用在线学习控制风洞气流，提升无人机飞行性能。
Shaping Wind-Tunnel Airflow for Unmanned Aerial Vehicles using Online Learning
- 结合简化物理模型与实时测量反馈，实现高效气流调控。
- 可生成均匀、高斯、抛物线等多种复杂气流分布。
- 能设计适合滑翔飞行的气流，显著提升无人机续航能力。
先进空中机器人研发与测试需在可控环境中实现定制化气流。本文提出一种用于多风扇垂直风洞的在线学习算法，通过融合简化物理模型与迭代测量学习，实现对复杂气流场的高效控制。该方法能在少量样本下快速收敛至目标气流分布。实验验证了其生成均匀、高斯及抛物线形等复杂气流的能力。关键成果是成功设计出专为被动滑翔优化的气流，显著提升滑翔机器人的飞行性能。此外，算法在不同风扇数量下的稳定运行进一步证明其鲁棒性与实用性。
原文摘要 · Abstract (English)
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.
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