Reference : Cross-Entropy Optimization for Scaling Factors of a Fuzzy Controller: A See-and-Avoid...
Scientific journals : Article
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/6201
Cross-Entropy Optimization for Scaling Factors of a Fuzzy Controller: A See-and-Avoid Approach for Unmanned Aerial Systems
English
Olivares Mendez, Miguel Angel mailto [Universidad Politecnica de Madrid, Centro de Automatica y Robotica. Spain. > Computer Vision Group]
Mejias, Luis [Queensland University of Technology (QUT), Brisbane, Qld 4001, Australia. > Australian Research Center for Aerospace Automation (ARCAA)]
Campoy, Pascual [Universidad Politecnica de Madrid, Centro de Automatica y Robotica. Spain. > Computer Vision Group]
Mellado-Bataller, Ignacio [Universidad Politecnica de Madrid, Centro de Automatica y Robotica. Spain. > Computer Vision Group]
Jan-2013
Journal of Intelligent & Robotic Systems
Springer
69
1-4
SI
189-205
Yes (verified by ORBilu)
International
0921-0296
Dordrecht
[en] UAV ; UAS ; Fuzzy logic ; Fuzzy control ; Cross-Entropy ; Visual servoing ; Computer vision ; Sense and avoid ; See and avoid ; Optimization
[en] The Cross-Entropy (CE) is an efficient method for the estimation of rare-event probabilities and combinatorial optimization. This work presents a novel approach of the CE for optimization of a Soft-Computing controller. A Fuzzy controller was designed to command an unmanned aerial system (UAS) for avoiding collision task. The only sensor used to accomplish this task was a forward camera. The CE is used to reach a near-optimal controller by modifying the scaling factors of the controller inputs. The optimization was realized using the ROS-Gazebo simulation system. In order to evaluate the optimization a big amount of tests were carried out with a real quadcopter.
Spanish Science and Technology Ministry [CICYT DPI2010-20751-C02-01] ; IRSES Program Marie Curie [FP7 - PIRSES-GA-2009-230797 - ICPUAS]
http://hdl.handle.net/10993/6201
10.1007/s10846-012-9791-5
The work reported in this paper is the consecution of several research stages at the Computer Vision Group - Universidad Politecnica de Madrid and at the Australian Reasearch Centre for Aerospace Automation - Queensland University of Technology (ARCAA-QUT). The authors would like to thank to Caja Madrid for the mobility research grant of one of the authors. This work has been sponsored by the Spanish Science and Technology Ministry under the grant CICYT DPI2010-20751-C02-01 and by the IRSES Program Marie Curie FP7 - PIRSES-GA-2009-230797 - ICPUAS (International Cooperation Program for Unmanned Aerial Systems Research and Development).
1
FP7 ; 230797 - ICPUAS - International Cooperation Program for Unmanned Aerial Systems (UAS) Research and Development (ICPUAS)

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