GeoWind2Plan: Mission-Time 3D Urban Wind Prediction for Energy-Efficient UAV Planning

2026-09-28

AI Summary

This study addresses the prohibitive computational cost of urban low-altitude CFD wind field simulations, which impedes real-time energy optimization for UAVs, by proposing a “Geometry–Wind–Planning” framework. It introduces a corridor-localized inference paradigm that focuses on decision-relevant regions rather than full-scene reconstruction. A geometry-conditioned neural operator rapidly predicts local 3D wind fields for seamless stitching, while a physics-based energy model jointly optimizes path and velocity. Inference requires only three seconds—achieving a four-order-of-magnitude speedup over CFD—and yields energy savings of 6.9%, 12.7%, and 4.5% in tailwind, headwind, and crosswind scenarios, respectively, recovering most of the theoretical optimal gains.

Abstract

In urban low-altitude flight, buildings reshape ambient wind into spatially varying 3D flow, making unmanned aerial vehicle (UAV) energy depend on local wind exposure as well as path length. However, building-resolved wind information is rarely available when a mission must be planned. Computational fluid dynamics (CFD) can produce high-fidelity urban flow fields, but each simulation is tied to a fixed inflow boundary condition and can take hours to days, which is incompatible with urban UAV missions that typically last minutes to tens of minutes. We present GeoWind2Plan, a geometry-to-wind-to-planning framework for mission-time 3D urban wind prediction and energy-efficient UAV planning. Given only a background wind vector, 3D building geometry, and a start-goal pair, GeoWind2Plan transforms the building geometry into a reference-wind frame, predicts mission-relevant 3D wind patches with a localized geometry-conditioned neural operator, stitches them into a queryable local wind field, and optimizes a feasible 3D path and speed profile using a physically grounded UAV energy model. Rather than pursuing CFD-perfect reconstruction, GeoWind2Plan targets decision-useful wind prediction: trajectories are planned with predicted wind and evaluated under high-fidelity CFD wind. Across held-out urban domains, wind speeds, and mission wind-angle regimes, GeoWind2Plan performs corridor-localized wind inference in about 3 seconds, compared with roughly 8 hours for CFD. Under CFD evaluation, trajectories planned with GeoWind2Plan reduce energy by 6.9%, 12.7%, and 4.5% in tailwind, headwind, and crosswind missions relative to wind-agnostic planning, 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.

Problem

• UAV energy-efficient planning
• 3D urban wind prediction
• building-resolved wind field
• mission-time computation

Innovation

• neural operator
• 3D wind prediction
• UAV path planning
• energy-efficient routing
• urban aerodynamics