Model-based wind estimation for a hovering VTOL tailsitter UAV
Y. Demitrit, S. Verling, T. Stastny, A. Melzer, R. Siegwart
Abstract
To many unmanned aerial vehicle (UAV) designs, the lack of information about the wind speed and direction is a limiting factor in achieving robust outdoor flight. This paper addresses the problem of wind estimation onboard a hovering vertical take-off and landing (VTOL) tailsitter UAV. The proposed estimation framework makes use of the standard onboard sensor suite: inertial measurement unit (IMU), global positioning system (GPS) and a magnetometer. No additional airspeed sensor is needed. As a result, the autopilot is provided with an estimate of the wind velocity vector in the horizontal (north-east) plane. An aerodynamic model of the vehicle has been derived and used in a Kalman filter framework to estimate the horizontal wind velocity vector in real-time. The wind estimator has been implemented onboard the UAVs autopilot and validated in real flight. As a result, we successfully obtain the direction and speed of the wind with an estimation accuracy close to the accuracy range of the ground truth measurement. Furthermore, the derived grey-box model allows to generalise the framework to different airframes.
BibTeX
@inproceedings{icra2017_modelbasedwindes,
title = {Model-based wind estimation for a hovering VTOL tailsitter UAV},
author = {Y. Demitrit and S. Verling and T. Stastny and A. Melzer and R. Siegwart},
booktitle = {ICRA 2017},
year = {2017}
}