🤖 AI Summary
Real-time, high-precision 3D wind vector estimation for UAVs under external dependency-free conditions remains a significant challenge.
Method: This paper proposes a fully onboard wind estimation algorithm that integrates a disturbance observer (DOB) with thin-plate spline (TPS)-based nonlinear mapping, coupled with a custom-designed, high-sensitivity aerodynamic anemometer structure. This framework enables, for the first time, high-accuracy online estimation of the vertical wind component during dynamic flight.
Contribution/Results: An embedded real-time implementation is validated across diverse scenarios—including wind tunnel tests, indoor/outdoor hover, and aggressive maneuvering flight. Experimental results demonstrate root-mean-square errors (RMSEs) of 0.06 m/s (wind tunnel), 0.22 m/s (outdoor hover), and <0.38 m/s (dynamic flight) for horizontal wind speed; <7.3° for wind direction; and <0.17 m/s for vertical wind—surpassing state-of-the-art methods across all metrics.
📝 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.