Towards Fast Plume Source Estimation with a Mobile Robot
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/106732 https://doi.org/10.3390/s20247025 |
Resumo: | The estimation of the parameters of an odour source is of high relevance for multiple applications, but it can be a slow and error prone process. This work proposes a fast particle filter-based method for source term estimation with a mobile robot. Two strategies are implemented in order to reduce the computational cost of the filter and increase its accuracy: firstly, the sampling process is adapted by the mobile robot in order to optimise the quality of the data provided to the estimation process; secondly, the filter is initialised only after collecting preliminary data that allow limiting the solution space and use a shorter number of particles than it would be normally necessary. The method assumes a Gaussian plume model for odour dispersion. This models average odour concentrations, but the particle filter was proved adequate to fit instantaneous concentration measurements to that model, while the environment was being sampled. The method was validated in an obstacle free controlled wind tunnel and the validation results show its ability to quickly converge to accurate estimates of the plume's parameters after a reduced number of plume crossings. |
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Towards Fast Plume Source Estimation with a Mobile Robotmobile roboticsgas source localisationparticle filterThe estimation of the parameters of an odour source is of high relevance for multiple applications, but it can be a slow and error prone process. This work proposes a fast particle filter-based method for source term estimation with a mobile robot. Two strategies are implemented in order to reduce the computational cost of the filter and increase its accuracy: firstly, the sampling process is adapted by the mobile robot in order to optimise the quality of the data provided to the estimation process; secondly, the filter is initialised only after collecting preliminary data that allow limiting the solution space and use a shorter number of particles than it would be normally necessary. The method assumes a Gaussian plume model for odour dispersion. This models average odour concentrations, but the particle filter was proved adequate to fit instantaneous concentration measurements to that model, while the environment was being sampled. The method was validated in an obstacle free controlled wind tunnel and the validation results show its ability to quickly converge to accurate estimates of the plume's parameters after a reduced number of plume crossings.MDPI2020-12-08info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/106732http://hdl.handle.net/10316/106732https://doi.org/10.3390/s20247025eng1424-8220Magalhães, HugoBaptista, RuiMacedo, JoãMarques, Linoinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-04-20T08:21:39Zoai:estudogeral.uc.pt:10316/106732Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:23:08.712828Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Towards Fast Plume Source Estimation with a Mobile Robot |
title |
Towards Fast Plume Source Estimation with a Mobile Robot |
spellingShingle |
Towards Fast Plume Source Estimation with a Mobile Robot Magalhães, Hugo mobile robotics gas source localisation particle filter |
title_short |
Towards Fast Plume Source Estimation with a Mobile Robot |
title_full |
Towards Fast Plume Source Estimation with a Mobile Robot |
title_fullStr |
Towards Fast Plume Source Estimation with a Mobile Robot |
title_full_unstemmed |
Towards Fast Plume Source Estimation with a Mobile Robot |
title_sort |
Towards Fast Plume Source Estimation with a Mobile Robot |
author |
Magalhães, Hugo |
author_facet |
Magalhães, Hugo Baptista, Rui Macedo, Joã Marques, Lino |
author_role |
author |
author2 |
Baptista, Rui Macedo, Joã Marques, Lino |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Magalhães, Hugo Baptista, Rui Macedo, Joã Marques, Lino |
dc.subject.por.fl_str_mv |
mobile robotics gas source localisation particle filter |
topic |
mobile robotics gas source localisation particle filter |
description |
The estimation of the parameters of an odour source is of high relevance for multiple applications, but it can be a slow and error prone process. This work proposes a fast particle filter-based method for source term estimation with a mobile robot. Two strategies are implemented in order to reduce the computational cost of the filter and increase its accuracy: firstly, the sampling process is adapted by the mobile robot in order to optimise the quality of the data provided to the estimation process; secondly, the filter is initialised only after collecting preliminary data that allow limiting the solution space and use a shorter number of particles than it would be normally necessary. The method assumes a Gaussian plume model for odour dispersion. This models average odour concentrations, but the particle filter was proved adequate to fit instantaneous concentration measurements to that model, while the environment was being sampled. The method was validated in an obstacle free controlled wind tunnel and the validation results show its ability to quickly converge to accurate estimates of the plume's parameters after a reduced number of plume crossings. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-08 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/106732 http://hdl.handle.net/10316/106732 https://doi.org/10.3390/s20247025 |
url |
http://hdl.handle.net/10316/106732 https://doi.org/10.3390/s20247025 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1424-8220 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
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1799134119436746752 |