Fast-response hot-wire flow sensors for wind and gust estimation on UAVs
ORAL
Abstract
Unsteady airflow phenomena such as wind gusts present a control challenge for Unmanned Aerial Vehicle (UAV) operations. The prevailing control strategy is to apply corrective action after setpoint error is detected. A proactive approach, enabling faster and more precise control, requires measuring the wind directly with onboard flow sensors. Existing anemometry techniques are unsuitable due to form factor, resolution, or robustness requirements. To overcome this, a novel, fast-response sensor to measure a wind vector in two dimensions is introduced. This sensor, named 'MAST' (MEMS Anemometry Sensing Tower), leverages advances in microelectromechanical (MEMS) hot-wire devices to produce a solid-state, lightweight, and robust flow sensor suitable for real-time wind estimation onboard a UAV. The MAST is a modular system that supports multiple configurations, which were evaluated in the wind tunnel. A neural network sensor model was trained to predict the direction (0-360 degrees) and magnitude (0-5 m/s) of the wind. Additionally, the bandwidth of the MAST system is sufficiently high to capture the relevant atmospheric phenomena. The resulting system stands to greatly enhance UAV wind estimation capabilities.
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Presenters
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Nathaniel Simon
Princeton University
Authors
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Nathaniel Simon
Princeton University
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Alexander Pique
Princeton University
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David Snyder
Princeton University
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Kyle Ikuma
Princeton University
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Anirudha Majumdar
Princeton University
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Marcus Hultmark
Princeton University