From Large-Scale Winds to Urban Decision Making: A Cross-Scale Framework for Wind-Aware UAV Navigation

Shaoxiang Qin, Fuyuan Lyu, Di Zhou, Xue Liu, Xiongye Xiao, Anima Anandkumar, Liangzhu Wang

Keywords: Fourier neural operators, AI for PDE, UAV path planning, Trajectory optimization

TL;DR: Our framework bridges regional atmospheric models with city-scale wind fields via 3D neural operators, enabling wind-aware and energy-optimal UAV path planning in urban environments.

Abstract: Large-scale weather and climate models provide reliable wind information at regional scales, yet their outputs are typically too coarse for direct UAV decision making in geometrically complex urban environments. This paper investigates how large-scale atmospheric information can be transformed into city-scale wind representations and utilized for downstream navigation decisions. We propose a cross-scale prediction and decision framework that takes background wind conditions from existing weather or climate models and combines them with detailed 3D urban geometry to predict time-averaged urban wind fields using a 3D neural operator. The predicted wind fields are then incorporated into a wind-aware UAV trajectory optimization problem to minimize energy consumption under kinematic feasibility and safety constraints. By comparing trajectories planned against a wind-agnostic baseline, we demonstrate significant efficiency gains enabled by AI-predicted wind, specifically 10.3% savings in tailwinds, 7.7% in headwinds, and 3.9% in crosswind conditions. These results indicate that learning decision-relevant urban wind representations offers a practical pathway for bridging large-scale atmospheric information and fine-scale urban decision making.

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Submission Number: 112