See-and-Avoid Quadcopter using Fuzzy Control Optimized by Cross-Entropy
English
Olivares Mendez, Miguel Angel[Universidad Politecnica de Madrid, Centro de Automatica y Robotica, Spain > Computer Vision Group]
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]
Mejias, Luis[Queenland University of Technology (QUT), Brisbane, Australia > Australian Research Center for Aerospace Automation (ARCAA)]
2012
See-and-Avoid Quadcopter using Fuzzy Control Optimized by Cross-Entropy
Ieee
Yes
International
New York
IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)/International Joint Conference on Neural Networks (IJCNN)/IEEE Congress on Evolutionary Computation (IEEE-CEC)/IEEE World Congress on Computational Intelligence (IEEE-WCCI)
JUN 10-15, 2012
IEEE
Brisbane
AUSTRALIA
[en] UAV ; Fuzzy Control ; See and Avoid ; control ; navigation ; soft-Computing ; Optimization ; Cross-Entropy
[en] 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 cross-entropy methods is a straightforward way to estimate optimal gains achieving excellent results when tested in real flights.