Summary of Windseer: Real-time Volumetric Wind Prediction Over Complex Terrain Aboard a Small Uav, by Florian Achermann et al.
WindSeer: Real-time volumetric wind prediction over complex terrain aboard a small UAV
by Florian Achermann, Thomas Stastny, Bogdan Danciu, Andrey Kolobov, Jen Jen Chung, Roland Siegwart, Nicholas Lawrance
First submitted to arxiv on: 18 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed work demonstrates the ability to predict low-altitude wind in real-time on limited-compute devices, from only sparse measurement data. The trained neural network, WindSeer, uses synthetic data from computational fluid dynamics simulations and successfully predicts real wind fields over terrain with known topography from just a few noisy and spatially clustered wind measurements. This model can generate accurate predictions at different resolutions and domain sizes on previously unseen topography without retraining. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Real-time high-resolution wind predictions are important for safe manned and unmanned aviation. Current weather models aren’t suitable because they require too much compute power and lack the necessary predictive capabilities. They’re only valid at a scale of multiple kilometers and hours, which is much lower than what’s needed. This new work shows how to predict low-altitude wind in real-time on devices with limited computing power, using only sparse measurement data. |
Keywords
* Artificial intelligence * Neural network * Synthetic data
