UAS See-and-Avoid Strategy using a Fuzzy Logic Controller Optimized by Cross-Entropy in Scaling Factors and Membership Functions.

Fu, Changhong; Olivares Méndez, Miguel Ángel; Campoy Cervera, Pascual y Suárez Fernández, Ramón (2013). UAS See-and-Avoid Strategy using a Fuzzy Logic Controller Optimized by Cross-Entropy in Scaling Factors and Membership Functions.. En: "2013 International Conference on Unmanned Aircraft Systems (ICUAS'13)", 28-31 May 2013, Atlanta, GA, USA. ISBN 978-1-4799-0817-2. pp. 532-541.

Descripción

Título: UAS See-and-Avoid Strategy using a Fuzzy Logic Controller Optimized by Cross-Entropy in Scaling Factors and Membership Functions.
Autor/es:
  • Fu, Changhong
  • Olivares Méndez, Miguel Ángel
  • Campoy Cervera, Pascual
  • Suárez Fernández, Ramón
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 2013 International Conference on Unmanned Aircraft Systems (ICUAS'13)
Fechas del Evento: 28-31 May 2013
Lugar del Evento: Atlanta, GA, USA
Título del Libro: 2013 International Conference on Unmanned Aircraft Systems (ICUAS'13). Conference Proceedings
Fecha: 2013
ISBN: 978-1-4799-0817-2
Materias:
Escuela: E.T.S.I. Industriales (UPM)
Departamento: Automática, Ingeniería Electrónica e Informática Industrial [hasta 2014]
Grupo Investigación UPM: Computer Vision Group - Universidad Politécnica de Madrid
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

This work aims to develop a novel Cross-Entropy (CE) optimization-based fuzzy controller for Unmanned Aerial Monocular Vision-IMU System (UAMVIS) to solve the seeand-avoid problem using its accurate autonomous localization information. The function of this fuzzy controller is regulating the heading of this system to avoid the obstacle, e.g. wall. In the Matlab Simulink-based training stages, the Scaling Factor (SF) is adjusted according to the specified task firstly, and then the Membership Function (MF) is tuned based on the optimized Scaling Factor to further improve the collison avoidance performance. After obtained the optimal SF and MF, 64% of rules has been reduced (from 125 rules to 45 rules), and a large number of real flight tests with a quadcopter have been done. The experimental results show that this approach precisely navigates the system to avoid the obstacle. To our best knowledge, this is the first work to present the optimized fuzzy controller for UAMVIS using Cross-Entropy method in Scaling Factors and Membership Functions optimization.

Proyectos asociados

TipoCódigoAcrónimoResponsableTítulo
Gobierno de EspañaDPI2010-20751-C02-01CICYTSin especificar, China Scholarship Council (CSC)
FP7Sin especificarECHORDSin especificarSin especificar

Más información

ID de Registro: 37638
Identificador DC: http://oa.upm.es/37638/
Identificador OAI: oai:oa.upm.es:37638
URL Oficial: http://www.uasconferences.com/
Depositado por: Memoria Investigacion
Depositado el: 10 Feb 2016 15:39
Ultima Modificación: 10 Feb 2016 15:46
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