería. Departamento de Electrotecnia. Laboratorio de Electrónica Industrial, Control e Instrumentación; ArgentinaElsevier Science2009-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/240005Fernandez Leon, Jose Alberto; Acosta, Gerardo Gabriel; Mayosky, Miguel Angel; Behavioral control through evolutionary neurocontrollers for autonomous mobile robot navigation; Elsevier Science; Robotics And Autonomous Systems; 57; 4; 4-2009; 411-4190921-8890CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.robot.2008.06.012info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:47:22Zoai:ri.conicet.gov.ar:11336/240005instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 14:47:23.038CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Behavioral control through evolutionary neurocontrollers for autonomous mobile robot navigation
title Behavioral control through evolutionary neurocontrollers for autonomous mobile robot navigation
spellingShingle Behavioral control through evolutionary neurocontrollers for autonomous mobile robot navigation
Fernandez Leon, Jose Alberto
autonomous navigation
robotics
neural networks
bio-inspiration
title_short Behavioral control through evolutionary neurocontrollers for autonomous mobile robot navigation
title_full Behavioral control through evolutionary neurocontrollers for autonomous mobile robot navigation
title_fullStr Behavioral control through evolutionary neurocontrollers for autonomous mobile robot navigation
title_full_unstemmed Behavioral control through evolutionary neurocontrollers for autonomous mobile robot navigation
title_sort Behavioral control through evolutionary neurocontrollers for autonomous mobile robot navigation
dc.creator.none.fl_str_mv Fernandez Leon, Jose Alberto
Acosta, Gerardo Gabriel
Mayosky, Miguel Angel
author Fernandez Leon, Jose Alberto
author_facet Fernandez Leon, Jose Alberto
Acosta, Gerardo Gabriel
Mayosky, Miguel Angel
author_role author
author2 Acosta, Gerardo Gabriel
Mayosky, Miguel Angel
author2_role author
author
dc.subject.none.fl_str_mv autonomous navigation
robotics
neural networks
bio-inspiration
topic autonomous navigation
robotics
neural networks
bio-inspiration
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv This paper deals with the study of scaling up behaviors in evolutive robotics (ER). Complex behaviors were obtained from simple ones. Each behavior is supported by an artificial neural network (ANN)-based controller or neurocontroller. Hence, a method for the generation of a hierarchy of neurocontrollers, resorting to the paradigm of Layered Evolution (LE), is developed and verified experimentally through computer simulations and tests in a Khepera® micro-robot. Several behavioral modules are initially evolved using specialized neurocontrollers based on different ANN paradigms. The results show that simple behaviors coordination through LE is a feasible strategy that gives rise to emergent complex behaviors. These complex behaviors can then solve real-world problems efficiently. From a pure evolutionary perspective, however, the methodology presented is too much dependent on user’s prior knowledge about the problem to solve and also that evolution take place in a rigid, prescribed framework. Mobile robot’s navigation in an unknown environment is used as a test bed for the proposed scaling strategies.
Fil: Fernandez Leon, Jose Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina. University of Sussex; Reino Unido
Fil: Acosta, Gerardo Gabriel. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ingeniería Olavarría. Departamento de Electromecánica. Grupo INTELYMEC; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina
Fil: Mayosky, Miguel Angel. Universidad Nacional de La Plata. Facultad de Ingeniería. Departamento de Electrotecnia. Laboratorio de Electrónica Industrial, Control e Instrumentación; Argentina
description This paper deals with the study of scaling up behaviors in evolutive robotics (ER). Complex behaviors were obtained from simple ones. Each behavior is supported by an artificial neural network (ANN)-based controller or neurocontroller. Hence, a method for the generation of a hierarchy of neurocontrollers, resorting to the paradigm of Layered Evolution (LE), is developed and verified experimentally through computer simulations and tests in a Khepera® micro-robot. Several behavioral modules are initially evolved using specialized neurocontrollers based on different ANN paradigms. The results show that simple behaviors coordination through LE is a feasible strategy that gives rise to emergent complex behaviors. These complex behaviors can then solve real-world problems efficiently. From a pure evolutionary perspective, however, the methodology presented is too much dependent on user’s prior knowledge about the problem to solve and also that evolution take place in a rigid, prescribed framework. Mobile robot’s navigation in an unknown environment is used as a test bed for the proposed scaling strategies.
publishDate 2009
dc.date.none.fl_str_mv 2009-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/240005
Fernandez Leon, Jose Alberto; Acosta, Gerardo Gabriel; Mayosky, Miguel Angel; Behavioral control through evolutionary neurocontrollers for autonomous mobile robot navigation; Elsevier Science; Robotics And Autonomous Systems; 57; 4; 4-2009; 411-419
0921-8890
CONICET Digital
CONICET
url http://hdl.handle.net/11336/240005
identifier_str_mv Fernandez Leon, Jose Alberto; Acosta, Gerardo Gabriel; Mayosky, Miguel Angel; Behavioral control through evolutionary neurocontrollers for autonomous mobile robot navigation; Elsevier Science; Robotics And Autonomous Systems; 57; 4; 4-2009; 411-419
0921-8890
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.robot.2008.06.012
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846082989684948992
score 13.22299

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