WindSeer：小型UAV上で複雑地形上のリアルタイム volumetric 風予測
WindSeer: Real-time volumetric wind prediction over complex terrain aboard a small UAV
CFDシミュレーションの合成データのみで学習したニューラルネットワークにより、限られた計算資源の小型UAV上で、少数の風計測から複雑地形上の高解像度風場をリアルタイム予測する手法を実現した。
著者: Florian Achermann, Thomas Stastny, Bogdan Danciu, Andrey Kolobov, Jen Jen Chung, Roland Siegwart, Nicholas Lawrance
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
Real-time high-resolution wind predictions are beneficial for various applications including safe manned and unmanned aviation. Current weather models require too much compute and lack the necessary predictive capabilities as they are valid only at the scale of multiple kilometers and hours - much lower spatial and temporal resolutions than these applications require. Our work, for the first time, demonstrates the ability to predict low-altitude wind in real-time on limited-compute devices, from only sparse measurement data. We train a neural network, WindSeer, using only synthetic data from computational fluid dynamics simulations and show that it can successfully predict real wind fields over terrain with known topography from just a few noisy and spatially clustered wind measurements. WindSeer can generate accurate predictions at different resolutions and domain sizes on previously unseen topography without retraining. We demonstrate that the model successfully predicts historical wind data collected by weather stations and wind measured onboard drones.
