AI 中文总结
该研究提出一种结合简化物理模型与迭代测量学习的在线学习算法，用于控制多风扇垂直风洞气流，可生成多种气流剖面，还能提升滑翔机器人飞行性能，且对风扇数量变化具备鲁棒性。
AI 中文摘要
先进空中机器人的研发与测试需在可控环境中开展实验，以获取定制化气流剖面。本文提出一种在线学习算法，用于控制多风扇垂直风洞中的复杂气流场。该方法结合简化物理模型与基于测量的迭代学习，实现对期望气流分布的高效样本收敛。我们通过生成均匀、高斯、抛物线等复杂气流剖面，验证了方法的通用性。关键在于，该算法可生成专为被动滑翔设计的气流剖面，大幅提升滑翔机器人的飞行性能。此外，通过在不同数量风扇下成功运行，进一步凸显了本方法的可变性、实用性与鲁棒性。
英文摘要
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.
Comments8 pages and 7 figures
