基于环境几何的风场预测，让四旋翼无人机提前避风飞行更稳更快。
WESPR: Wind-adaptive Energy-Efficient Safe Perception & Planning for Robust Flight with Quadrotors
- 融合地形感知与气象数据，10秒内估算局部风场。
- 实测轨迹偏差降低35.6%，飞行稳定性提升24.6%。
- 适合复杂城市或障碍密集区的实时安全飞行任务。
局部风况显著影响无人机性能：逆风可延长续航，侧风和风切变则降低在复杂空间中的机动性，顺风则缩短航程。尽管自适应控制器能缓解湍流影响，但对生成风况的周围几何结构缺乏认知，难以主动规避。现有建模方法通常依赖计算量大的流体动力学模拟，限制了新环境与条件下的实时适应能力。为此，我们提出WESPR——一种快速框架，可预测环境几何对局部风况的影响，实现主动路径规划与控制策略调整。其轻量级流程整合几何感知与本地气象数据，实现风场估计、高效路径计算与控制适配，全程耗时不足10秒。我们在Crazyflie无人机上验证了该系统在湍流障碍赛道的表现，结果表明，相比无风感知的自适应控制器，最大轨迹偏差平均降低35.6%，稳定性提升24.6%。
原文摘要 · Abstract (English)
Local wind conditions strongly influence drone performance: headwinds increase flight time, crosswinds and wind shear hinder agility in cluttered spaces, while tailwinds reduce travel time. Although adaptive controllers can mitigate turbulence, they remain unaware of the surrounding geometry that generates it, preventing proactive avoidance. Existing methods that model how wind interacts with the environment typically rely on computationally expensive fluid dynamics simulations, limiting real-time adaptation to new environments and conditions. To bridge this gap, we present WESPR, a fast framework that predicts how environmental geometry affects local wind conditions, enabling proactive path planning and control adaptation. Our lightweight pipeline integrates geometric perception and local weather data to estimate wind fields, compute cost-efficient paths, and adjust control strategies, all within 10 seconds. We validate WESPR on a Crazyflie drone navigating turbulent obstacle courses. Our results show a 35.6% reduction on average in maximum trajectory deviation and a 24.6% improvement in stability compared to a wind-agnostic adaptive controller.
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