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Drones 2019, 3(2), 31;

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
Author to whom correspondence should be addressed.
Received: 28 January 2019 / Revised: 24 March 2019 / Accepted: 24 March 2019 / Published: 30 March 2019
PDF [1469 KB, uploaded 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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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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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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