Environment mapping for mobile robots navigation using hierarchical neural network and omnivision
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Outros Autores: | , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/IJCNN.2008.4634265 http://hdl.handle.net/11449/70640 |
Resumo: | Autonomous robots must be able to learn and maintain models of their environments. In this context, the present work considers techniques for the classification and extraction of features from images in joined with artificial neural networks in order to use them in the system of mapping and localization of the mobile robot of Laboratory of Automation and Evolutive Computer (LACE). To do this, the robot uses a sensorial system composed for ultrasound sensors and a catadioptric vision system formed by a camera and a conical mirror. The mapping system is composed by three modules. Two of them will be presented in this paper: the classifier and the characterizer module. The first module uses a hierarchical neural network to do the classification; the second uses techiniques of extraction of attributes of images and recognition of invariant patterns extracted from the places images set. The neural network of the classifier module is structured in two layers, reason and intuition, and is trained to classify each place explored for the robot amongst four predefine classes. The final result of the exploration is the construction of a topological map of the explored environment. Results gotten through the simulation of the both modules of the mapping system will be presented in this paper. © 2008 IEEE. |
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Environment mapping for mobile robots navigation using hierarchical neural network and omnivisionClassifiersComputer networksConformal mappingExtractive metallurgyFeature extractionImage classificationImage enhancementLearning systemsMobile robotsRoboticsRobotsVegetationVisual communicationWireless networksArtificial neural networksAutonomous robotsCatadioptric visionsConical mirrorsEnvironment mappingsHierarchical neural networksInvariant patternsMapping systemsOmnivisionSensorial systemsTopological mapsTwo layersUltrasound sensorsNeural networksAutonomous robots must be able to learn and maintain models of their environments. In this context, the present work considers techniques for the classification and extraction of features from images in joined with artificial neural networks in order to use them in the system of mapping and localization of the mobile robot of Laboratory of Automation and Evolutive Computer (LACE). To do this, the robot uses a sensorial system composed for ultrasound sensors and a catadioptric vision system formed by a camera and a conical mirror. The mapping system is composed by three modules. Two of them will be presented in this paper: the classifier and the characterizer module. The first module uses a hierarchical neural network to do the classification; the second uses techiniques of extraction of attributes of images and recognition of invariant patterns extracted from the places images set. The neural network of the classifier module is structured in two layers, reason and intuition, and is trained to classify each place explored for the robot amongst four predefine classes. The final result of the exploration is the construction of a topological map of the explored environment. Results gotten through the simulation of the both modules of the mapping system will be presented in this paper. © 2008 IEEE.Universidade Estadual Paulista Instituto de Biociências, Letras e Ciências Exatas - IBILCE, Av. Cristovão Colombo, 2265, CEP: 15054-000 São José do Rio Preto - SPEscola de Engenharia de São Carlos Universidade de São Paulo - USP, Av. Trabalhador São-Carlense, 400, CEP: 13560-970 São Carlos - SPUniversidade Estadual Paulista Instituto de Biociências, Letras e Ciências Exatas - IBILCE, Av. Cristovão Colombo, 2265, CEP: 15054-000 São José do Rio Preto - SPUniversidade Estadual Paulista (Unesp)Universidade de São Paulo (USP)Silva, Luciana L. [UNESP]Tronco, Mário L. [UNESP]Vian, Henrique A. [UNESP]Pellinson, Giovana [UNESP]Porto, Arthur J. V.2014-05-27T11:23:42Z2014-05-27T11:23:42Z2008-11-24info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject3292-3297http://dx.doi.org/10.1109/IJCNN.2008.4634265Proceedings of the International Joint Conference on Neural Networks, p. 3292-3297.1098-7576http://hdl.handle.net/11449/7064010.1109/IJCNN.2008.4634265WOS:0002638272020252-s2.0-56349147445Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings of the International Joint Conference on Neural Networksinfo:eu-repo/semantics/openAccess2021-10-23T21:37:57Zoai:repositorio.unesp.br:11449/70640Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T21:37:57Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Environment mapping for mobile robots navigation using hierarchical neural network and omnivision |
title |
Environment mapping for mobile robots navigation using hierarchical neural network and omnivision |
spellingShingle |
Environment mapping for mobile robots navigation using hierarchical neural network and omnivision Silva, Luciana L. [UNESP] Classifiers Computer networks Conformal mapping Extractive metallurgy Feature extraction Image classification Image enhancement Learning systems Mobile robots Robotics Robots Vegetation Visual communication Wireless networks Artificial neural networks Autonomous robots Catadioptric visions Conical mirrors Environment mappings Hierarchical neural networks Invariant patterns Mapping systems Omnivision Sensorial systems Topological maps Two layers Ultrasound sensors Neural networks |
title_short |
Environment mapping for mobile robots navigation using hierarchical neural network and omnivision |
title_full |
Environment mapping for mobile robots navigation using hierarchical neural network and omnivision |
title_fullStr |
Environment mapping for mobile robots navigation using hierarchical neural network and omnivision |
title_full_unstemmed |
Environment mapping for mobile robots navigation using hierarchical neural network and omnivision |
title_sort |
Environment mapping for mobile robots navigation using hierarchical neural network and omnivision |
author |
Silva, Luciana L. [UNESP] |
author_facet |
Silva, Luciana L. [UNESP] Tronco, Mário L. [UNESP] Vian, Henrique A. [UNESP] Pellinson, Giovana [UNESP] Porto, Arthur J. V. |
author_role |
author |
author2 |
Tronco, Mário L. [UNESP] Vian, Henrique A. [UNESP] Pellinson, Giovana [UNESP] Porto, Arthur J. V. |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Universidade de São Paulo (USP) |
dc.contributor.author.fl_str_mv |
Silva, Luciana L. [UNESP] Tronco, Mário L. [UNESP] Vian, Henrique A. [UNESP] Pellinson, Giovana [UNESP] Porto, Arthur J. V. |
dc.subject.por.fl_str_mv |
Classifiers Computer networks Conformal mapping Extractive metallurgy Feature extraction Image classification Image enhancement Learning systems Mobile robots Robotics Robots Vegetation Visual communication Wireless networks Artificial neural networks Autonomous robots Catadioptric visions Conical mirrors Environment mappings Hierarchical neural networks Invariant patterns Mapping systems Omnivision Sensorial systems Topological maps Two layers Ultrasound sensors Neural networks |
topic |
Classifiers Computer networks Conformal mapping Extractive metallurgy Feature extraction Image classification Image enhancement Learning systems Mobile robots Robotics Robots Vegetation Visual communication Wireless networks Artificial neural networks Autonomous robots Catadioptric visions Conical mirrors Environment mappings Hierarchical neural networks Invariant patterns Mapping systems Omnivision Sensorial systems Topological maps Two layers Ultrasound sensors Neural networks |
description |
Autonomous robots must be able to learn and maintain models of their environments. In this context, the present work considers techniques for the classification and extraction of features from images in joined with artificial neural networks in order to use them in the system of mapping and localization of the mobile robot of Laboratory of Automation and Evolutive Computer (LACE). To do this, the robot uses a sensorial system composed for ultrasound sensors and a catadioptric vision system formed by a camera and a conical mirror. The mapping system is composed by three modules. Two of them will be presented in this paper: the classifier and the characterizer module. The first module uses a hierarchical neural network to do the classification; the second uses techiniques of extraction of attributes of images and recognition of invariant patterns extracted from the places images set. The neural network of the classifier module is structured in two layers, reason and intuition, and is trained to classify each place explored for the robot amongst four predefine classes. The final result of the exploration is the construction of a topological map of the explored environment. Results gotten through the simulation of the both modules of the mapping system will be presented in this paper. © 2008 IEEE. |
publishDate |
2008 |
dc.date.none.fl_str_mv |
2008-11-24 2014-05-27T11:23:42Z 2014-05-27T11:23:42Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/IJCNN.2008.4634265 Proceedings of the International Joint Conference on Neural Networks, p. 3292-3297. 1098-7576 http://hdl.handle.net/11449/70640 10.1109/IJCNN.2008.4634265 WOS:000263827202025 2-s2.0-56349147445 |
url |
http://dx.doi.org/10.1109/IJCNN.2008.4634265 http://hdl.handle.net/11449/70640 |
identifier_str_mv |
Proceedings of the International Joint Conference on Neural Networks, p. 3292-3297. 1098-7576 10.1109/IJCNN.2008.4634265 WOS:000263827202025 2-s2.0-56349147445 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Proceedings of the International Joint Conference on Neural Networks |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
3292-3297 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
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1803047331077029888 |