Shaping Wind-Tunnel Airflow for Unmanned Aerial Vehicles using Online Learning

2026-08-04

AI Summary

This work addresses the inefficiency of customized airflow generation in multi-fan vertical wind tunnels by proposing an integrated framework that combines a simplified physics-based model with a sample-efficient online learning approach. Leveraging real-time airflow measurements and feedback, the method iteratively refines a coordinated control policy for multiple fans, enabling flexible and stable synthesis of diverse complex velocity profiles—including uniform, Gaussian, and parabolic distributions. Notably, it achieves, for the first time, tailored airflow generation specifically optimized for passive gliding performance, significantly enhancing aerial robot flight capabilities across varying fan configurations.

Abstract

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 of a soaring robot. Variability, practical utility, and robustness of our approach are further highlighted by successful operation with a varying number of fans.

Problem

• wind tunnel
• airflow control
• unmanned aerial vehicles
• online learning
• passive soaring

Innovation

• online learning
• airflow shaping
• wind tunnel control
• aerial robotics
• passive soaring