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Terrain Awareness Using a Tracked Skid-Steering Vehicle With Passive Independent Suspensions
This paper presents a novel approach for terrain characterization based on a tracked skid-steer vehicle with a passive independent suspensions system. A set of physics-based parameters is used to characterize the terrain properties: drive motor electrical currents, the equivalent track, the power sp...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806075/ https://www.ncbi.nlm.nih.gov/pubmed/33501062 http://dx.doi.org/10.3389/frobt.2019.00046 |
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author | Galati, Rocco Reina, Giulio |
author_facet | Galati, Rocco Reina, Giulio |
author_sort | Galati, Rocco |
collection | PubMed |
description | This paper presents a novel approach for terrain characterization based on a tracked skid-steer vehicle with a passive independent suspensions system. A set of physics-based parameters is used to characterize the terrain properties: drive motor electrical currents, the equivalent track, the power spectral density for the vertical accelerations and motor currents. Based on this feature set, the system predicts the type of terrain that the robot traverses. A wide set of experimental results acquired on various surfaces are provided to verify the study in the field, proving its effectiveness for application in autonomous robots. |
format | Online Article Text |
id | pubmed-7806075 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78060752021-01-25 Terrain Awareness Using a Tracked Skid-Steering Vehicle With Passive Independent Suspensions Galati, Rocco Reina, Giulio Front Robot AI Robotics and AI This paper presents a novel approach for terrain characterization based on a tracked skid-steer vehicle with a passive independent suspensions system. A set of physics-based parameters is used to characterize the terrain properties: drive motor electrical currents, the equivalent track, the power spectral density for the vertical accelerations and motor currents. Based on this feature set, the system predicts the type of terrain that the robot traverses. A wide set of experimental results acquired on various surfaces are provided to verify the study in the field, proving its effectiveness for application in autonomous robots. Frontiers Media S.A. 2019-06-21 /pmc/articles/PMC7806075/ /pubmed/33501062 http://dx.doi.org/10.3389/frobt.2019.00046 Text en Copyright © 2019 Galati and Reina. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Robotics and AI Galati, Rocco Reina, Giulio Terrain Awareness Using a Tracked Skid-Steering Vehicle With Passive Independent Suspensions |
title | Terrain Awareness Using a Tracked Skid-Steering Vehicle With Passive Independent Suspensions |
title_full | Terrain Awareness Using a Tracked Skid-Steering Vehicle With Passive Independent Suspensions |
title_fullStr | Terrain Awareness Using a Tracked Skid-Steering Vehicle With Passive Independent Suspensions |
title_full_unstemmed | Terrain Awareness Using a Tracked Skid-Steering Vehicle With Passive Independent Suspensions |
title_short | Terrain Awareness Using a Tracked Skid-Steering Vehicle With Passive Independent Suspensions |
title_sort | terrain awareness using a tracked skid-steering vehicle with passive independent suspensions |
topic | Robotics and AI |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806075/ https://www.ncbi.nlm.nih.gov/pubmed/33501062 http://dx.doi.org/10.3389/frobt.2019.00046 |
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