117214 20221124103219.0 doi 10.3389/frai.2021.761123 sideral 126756 ART-2021-126756 eng Frahi T. Monitoring weeder robots and anticipating their functioning by using advanced topological data analysis 2021 Access copy available to the general public Unrestricted 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. Copyright © 2021 Frahi, Sancarlos, Galle, Beaulieu, Chambard, Falco, Cueto and Chinesta. info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 2.2 2021 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Sancarlos A. Galle M. Beaulieu X. Chambard A. Falco A. Cueto, E. Universidad de Zaragoza (orcid)0000-0003-1017-4381 Chinesta F. 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 4 (2021), 761123 [12 pp] Front. artif. intell. Frontiers in artificial intelligence 2624-8212 3540633 http://zaguan.unizar.es/record/117214/files/texto_completo.pdf Versión publicada 2251525 http://zaguan.unizar.es/record/117214/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:117214 articulos driver 2022-11-24-10:10:28 ARTICLE