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Estimación de orientación de un vehículo aéreo no modelado usando fusión de sensores inerciales y aprendizaje de máquina

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Estimación de orientación de un vehículo aéreo no modelado usando fusión de sensores inerciales y aprendizaje de máquina

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dc.contributor.author Fonnegra, Ruben es_ES
dc.contributor.author Goez, German es_ES
dc.contributor.author Tobón, Andrés es_ES
dc.date.accessioned 2019-09-24T07:39:54Z
dc.date.available 2019-09-24T07:39:54Z
dc.date.issued 2019-09-20
dc.identifier.issn 1697-7912
dc.identifier.uri http://hdl.handle.net/10251/126284
dc.description.abstract [EN] Unmanned Aerial Vehicles (UAV) have oered alternatives for applications in which human integrity is compromised. In this sense, the need of increasing autonomy in these vehicles presents an alternative to artificial intelligence areas to enhance navigation capacities through several environments. This article presents an evaluation for estimating inclination and orientation, using automatic learning algorithms for a dynamic multi-rotor plant. To do so, an experiment is proposed to collect the data from multiple IMU sensors over an UAV main board, and submitted to dierent inclinations before achieving the classification task. The reported results using k nearest neighbors (k - NN), support vector machines (S VM) and Bayes show eficiency during the recognition, obtaining an accuracy score up to 99 %. Besides, the algoritms could be combined along with robust control techniques, which is ideal for implementation in embedded systems with low processing capacities. es_ES
dc.description.abstract [ES] Los vehículos aéreos no tripulados (UAV) ofrecen alternativas para diversas aplicaciones en las que se compromete la integridad humana. En este sentido, la necesidad de incrementar la autonomía de estos vehículos presenta una alternativa al área de inteligencia artificial para aumentar las capacidades de navegación en diversos entornos. Este artículo presenta una evaluación para estimación de inclinación y orientación, utilizando algoritmos de aprendizaje automático para una planta dinámica con múltiples rotores. Para esto se propone un experimento para recopilar datos de unidades de medición inercial (IMU) sobre la placa de un UAV, y sometidos a diferentes inclinaciones antes de lograr la tarea de clasificación. Los resultados reportados usando los algoritmos de k vecinos más cercanos (k-NN), máquinas de soporte vectorial (SVM) y de Bayes muestran eficiencia en el reconocimiento, obteniendo una precisión hasta del 99 %. Además, estos algoritmos podrían combinarse con técnicas de control robustas, ideal para la implementación en sistemas con capacidades de procesamiento limitadas. es_ES
dc.language Español es_ES
dc.relation.ispartof Revista Iberoamericana de Automática e Informática.
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Sensores inerciales es_ES
dc.subject Inteligencia artificial es_ES
dc.subject Aprendizaje de máquinas es_ES
dc.subject UAV es_ES
dc.subject Inertial Sensors es_ES
dc.subject Artificial Intelligence es_ES
dc.subject Machine Learning es_ES
dc.title Estimación de orientación de un vehículo aéreo no modelado usando fusión de sensores inerciales y aprendizaje de máquina es_ES
dc.title.alternative Orientation estimating in a non-modeled aerial vehicle using inertial sensor fusion and machine learning techniques es_ES
dc.type Artículo es_ES
dc.date.updated 2019-09-24T06:57:08Z
dc.identifier.doi 10.4995/riai.2019.11286
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Fonnegra, R.; Goez, G.; Tobón, A. (2019). Estimación de orientación de un vehículo aéreo no modelado usando fusión de sensores inerciales y aprendizaje de máquina. Revista Iberoamericana de Automática e Informática. 16(4):415-422. https://doi.org/10.4995/riai.2019.11286 es_ES
dc.relation.publisherversion https://doi.org/10.4995/riai.2019.11286 es_ES
dc.description.upvformatpinicio 415 es_ES
dc.description.upvformatpfin 422 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 16
dc.description.issue 4
dc.identifier.eissn 1697-7920
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