Monocular visual-inertial SLAM-based collision avoidance strategy for fail-safe UAV using fuzzy logic controllers

Fu, Changhong; Olivares Mendez, Miguel Ángel; Suarez Fernandez, Ramon y Campoy Cervera, Pascual (2014). Monocular visual-inertial SLAM-based collision avoidance strategy for fail-safe UAV using fuzzy logic controllers. "Journal of intelligent & robotic systems", v. 73 (n. 1); pp. 513-533. ISSN 0921-0296. https://doi.org/10.1007/s10846-013-9918-3.

Descripción

Título: Monocular visual-inertial SLAM-based collision avoidance strategy for fail-safe UAV using fuzzy logic controllers
Autor/es:
  • Fu, Changhong
  • Olivares Mendez, Miguel Ángel
  • Suarez Fernandez, Ramon
  • Campoy Cervera, Pascual
Tipo de Documento: Artículo
Título de Revista/Publicación: Journal of intelligent & robotic systems
Fecha: Enero 2014
Volumen: 73
Materias:
Palabras Clave Informales: Monocular visual-inertial SLAM Collision avoidance Fuzzy Logic Controller (FLC) Cross Entropy Optimization (CEO) Unmanned Aerial Vehicle (UAV)
Escuela: Centro de Automática y Robótica (CAR) UPM-CSIC
Departamento: Otro
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

In this paper, we developed a novel Cross-Entropy Optimization (CEO)-based Fuzzy Logic Controller (FLC) for Fail-Safe UAV to expand its collision avoidance capabilities in the GPS-denied envrionments using Monocular Visual-Inertial SLAM-based strategy. The function of this FLC aims to control the heading of Fail-Safe UAV to avoid the obstacle, e.g. wall, bridge, tree line et al, using its real-time and accurate localization information. In the Matlab Simulink-based training framework, the Scaling Factor (SF) is adjusted according to the collision avoidance task firstly, and then the Membership Function (MF) is tuned based on the optimized Scaling Factor to further improve the control performances. 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 see-and-avoid tests with a quadcopter have done. The simulation and experiment results show that this new proposed FLC can precisely navigates the Fail-Safe UAV to avoid the obstacle, obtaining better performances compared to only SF optimization-based FLC. To our best knowledge, this is the first work to present the optimized FLC using Cross-Entropy method in both SF and MF optimization, and apply it in the UAV.

Más información

ID de Registro: 36248
Identificador DC: http://oa.upm.es/36248/
Identificador OAI: oai:oa.upm.es:36248
Identificador DOI: 10.1007/s10846-013-9918-3
URL Oficial: https://link.springer.com/article/10.1007/s10846-013-9918-3
Depositado por: Memoria Investigacion
Depositado el: 01 Abr 2017 08:25
Ultima Modificación: 01 Abr 2017 08:25
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