Predictive Dynamic Window Approach Development with Artificial Neural Fuzzy Inference Improvement

The aim of this paper is to improve the dynamic window approach algorithm for mobile robots by implementing a prediction window with a fuzzy inference system to adapt to fixed parameters, depending on the surrounding conditions. The first implementation shows the advantage of the prediction step in...

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Detalhes bibliográficos
Autores: Teso Fernández de Betoño, Daniel, Zulueta Guerrero, Ekaitz, Fernández Gámiz, Unai, Sáenz Aguirre, Aitor, Martínez Rodríguez, Raquel
Tipo de documento: artigo
Data de publicação:2019
País:España
Recursos:Universidad del País Vasco
Repositório:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/37530
Acesso em linha:http://hdl.handle.net/10810/37530
Access Level:Acceso aberto
Palavra-chave:DWA
ANFIS
motion planning
mobile robots
obstacle avoidance
fuzzy logic
MPC
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spelling Predictive Dynamic Window Approach Development with Artificial Neural Fuzzy Inference ImprovementTeso Fernández de Betoño, DanielZulueta Guerrero, EkaitzFernández Gámiz, UnaiSáenz Aguirre, AitorMartínez Rodríguez, RaquelDWAANFISmotion planningmobile robotsobstacle avoidancefuzzy logicMPCThe aim of this paper is to improve the dynamic window approach algorithm for mobile robots by implementing a prediction window with a fuzzy inference system to adapt to fixed parameters, depending on the surrounding conditions. The first implementation shows the advantage of the prediction step in terms of optimizing the path selection. The second improvement uses fuzzy inference to optimize each of the fixed parameters' values to increase the algorithm performance. Nevertheless, a simple fuzzy inference system (FIS) was not used for this particular study; instead, an artificial neuro-fuzzy inference system (ANFIS) was used, thus making it possible to develop a FIS system with a back-propagation technique. Each parameter would have a particular ANFIS, in order to modify the alpha(D), beta(D), and gamma(D) parameters individually. At the end of the article, different scenarios are analyzed to determine whether the developments in this article have improved the DWA behavior. The results show that the prediction step and ANFIS adapt DWA performance by optimizing the path resolution.This research was financed by the plant of Mercedes-Benz Vitoria through PIF program to develop an intelligent production.MDPI202020202019info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/37530reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoIngléshttps://www.mdpi.com/2079-9292/8/9/935info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/3.0/es/This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Atribución 3.0 Españaoai:addi.ehu.eus:10810/375302026-06-18T09:23:17Z
dc.title.none.fl_str_mv Predictive Dynamic Window Approach Development with Artificial Neural Fuzzy Inference Improvement
title Predictive Dynamic Window Approach Development with Artificial Neural Fuzzy Inference Improvement
spellingShingle Predictive Dynamic Window Approach Development with Artificial Neural Fuzzy Inference Improvement
Teso Fernández de Betoño, Daniel
DWA
ANFIS
motion planning
mobile robots
obstacle avoidance
fuzzy logic
MPC
title_short Predictive Dynamic Window Approach Development with Artificial Neural Fuzzy Inference Improvement
title_full Predictive Dynamic Window Approach Development with Artificial Neural Fuzzy Inference Improvement
title_fullStr Predictive Dynamic Window Approach Development with Artificial Neural Fuzzy Inference Improvement
title_full_unstemmed Predictive Dynamic Window Approach Development with Artificial Neural Fuzzy Inference Improvement
title_sort Predictive Dynamic Window Approach Development with Artificial Neural Fuzzy Inference Improvement
dc.creator.none.fl_str_mv Teso Fernández de Betoño, Daniel
Zulueta Guerrero, Ekaitz
Fernández Gámiz, Unai
Sáenz Aguirre, Aitor
Martínez Rodríguez, Raquel
author Teso Fernández de Betoño, Daniel
author_facet Teso Fernández de Betoño, Daniel
Zulueta Guerrero, Ekaitz
Fernández Gámiz, Unai
Sáenz Aguirre, Aitor
Martínez Rodríguez, Raquel
author_role author
author2 Zulueta Guerrero, Ekaitz
Fernández Gámiz, Unai
Sáenz Aguirre, Aitor
Martínez Rodríguez, Raquel
author2_role author
author
author
author
dc.subject.none.fl_str_mv DWA
ANFIS
motion planning
mobile robots
obstacle avoidance
fuzzy logic
MPC
topic DWA
ANFIS
motion planning
mobile robots
obstacle avoidance
fuzzy logic
MPC
description The aim of this paper is to improve the dynamic window approach algorithm for mobile robots by implementing a prediction window with a fuzzy inference system to adapt to fixed parameters, depending on the surrounding conditions. The first implementation shows the advantage of the prediction step in terms of optimizing the path selection. The second improvement uses fuzzy inference to optimize each of the fixed parameters' values to increase the algorithm performance. Nevertheless, a simple fuzzy inference system (FIS) was not used for this particular study; instead, an artificial neuro-fuzzy inference system (ANFIS) was used, thus making it possible to develop a FIS system with a back-propagation technique. Each parameter would have a particular ANFIS, in order to modify the alpha(D), beta(D), and gamma(D) parameters individually. At the end of the article, different scenarios are analyzed to determine whether the developments in this article have improved the DWA behavior. The results show that the prediction step and ANFIS adapt DWA performance by optimizing the path resolution.
publishDate 2019
dc.date.none.fl_str_mv 2019
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/37530
url http://hdl.handle.net/10810/37530
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://www.mdpi.com/2079-9292/8/9/935
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/3.0/es/
Atribución 3.0 España
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/3.0/es/
Atribución 3.0 España
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.228081

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