000117214 001__ 117214
000117214 005__ 20221124103219.0
000117214 0247_ $$2doi$$a10.3389/frai.2021.761123
000117214 0248_ $$2sideral$$a126756
000117214 037__ $$aART-2021-126756
000117214 041__ $$aeng
000117214 100__ $$aFrahi T.
000117214 245__ $$aMonitoring weeder robots and anticipating their functioning by using advanced topological data analysis
000117214 260__ $$c2021
000117214 5060_ $$aAccess copy available to the general public$$fUnrestricted
000117214 5203_ $$aThe 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.
000117214 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000117214 594__ $$a2.2$$b2021
000117214 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000117214 700__ $$aSancarlos A.
000117214 700__ $$aGalle M.
000117214 700__ $$aBeaulieu X.
000117214 700__ $$aChambard A.
000117214 700__ $$aFalco A.
000117214 700__ $$0(orcid)0000-0003-1017-4381$$aCueto, E.$$uUniversidad de Zaragoza
000117214 700__ $$aChinesta F.
000117214 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000117214 773__ $$g4 (2021), 761123 [12 pp]$$pFront. artif. intell.$$tFrontiers in artificial intelligence$$x2624-8212
000117214 8564_ $$s3540633$$uhttps://zaguan.unizar.es/record/117214/files/texto_completo.pdf$$yVersión publicada
000117214 8564_ $$s2251525$$uhttps://zaguan.unizar.es/record/117214/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000117214 909CO $$ooai:zaguan.unizar.es:117214$$particulos$$pdriver
000117214 951__ $$a2022-11-24-10:10:28
000117214 980__ $$aARTICLE