Publicação
A machine learning approach to robot localization using fiducial markers in robotatfactory 4.0 competition
| Resumo: | Localization is a crucial skill in mobile robotics because the robot needs to make reasonable navigation decisions to complete its mission. Many approaches exist to implement localization, but artificial intelligence can be an interesting alternative to traditional localization techniques based on model calculations. This work proposes a machine learning approach to solve the localization problem in the RobotAtFactory 4.0 competition. The idea is to obtain the relative pose of an onboard camera with respect to fiducial markers (ArUcos) and then estimate the robot pose with machine learning. The approaches were validated in a simulation. Several algorithms were tested, and the best results were obtained by using Random Forest Regressor, with an error on the millimeter scale. The proposed solution presents results as high as the analytical approach for solving the localization problem in the RobotAtFactory 4.0 scenario, with the advantage of not requiring explicit knowledge of the exact positions of the fiducial markers, as in the analytical approach. |
|---|---|
| Autores principais: | Klein, Luan C. |
| Outros Autores: | Braun, João; Mendes, João; Pinto, Vítor H.; Martins, Felipe N.; Oliveira, Andre Schneider; Oliveira, Andre Schneider; Wörtche, Heinrich; Costa, Paulo Gomes da; Lima, José |
| Assunto: | Indoor localization Machine learning Fiducial markers Industry 4.0 Robotics competitions |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| _version_ | 1863850657596833792 |
|---|---|
| author | Klein, Luan C. |
| author2 | Braun, João Mendes, João Pinto, Vítor H. Martins, Felipe N. Oliveira, Andre Schneider Oliveira, Andre Schneider Wörtche, Heinrich Costa, Paulo Gomes da Lima, José |
| author2_role | author author author author author author author author author |
| author_facet | Klein, Luan C. Braun, João Mendes, João Pinto, Vítor H. Martins, Felipe N. Oliveira, Andre Schneider Oliveira, Andre Schneider Wörtche, Heinrich Costa, Paulo Gomes da Lima, José |
| author_role | author |
| contributor_name_str_mv | Biblioteca Digital do IPB |
| country_str | PT |
| creators_json_str | [{\"Person.name\":\"Klein, Luan C.\"},{\"Person.name\":\"Braun, João\",\"Person.identifier.orcid\":\"0000-0003-0276-4314\"},{\"Person.name\":\"Mendes, João\",\"Person.identifier.orcid\":\"0000-0003-0979-8314\"},{\"Person.name\":\"Pinto, Vítor H.\"},{\"Person.name\":\"Martins, Felipe N.\"},{\"Person.name\":\"Oliveira, Andre Schneider\"},{\"Person.name\":\"Oliveira, Andre Schneider\"},{\"Person.name\":\"Wörtche, Heinrich\"},{\"Person.name\":\"Costa, Paulo Gomes da\"},{\"Person.name\":\"Lima, José\",\"Person.identifier.orcid\":\"0000-0001-7902-1207\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Biblioteca Digital do IPB |
| datacite.creators.creator.creatorName.fl_str_mv | Klein, Luan C. Braun, João Mendes, João Pinto, Vítor H. Martins, Felipe N. Oliveira, Andre Schneider Oliveira, Andre Schneider Wörtche, Heinrich Costa, Paulo Gomes da Lima, José |
| datacite.date.Accepted.fl_str_mv | 2023-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2018-06-15T15:18:21Z |
| datacite.date.embargoed.fl_str_mv | 2018-06-15T15:18:21Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Indoor localization Machine learning Fiducial markers Industry 4.0 Robotics competitions |
| datacite.titles.title.fl_str_mv | A machine learning approach to robot localization using fiducial markers in robotatfactory 4.0 competition |
| dc.contributor.none.fl_str_mv | Biblioteca Digital do IPB |
| dc.creator.none.fl_str_mv | Klein, Luan C. Braun, João Mendes, João Pinto, Vítor H. Martins, Felipe N. Oliveira, Andre Schneider Oliveira, Andre Schneider Wörtche, Heinrich Costa, Paulo Gomes da Lima, José |
| dc.date.Accepted.fl_str_mv | 2023-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2018-06-15T15:18:21Z |
| dc.date.embargoed.fl_str_mv | 2018-06-15T15:18:21Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10198/17690 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | MDPI |
| dc.rights.cclincense.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Indoor localization Machine learning Fiducial markers Industry 4.0 Robotics competitions |
| dc.title.fl_str_mv | A machine learning approach to robot localization using fiducial markers in robotatfactory 4.0 competition |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | Localization is a crucial skill in mobile robotics because the robot needs to make reasonable navigation decisions to complete its mission. Many approaches exist to implement localization, but artificial intelligence can be an interesting alternative to traditional localization techniques based on model calculations. This work proposes a machine learning approach to solve the localization problem in the RobotAtFactory 4.0 competition. The idea is to obtain the relative pose of an onboard camera with respect to fiducial markers (ArUcos) and then estimate the robot pose with machine learning. The approaches were validated in a simulation. Several algorithms were tested, and the best results were obtained by using Random Forest Regressor, with an error on the millimeter scale. The proposed solution presents results as high as the analytical approach for solving the localization problem in the RobotAtFactory 4.0 scenario, with the advantage of not requiring explicit knowledge of the exact positions of the fiducial markers, as in the analytical approach. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | article |
| fulltext.url.fl_str_mv | https://bibliotecadigital.ipb.pt/bitstreams/c3030bed-ad68-45aa-a649-81c1145f4fec/download |
| id | ipb_7233bc0da0ec96a8cdeee61dc22eff93 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10198/17690 |
| instacron_str | ipb |
| institution | Instituto Politécnico de Bragança |
| instname_str | Instituto Politécnico de Bragança |
| language | eng |
| network_acronym_str | ipb |
| network_name_str | Biblioteca Digital do IPB |
| oai_identifier_str | oai:bibliotecadigital.ipb.pt:10198/17690 |
| organization_str_mv | urn:organizationAcronym:ipb |
| person_str_mv | Klein, Luan C. Braun, João Braun, João https://www.ciencia-id.pt/BF13-D66B-7D08 BF13-D66B-7D08 http://orcid.org/0000-0003-0276-4314 0000-0003-0276-4314 Mendes, João Mendes, João https://www.ciencia-id.pt/EA1F-844D-6BA9 EA1F-844D-6BA9 http://orcid.org/0000-0003-0979-8314 0000-0003-0979-8314 Pinto, Vítor H. Martins, Felipe N. Oliveira, Andre Schneider Oliveira, Andre Schneider Wörtche, Heinrich Costa, Paulo Gomes da Lima, José Lima, José https://www.ciencia-id.pt/6016-C902-86A9 6016-C902-86A9 http://orcid.org/0000-0001-7902-1207 0000-0001-7902-1207 |
| publishDate | 2023 |
| publisher.none.fl_str_mv | MDPI |
| reponame_str | Biblioteca Digital do IPB |
| repository_id_str | urn:repositoryAcronym:ipb |
| service_str_mv | urn:repositoryAcronym:ipb |
| spelling | engMDPIpt_PTLocalization is a crucial skill in mobile robotics because the robot needs to make reasonable navigation decisions to complete its mission. Many approaches exist to implement localization, but artificial intelligence can be an interesting alternative to traditional localization techniques based on model calculations. This work proposes a machine learning approach to solve the localization problem in the RobotAtFactory 4.0 competition. The idea is to obtain the relative pose of an onboard camera with respect to fiducial markers (ArUcos) and then estimate the robot pose with machine learning. The approaches were validated in a simulation. Several algorithms were tested, and the best results were obtained by using Random Forest Regressor, with an error on the millimeter scale. The proposed solution presents results as high as the analytical approach for solving the localization problem in the RobotAtFactory 4.0 scenario, with the advantage of not requiring explicit knowledge of the exact positions of the fiducial markers, as in the analytical approach.application/pdfpt_PTA machine learning approach to robot localization using fiducial markers in robotatfactory 4.0 competitionKlein, Luan C.PersonalBraun, JoãoDSpacehttp://dspace.org/items/b8dfcbd7-1b89-48f3-afee-3e7d3f3c90d4DSpacehttp://dspace.org/items/b8dfcbd7-1b89-48f3-afee-3e7d3f3c90d4BraunJoão A.Ciência IDhttps://www.ciencia-id.ptBF13-D66B-7D08ORCIDhttp://orcid.org0000-0003-0276-4314Scopus Author IDhttps://www.scopus.com57211244317PersonalMendes, JoãoDSpacehttp://dspace.org/items/b5c9de22-cf9e-47b8-b7a4-26e08fb12b28DSpacehttp://dspace.org/items/b5c9de22-cf9e-47b8-b7a4-26e08fb12b28MendesJoãoCiência IDhttps://www.ciencia-id.ptEA1F-844D-6BA9ORCIDhttp://orcid.org0000-0003-0979-8314Scopus Author IDhttps://www.scopus.com57225794972Pinto, Vítor H.Martins, Felipe N.Oliveira, Andre SchneiderOliveira, Andre SchneiderWörtche, HeinrichCosta, Paulo Gomes daPersonalLima, JoséDSpacehttp://dspace.org/items/d88c2b2a-efc2-48ef-b1fd-1145475e0055DSpacehttp://dspace.org/items/d88c2b2a-efc2-48ef-b1fd-1145475e0055LimaJoséCiência IDhttps://www.ciencia-id.pt6016-C902-86A9ORCIDhttp://orcid.org0000-0001-7902-1207Researcher IDhttps://www.researcherid.comL-3370-2014Scopus Author IDhttps://www.scopus.com55851941311HostingInstitutionOrganizationalBiblioteca Digital do IPBe-mailmailto:dspace@ipb.ptdspace@ipb.ptDOIIsPartOf10.3390/s230631282018-06-15T15:18:21Z20232023-01-01T00:00:00ZHandlehttp://hdl.handle.net/10198/17690http://purl.org/coar/access_right/c_abf2open accessIndoor localizationMachine learningFiducial markersIndustry 4.0Robotics competitions2510349 bytesliteraturehttp://purl.org/coar/resource_type/c_6501journal article2023http://creativecommons.org/licenses/by/4.0/http://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://bibliotecadigital.ipb.pt/bitstreams/c3030bed-ad68-45aa-a649-81c1145f4fec/downloadSensors |
| spellingShingle | A machine learning approach to robot localization using fiducial markers in robotatfactory 4.0 competition Klein, Luan C. Indoor localization Machine learning Fiducial markers Industry 4.0 Robotics competitions |
| subject.fl_str_mv | Indoor localization Machine learning Fiducial markers Industry 4.0 Robotics competitions |
| title | A machine learning approach to robot localization using fiducial markers in robotatfactory 4.0 competition |
| title_full | A machine learning approach to robot localization using fiducial markers in robotatfactory 4.0 competition |
| title_fullStr | A machine learning approach to robot localization using fiducial markers in robotatfactory 4.0 competition |
| title_full_unstemmed | A machine learning approach to robot localization using fiducial markers in robotatfactory 4.0 competition |
| title_short | A machine learning approach to robot localization using fiducial markers in robotatfactory 4.0 competition |
| title_sort | A machine learning approach to robot localization using fiducial markers in robotatfactory 4.0 competition |
| topic | Indoor localization Machine learning Fiducial markers Industry 4.0 Robotics competitions |
| topic_facet | Indoor localization Machine learning Fiducial markers Industry 4.0 Robotics competitions |
| url | http://hdl.handle.net/10198/17690 |
| visible | 1 |