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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|
Falcó Montesinos, Antonio.
Cueto Prendes, Elías.
|Keywords:||Vides - Cultivo - Automatización.; Viticulture - Automation.; Topology.; Análisis de datos.; Data analysis.; Inteligencia artificial.; Artificial intelligence.; Topología.|
|Citation:||Frahi, T., Sancarlos, A., Galle, M., Beaulieu, X., Chambard, A., Falco, A., Cueto, E. & Chinesta, F. (2021). Monitoring weeder robots and anticipating their functioning by using advanced topological data analysis. Frontiers in Artificial Intelligence, vol. 4, art. 761123 (13 dec.). DOI: https://doi.org/10.3389/frai.2021.761123|
|Abstract:||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.|
|Description:||Este artículo se encuentra disponible en la página web de la revista en la siguiente URL: https://www.frontiersin.org/articles/10.3389/frai.2021.761123/full|
Este artículo pertenece a la sección "AI in Business".
|Center :||Universidad Cardenal Herrera-CEU|
|Appears in Collections:||Dpto. Matemáticas, Física y Ciencias Tecnológicas|
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