Abstract
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In this work we present an optimized fuzzy visual servoing system for obstacle avoidance using an unmanned aerial vehicle. The cross-entropy theory is used to optimise the gains of our controllers. The optimization process was made using the ROS-Gazebo 3D simulation with purposeful extensions developed for our experiments. Visual servoing is achieved through an image processing front-end that uses the Camshift algorithm to detect and track objects in the scene. Experimental flight trials using a small quadrotor were performed to validate the parameters estimated from simulation. The integration of crossentropy methods is a straightforward way to estimate optimal gains achieving excellent results when tested in real flights. | |
International
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Si |
Congress
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WCCI 2012 IEEE World Congress on Computational Intelligence |
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960 |
Place
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Brisbane, Australia |
Reviewers
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Si |
ISBN/ISSN
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CDP08UPM |
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Start Date
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10/06/2012 |
End Date
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15/06/2012 |
From page
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0 |
To page
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0 |
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Proceedings on on Computational Intelligence WCCI 2012 IEEE World Congress |