Evaluation of Skid-Steering Kinematic Models for Subarctic Environments
International audience In subarctic and arctic areas, large and heavy skid-steered robots are preferred for their robustness and ability to operate on difficult terrain. State estimation, motion control and path planning for these robots rely on accurate odometry models based on wheel velocities. Ho...
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ftinraparis:oai:HAL:hal-04589438v1 2024-06-23T07:50:25+00:00 Evaluation of Skid-Steering Kinematic Models for Subarctic Environments Baril, Dominic Grondin, Vincent Deschenes, Simon-Pierre Laconte, Johann Vaidis, Maxime Kubelka, Vladimir Gallant, Andre Giguere, Philippe Pomerleau, Francois Université Laval Québec (ULaval) Ottawa, France 2020-05-13 https://hal.inrae.fr/hal-04589438 https://hal.inrae.fr/hal-04589438/document https://hal.inrae.fr/hal-04589438/file/2004.05131v1.pdf https://doi.org/10.1109/CRV50864.2020.00034 en eng HAL CCSD IEEE info:eu-repo/semantics/altIdentifier/doi/10.1109/CRV50864.2020.00034 hal-04589438 https://hal.inrae.fr/hal-04589438 https://hal.inrae.fr/hal-04589438/document https://hal.inrae.fr/hal-04589438/file/2004.05131v1.pdf doi:10.1109/CRV50864.2020.00034 info:eu-repo/semantics/OpenAccess 17th Conference on Computer and Robot Vision (CRV) https://hal.inrae.fr/hal-04589438 17th Conference on Computer and Robot Vision (CRV), May 2020, Ottawa, France. pp.198-205, ⟨10.1109/CRV50864.2020.00034⟩ mobile robots skid-steering vehicles robot kinematics winter [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO] info:eu-repo/semantics/conferenceObject Conference papers 2020 ftinraparis https://doi.org/10.1109/CRV50864.2020.00034 2024-06-04T14:56:34Z International audience In subarctic and arctic areas, large and heavy skid-steered robots are preferred for their robustness and ability to operate on difficult terrain. State estimation, motion control and path planning for these robots rely on accurate odometry models based on wheel velocities. However, the state-of-theart odometry models for skid-steer mobile robots (SSMRs) have usually been tested on relatively lightweight platforms. In this paper, we focus on how these models perform when deployed on a large and heavy (590 kg) SSMR. We collected more than 2 km of data on both snow and concrete. We compare the ideal differential-drive, extended differential-drive, radius-ofcurvature-based, and full linear kinematic models commonly deployed for SSMRs. Each of the models is fine-tuned by searching their optimal parameters on both snow and concrete. We then discuss the relationship between the parameters, the model tuning, and the final accuracy of the models. Conference Object Arctic Subarctic Institut National de la Recherche Agronomique: ProdINRA Arctic 2020 17th Conference on Computer and Robot Vision (CRV) 198 205 |
institution |
Open Polar |
collection |
Institut National de la Recherche Agronomique: ProdINRA |
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ftinraparis |
language |
English |
topic |
mobile robots skid-steering vehicles robot kinematics winter [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO] |
spellingShingle |
mobile robots skid-steering vehicles robot kinematics winter [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO] Baril, Dominic Grondin, Vincent Deschenes, Simon-Pierre Laconte, Johann Vaidis, Maxime Kubelka, Vladimir Gallant, Andre Giguere, Philippe Pomerleau, Francois Evaluation of Skid-Steering Kinematic Models for Subarctic Environments |
topic_facet |
mobile robots skid-steering vehicles robot kinematics winter [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO] |
description |
International audience In subarctic and arctic areas, large and heavy skid-steered robots are preferred for their robustness and ability to operate on difficult terrain. State estimation, motion control and path planning for these robots rely on accurate odometry models based on wheel velocities. However, the state-of-theart odometry models for skid-steer mobile robots (SSMRs) have usually been tested on relatively lightweight platforms. In this paper, we focus on how these models perform when deployed on a large and heavy (590 kg) SSMR. We collected more than 2 km of data on both snow and concrete. We compare the ideal differential-drive, extended differential-drive, radius-ofcurvature-based, and full linear kinematic models commonly deployed for SSMRs. Each of the models is fine-tuned by searching their optimal parameters on both snow and concrete. We then discuss the relationship between the parameters, the model tuning, and the final accuracy of the models. |
author2 |
Université Laval Québec (ULaval) |
format |
Conference Object |
author |
Baril, Dominic Grondin, Vincent Deschenes, Simon-Pierre Laconte, Johann Vaidis, Maxime Kubelka, Vladimir Gallant, Andre Giguere, Philippe Pomerleau, Francois |
author_facet |
Baril, Dominic Grondin, Vincent Deschenes, Simon-Pierre Laconte, Johann Vaidis, Maxime Kubelka, Vladimir Gallant, Andre Giguere, Philippe Pomerleau, Francois |
author_sort |
Baril, Dominic |
title |
Evaluation of Skid-Steering Kinematic Models for Subarctic Environments |
title_short |
Evaluation of Skid-Steering Kinematic Models for Subarctic Environments |
title_full |
Evaluation of Skid-Steering Kinematic Models for Subarctic Environments |
title_fullStr |
Evaluation of Skid-Steering Kinematic Models for Subarctic Environments |
title_full_unstemmed |
Evaluation of Skid-Steering Kinematic Models for Subarctic Environments |
title_sort |
evaluation of skid-steering kinematic models for subarctic environments |
publisher |
HAL CCSD |
publishDate |
2020 |
url |
https://hal.inrae.fr/hal-04589438 https://hal.inrae.fr/hal-04589438/document https://hal.inrae.fr/hal-04589438/file/2004.05131v1.pdf https://doi.org/10.1109/CRV50864.2020.00034 |
op_coverage |
Ottawa, France |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Subarctic |
genre_facet |
Arctic Subarctic |
op_source |
17th Conference on Computer and Robot Vision (CRV) https://hal.inrae.fr/hal-04589438 17th Conference on Computer and Robot Vision (CRV), May 2020, Ottawa, France. pp.198-205, ⟨10.1109/CRV50864.2020.00034⟩ |
op_relation |
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op_rights |
info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.1109/CRV50864.2020.00034 |
container_title |
2020 17th Conference on Computer and Robot Vision (CRV) |
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