Design and Implementation of a High-Precision Wind-Estimation UAV with Onboard Sensors

TL;DR

This paper introduces a real-time wind estimation method for UAVs using only onboard sensors, combining disturbance observers and spline models, validated through extensive experiments showing high accuracy in various conditions.

Contribution

The paper presents a novel onboard sensor-based wind estimation approach that does not rely on external sensors or simplified dynamics, improving accuracy during agile flight.

Findings

Achieves wind speed RMSE as low as 0.06 m/s in wind tunnels.

Provides vertical wind estimates with RMSE below 0.17 m/s.

Outperforms existing methods in accuracy across multiple scenarios.

Abstract

Accurate real-time wind vector estimation is essential for enhancing the safety, navigation accuracy, and energy efficiency of unmanned aerial vehicles (UAVs). Traditional approaches rely on external sensors or simplify vehicle dynamics, which limits their applicability during agile flight or in resource-constrained platforms. This paper proposes a real-time wind estimation method based solely on onboard sensors. The approach first estimates external aerodynamic forces using a disturbance observer (DOB), and then maps these forces to wind vectors using a thin-plate spline (TPS) model. A custom-designed wind barrel mounted on the UAV enhances aerodynamic sensitivity, further improving estimation accuracy. The system is validated through comprehensive experiments in wind tunnels, indoor and outdoor flights. Experimental results demonstrate that the proposed method achieves consistently high-accuracy wind estimation across controlled and real-world conditions, with speed RMSEs as low as \SI{0.06}{m/s} in wind tunnel tests, \SI{0.22}{m/s} during outdoor hover, and below \SI{0.38}{m/s} in indoor and outdoor dynamic flights, and direction RMSEs under \ang{7.3} across all scenarios, outperforming existing baselines. Moreover, the method provides vertical wind estimates -- unavailable in baselines -- with RMSEs below \SI{0.17}{m/s} even during fast indoor translations.