GeoWind2Plan: Mission-Time 3D Urban Wind Prediction for Energy-Efficient UAV Planning
Overview
This paper introduces GeoWind2Plan, a framework that predicts 3D urban wind fields in real time to enable energy-efficient unmanned aerial vehicle (UAV) path planning. By transforming building geometry into a reference-wind frame and using a localized geometry-conditioned neural operator, the system stitches together mission-relevant wind patches within about 3 seconds. When evaluated against high-fidelity computational fluid dynamics (CFD) simulations, trajectories planned with GeoWind2Plan reduce energy consumption by 6.9%, 12.7%, and 4.5% in tailwind, headwind, and crosswind missions respectively, recovering 87.9%, 85.7%, and 75.0% of the savings achieved by CFD-based planning.
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“ing, recovering 87.9%, 85.7%, and 75.0% of CFD-reference savings. These results show that fast, corridor-localized 3D urban wind prediction can make wind-aware UAV energy planning practical at mission time. Code/dataset: https://github.com/DUAL-Xiao/GeoWind2Plan/ . Demo: https://geowind2plan.com .”
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- arXiv Robotics — research abstractsSignalGeoWind2Plan: Mission-Time 3D Urban Wind Prediction for Energy-Efficient UAV Planning
This paper introduces GeoWind2Plan, a framework that predicts 3D urban wind fields in real time to enable energy-efficient unmanned aerial vehicle (UAV) path planning. By transforming building geometry into a reference-wind frame and using a localized geometry-conditioned neural operator, the system stitches together mission-relevant wind patches within about 3 seconds. When evaluated against high-fidelity computational fluid dynamics (CFD) simulations, trajectories planned with GeoWind2Plan reduce energy consumption by 6.9%, 12.7%, and 4.5% in tailwind, headwind, and crosswind missions respectively, recovering 87.9%, 85.7%, and 75.0% of the savings achieved by CFD-based planning.
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