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Monitoring Weeder Robots and Anticipating Their Functioning by Using Advanced Topological Data Analysis
The present paper aims at analyzing the topological content of the complex trajectories that weeder-autonomous robots follow in operation. We will prove that the topological descriptors of these trajectories are affected by the robot environment as well as by the robot state, with respect to mainten...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8710805/ https://www.ncbi.nlm.nih.gov/pubmed/34966892 http://dx.doi.org/10.3389/frai.2021.761123 |
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author | Frahi , Tarek Sancarlos , Abel Galle , Mathieu Beaulieu, Xavier Chambard, Anne Falco, Antonio Cueto, Elias Chinesta, Francisco |
author_facet | Frahi , Tarek Sancarlos , Abel Galle , Mathieu Beaulieu, Xavier Chambard, Anne Falco, Antonio Cueto, Elias Chinesta, Francisco |
author_sort | Frahi , Tarek |
collection | PubMed |
description | The present paper aims at analyzing the topological content of the complex trajectories that weeder-autonomous robots follow in operation. We will prove that the topological descriptors of these trajectories are affected by the robot environment as well as by the robot state, with respect to maintenance operations. Most of existing methodologies enabling efficient diagnosis are based on the data analysis, and in particular on some statistical quantities derived from the data. The present work explores the use of an original approach that instead of analyzing quantities derived from the data, analyzes the “shape” of the data, that is, the time series topology based on the homology persistence. We will prove that this procedure is able to extract valuable patterns able to discriminate the trajectories that the robot follows depending on the particular patch in which it operates, as well as to differentiate the robot behavior before and after undergoing a maintenance operation. Even if it is a preliminary work, and it does not pretend to compare its performances with respect to other existing technologies, this work opens new perspectives in considering quite natural and simple descriptors based on the intrinsic information that data contains, with the aim of performing efficient diagnosis and prognosis. |
format | Online Article Text |
id | pubmed-8710805 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87108052021-12-28 Monitoring Weeder Robots and Anticipating Their Functioning by Using Advanced Topological Data Analysis Frahi , Tarek Sancarlos , Abel Galle , Mathieu Beaulieu, Xavier Chambard, Anne Falco, Antonio Cueto, Elias Chinesta, Francisco Front Artif Intell Artificial Intelligence The present paper aims at analyzing the topological content of the complex trajectories that weeder-autonomous robots follow in operation. We will prove that the topological descriptors of these trajectories are affected by the robot environment as well as by the robot state, with respect to maintenance operations. Most of existing methodologies enabling efficient diagnosis are based on the data analysis, and in particular on some statistical quantities derived from the data. The present work explores the use of an original approach that instead of analyzing quantities derived from the data, analyzes the “shape” of the data, that is, the time series topology based on the homology persistence. We will prove that this procedure is able to extract valuable patterns able to discriminate the trajectories that the robot follows depending on the particular patch in which it operates, as well as to differentiate the robot behavior before and after undergoing a maintenance operation. Even if it is a preliminary work, and it does not pretend to compare its performances with respect to other existing technologies, this work opens new perspectives in considering quite natural and simple descriptors based on the intrinsic information that data contains, with the aim of performing efficient diagnosis and prognosis. Frontiers Media S.A. 2021-12-13 /pmc/articles/PMC8710805/ /pubmed/34966892 http://dx.doi.org/10.3389/frai.2021.761123 Text en Copyright © 2021 Frahi , Sancarlos , Galle , Beaulieu, Chambard, Falco, Cueto and Chinesta. 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 | Artificial Intelligence Frahi , Tarek Sancarlos , Abel Galle , Mathieu Beaulieu, Xavier Chambard, Anne Falco, Antonio Cueto, Elias Chinesta, Francisco Monitoring Weeder Robots and Anticipating Their Functioning by Using Advanced Topological Data Analysis |
title | Monitoring Weeder Robots and Anticipating Their Functioning by Using Advanced Topological Data Analysis |
title_full | Monitoring Weeder Robots and Anticipating Their Functioning by Using Advanced Topological Data Analysis |
title_fullStr | Monitoring Weeder Robots and Anticipating Their Functioning by Using Advanced Topological Data Analysis |
title_full_unstemmed | Monitoring Weeder Robots and Anticipating Their Functioning by Using Advanced Topological Data Analysis |
title_short | Monitoring Weeder Robots and Anticipating Their Functioning by Using Advanced Topological Data Analysis |
title_sort | monitoring weeder robots and anticipating their functioning by using advanced topological data analysis |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8710805/ https://www.ncbi.nlm.nih.gov/pubmed/34966892 http://dx.doi.org/10.3389/frai.2021.761123 |
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