GeoWind2Plan: Mission-Time 3D Urban Wind Prediction for Energy-Efficient UAV Planning
GeoWind2Plan: 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.
The authors present GeoWind2Plan, a neural operator framework that predicts mission-time 3D urban wind fields in approximately 3 seconds given building geometry and background wind, achieving energy reductions of 6.9% to 12.7% relative to wind-agnostic planning when evaluated against high-fidelity CFD references.
Source: arXiv Robotics — research abstracts · Read original article ↗
Loading article text…
What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- code: Available Artifact link Open source S6
- paper: not_reported
- dataset: not_reported
- weights: not_reported
- code: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
imulation 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 trans
Open source S3
th 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 s, compared with roughly 8 h 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 plann
Open source S5
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 .
Open source S6
Source：arXiv Robotics — research abstracts · arxiv.org