发表机构
University of Tennessee, Knoxville; Concordia University; McGill University; MBZUAI; California Institute of Technology（田纳西大学诺克斯维尔分校; 康考迪亚大学; 麦吉尔大学; 穆罕默德·本·扎耶德人工智能大学; 加州理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
GeoWind2Plan提出几何到风到规划的框架，利用局部化神经算子快速预测三维城市风场，并基于物理能量模型优化无人机路径，在约3秒内实现节能规划，相比不考虑风的规划降低能耗6.9%-12.7%。
AI 中文摘要
在城市低空飞行中，建筑物将环境风重塑为空间变化的三维流场，使得无人机的能量消耗不仅取决于路径长度，还取决于局部风暴露情况。然而，在规划任务时，建筑物解析的风信息很少可用。计算流体动力学（CFD）可以生成高保真的城市流场，但每次模拟都绑定于固定的入流边界条件，且可能耗时数小时至数天，这与通常持续几分钟至几十分钟的城市无人机任务不相容。我们提出GeoWind2Plan，一个从几何到风再到规划的框架，用于任务时间的三维城市风预测和节能无人机规划。仅给定背景风矢量、三维建筑物几何以及起点-终点对，GeoWind2Plan将建筑物几何变换到参考风坐标系，使用局部化的几何条件神经算子预测与任务相关的三维风补丁，将其拼接成可查询的局部风场，并使用基于物理的无人机能量模型优化可行的三维路径和速度剖面。GeoWind2Plan并不追求CFD级别的完美重建，而是针对决策有用的风预测：轨迹使用预测的风进行规划，并在高保真CFD风下进行评估。在保留的城市域、风速和任务风向角体制中，GeoWind2Plan在约3秒内完成走廊局部风推断，而CFD大约需要8小时。在CFD评估下，相对于不考虑风的规划，使用GeoWind2Plan规划的轨迹在顺风、逆风和侧风任务中分别减少能量6.9%、12.7%和4.5%，恢复了CFD参考节省的87.9%、85.7%和75.0%。这些结果表明，快速、走廊局部的三维城市风预测可以使风感知的无人机能量规划在任务时间变得实用。
英文摘要
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.
CommentsAccepted at NeurIPS 2026 (Spotlight). 31 pages. Code and dataset: https://github.com/DUAL-Xiao/GeoWind2Plan/
