Shaping Wind-Tunnel Airflow for Unmanned Aerial Vehicles using Online Learning
The development and testing of advanced aerial robots require experiments in controlled environments with tailored airflow profiles. This paper presents an online learning algorithm for controlling the complex airflow field in a multi-fan vertical wind tunnel. Our method combines a simplified physical model with iterative, measurement-based learning, enabling sample-efficient convergence to desired airflow distributions. We demonstrate the method's versatility by generating complex airflow, such as uniform, Gaussian, and parabolic profiles. Crucially, we show that our algorithm can produce an airflow profile specifically designed for passive soaring, greatly enhancing flight performance o...
| Authors | Ghadeer Elmkaiel, Michael Muehlebach |
|---|---|
| Primary category | cs.RO |
| Published | 4 August 2026 |
| Last updated at source | 4 August 2026 |
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