Fuzzy logic controller for predictive vision-based target tracking with an unmanned aerial vehicle
Résumé
We present in this paper a Fuzzy Logic Controller (FLC) combined with a predictive algorithm to track an Unmanned Ground Vehicle (UGV), using an Unmanned Aerial Vehicle (UAV). The UAV is equipped with a down facing camera. The video flow is sent continuously to a ground station to be processed in order to extract the location of the UGV and send the commands back to the UAV to follow autonomously the UGV. To emulate an experienced UAVs pilot, we propose a fuzzy-logic set of rules. Double Exponential Smoothing algorithm is used to filter the measurements and give the predictive value of the errors in the image plan. The FLC inputs are the filtered errors (UGV position) in the image plan and the derivative of its predicted value. The outputs are pitch and roll commands to be sent to the UAV. We show the efficiency of the proposed controller experimentally, and discuss the improvement of the tracking results compared to our previous work.
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