Differential-drive mobile robot control using a cloud of particles approach
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/172850 |
Resumo: | Common control systems for mobile robots include the use of some deterministic control law coupled with some pose estimation method, such as the extended Kalman filter, by considering the certainty equivalence principle. Recent approaches consider the use of partially observable Markov decision process strategies together with Bayesian estimators. These methods are well suited to handle the uncertainty in pose estimation but demand significant processing power. In order to reduce the required processing power and still allow for multimodal or non-Gaussian uncertain distributions, we propose a scheme based on a particle filter and a corresponding cloud of control signals. The approach avoids the use of the certainty equivalence principle by postponing the decision on the optimal estimate to the control stage. As the mapping between the pose space and the control action space is nonlinear and the best estimation of robot pose is uncertain, postponing the decision to the control space makes it possible to select a better control action in the presence of multimodal and non-Gaussian uncertainty models. Simulation results are presented. |
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Lages, Walter FetterAlves, Jorge Augusto Vasconcelos2018-02-24T02:26:24Z20171729-8806http://hdl.handle.net/10183/172850001059075Common control systems for mobile robots include the use of some deterministic control law coupled with some pose estimation method, such as the extended Kalman filter, by considering the certainty equivalence principle. Recent approaches consider the use of partially observable Markov decision process strategies together with Bayesian estimators. These methods are well suited to handle the uncertainty in pose estimation but demand significant processing power. In order to reduce the required processing power and still allow for multimodal or non-Gaussian uncertain distributions, we propose a scheme based on a particle filter and a corresponding cloud of control signals. The approach avoids the use of the certainty equivalence principle by postponing the decision on the optimal estimate to the control stage. As the mapping between the pose space and the control action space is nonlinear and the best estimation of robot pose is uncertain, postponing the decision to the control space makes it possible to select a better control action in the presence of multimodal and non-Gaussian uncertainty models. Simulation results are presented.application/pdfengInternational journal of advanced robotic systems [recurso eletrônico]. [Thousand Oaks, Califórnia]. Vol. 14, no. 1 (Jan./Feb. 2017), p. 1-12Robôs móveisSistemas de controleMobile roboticsParticle filterNonlinear controlStochastic controlStochastic estimationDifferential-drive mobile robot control using a cloud of particles approachEstrangeiroinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSORIGINAL001059075.pdf001059075.pdfTexto completo (inglês)application/pdf766844http://www.lume.ufrgs.br/bitstream/10183/172850/1/001059075.pdf189546bf1f49de0fd53f7068f5496c00MD51TEXT001059075.pdf.txt001059075.pdf.txtExtracted Texttext/plain45362http://www.lume.ufrgs.br/bitstream/10183/172850/2/001059075.pdf.txtc1308c1c2c830c4a23dbb627b4740900MD52THUMBNAIL001059075.pdf.jpg001059075.pdf.jpgGenerated Thumbnailimage/jpeg2008http://www.lume.ufrgs.br/bitstream/10183/172850/3/001059075.pdf.jpgcf08e05bd9810dafdd9fba6da661c717MD5310183/1728502018-10-29 08:51:57.168oai:www.lume.ufrgs.br:10183/172850Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2018-10-29T11:51:57Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
Differential-drive mobile robot control using a cloud of particles approach |
title |
Differential-drive mobile robot control using a cloud of particles approach |
spellingShingle |
Differential-drive mobile robot control using a cloud of particles approach Lages, Walter Fetter Robôs móveis Sistemas de controle Mobile robotics Particle filter Nonlinear control Stochastic control Stochastic estimation |
title_short |
Differential-drive mobile robot control using a cloud of particles approach |
title_full |
Differential-drive mobile robot control using a cloud of particles approach |
title_fullStr |
Differential-drive mobile robot control using a cloud of particles approach |
title_full_unstemmed |
Differential-drive mobile robot control using a cloud of particles approach |
title_sort |
Differential-drive mobile robot control using a cloud of particles approach |
author |
Lages, Walter Fetter |
author_facet |
Lages, Walter Fetter Alves, Jorge Augusto Vasconcelos |
author_role |
author |
author2 |
Alves, Jorge Augusto Vasconcelos |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Lages, Walter Fetter Alves, Jorge Augusto Vasconcelos |
dc.subject.por.fl_str_mv |
Robôs móveis Sistemas de controle |
topic |
Robôs móveis Sistemas de controle Mobile robotics Particle filter Nonlinear control Stochastic control Stochastic estimation |
dc.subject.eng.fl_str_mv |
Mobile robotics Particle filter Nonlinear control Stochastic control Stochastic estimation |
description |
Common control systems for mobile robots include the use of some deterministic control law coupled with some pose estimation method, such as the extended Kalman filter, by considering the certainty equivalence principle. Recent approaches consider the use of partially observable Markov decision process strategies together with Bayesian estimators. These methods are well suited to handle the uncertainty in pose estimation but demand significant processing power. In order to reduce the required processing power and still allow for multimodal or non-Gaussian uncertain distributions, we propose a scheme based on a particle filter and a corresponding cloud of control signals. The approach avoids the use of the certainty equivalence principle by postponing the decision on the optimal estimate to the control stage. As the mapping between the pose space and the control action space is nonlinear and the best estimation of robot pose is uncertain, postponing the decision to the control space makes it possible to select a better control action in the presence of multimodal and non-Gaussian uncertainty models. Simulation results are presented. |
publishDate |
2017 |
dc.date.issued.fl_str_mv |
2017 |
dc.date.accessioned.fl_str_mv |
2018-02-24T02:26:24Z |
dc.type.driver.fl_str_mv |
Estrangeiro info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10183/172850 |
dc.identifier.issn.pt_BR.fl_str_mv |
1729-8806 |
dc.identifier.nrb.pt_BR.fl_str_mv |
001059075 |
identifier_str_mv |
1729-8806 001059075 |
url |
http://hdl.handle.net/10183/172850 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
International journal of advanced robotic systems [recurso eletrônico]. [Thousand Oaks, Califórnia]. Vol. 14, no. 1 (Jan./Feb. 2017), p. 1-12 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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application/pdf |
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