aiqbench analysis
This research applies online learning algorithms to optimize airflow in vertical wind tunnels, directly enhancing the flight performance of unmanned aerial vehicles through tailored aerodynamic profiles. By combining simplified physical models with iterative measurement-based techniques, the study demonstrates a practical convergence toward desired distributions for passive soaring robots. The approach highlights how adaptive machine learning can solve complex control problems in experimental robotics environments without requiring exhaustive data collection.
Shaping Wind-Tunnel Airflow for Unmanned Aerial Vehicles using Online Learning
arXiv:2608.03378v1 Announce Type: new 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.
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.
Robotics (cs.RO); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
arXiv:2608.03378 [cs.RO]
(or arXiv:2608.03378v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.03378 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 214 words
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