用机器学习快速预测城市风场，实现安全飞行路径规划
Wind-Informed Rapid Flight-Planning in Complex Urban Topologies via Machine Learning and Experimental Validation
- 基于建筑结构和风向数据训练代理模型，快速预测复杂风场
- 通过风场信息优化路径，使飞行器位移减少、稳定性提升
- 首次在实验中验证了城市空中交通的风感知飞行方案
先进空中交通有望拓展人口密集区的人与货物运输。然而，风与建筑环境相互作用带来的飞行风险仍是城市空域中的重大挑战。本文提出一种新型框架，实现风环境下空中载具的安全飞行规划。利用机器学习构建代理模型，基于建筑几何和入射风信息快速预测流场；再结合关键流参数与建筑物距离，生成三维飞行挑战标量场；最后通过成本最小化路径搜索算法确定安全飞行轨迹。整个系统通过微型飞行器在大型风扇阵列风洞中的城市模型实验进行了验证。相比无风场信息的路径规划，该方法显著降低飞行器非预期位移，提升飞行稳定性。本工作是首个在真实环境中演示的城市空中交通风感知安全飞行方案。
原文摘要 · Abstract (English)
Advanced air mobility operations hold the potential to enhance and expand regional transportation of both people and goods in populated areas. However, hazardous flight conditions arising from interactions between wind and the built environment remain a significant challenge for aerial vehicles in urban settings. This work proposes a novel framework towards safe flight planning of aerial vehicles in windy urban environments. A learning-based surrogate model is trained to rapidly predict flow fields from readily available information such as building geometry and incident wind. This surrogate prediction is used to calculate a volumetric flight challenge scalar field based on critical flow parameters and proximity to structures. A safe, flow-informed flight trajectory is then identified through a cost-minimizing pathfinder. The complete system is demonstrated experimentally through flight tests of a micro aerial vehicle through a model urban geometry placed in a large fan-array wind tunnel. Comparing this approach to trajectories generated without knowledge of the wind field, we find the flow-informed approach reduces undesired vehicle displacement and improves flight stability. This work is among the first practical demonstrations of safe, wind-aware methodologies for advanced air mobility in urban environments.
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