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A K Nearest Neighborhood-Based Wind Estimation for Rotary-Wing VTOL UAVs

Department of Mechanical and Aerospace Engineering, Rutgers, The State University of New Jersey, 98 Brett Road, Piscataway, NJ 08854, USA
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Drones 2019, 3(2), 31; https://doi.org/10.3390/drones3020031
Received: 28 January 2019 / Revised: 24 March 2019 / Accepted: 24 March 2019 / Published: 30 March 2019
Wind speed estimation for rotary-wing vertical take-off and landing (VTOL) UAVs is challenging due to the low accuracy of airspeed sensors, which can be severely affected by the rotor’s down-wash effect. Unlike traditional aerodynamic modeling solutions, in this paper, we present a K Nearest Neighborhood learning-based method which does not require the details of the aerodynamic information. The proposed method includes two stages: an off-line training stage and an on-line wind estimation stage. Only flight data is used for the on-line estimation stage, without direct airspeed measurements. We use Parrot AR.Drone as the testing quadrotor, and a commercial fan is used to generate wind disturbance. Experimental results demonstrate the accuracy and robustness of the developed wind estimation algorithms under hovering conditions. View Full-Text
Keywords: VTOL UAVs; wind estimation; learning-based model; KNN VTOL UAVs; wind estimation; learning-based model; KNN
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Wang, L.; Misra, G.; Bai, X. A K Nearest Neighborhood-Based Wind Estimation for Rotary-Wing VTOL UAVs. Drones 2019, 3, 31.

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