Abstract
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 12.5-58.7% reduction in maximum trajectory deviation and a 24.6% improvement in stability compared to a wind-agnostic adaptive controller.
Abstract (translated)
本地风况强烈影响无人机性能：逆风会增加飞行时间，侧风和风切变则在复杂环境中妨碍灵活性，而顺风则缩短了旅行时间。尽管自适应控制器可以减轻湍流的影响，但它们无法感知生成湍流的周围几何结构，从而无法进行主动避让。目前，模拟风与环境相互作用的方法通常依赖于计算成本高昂的流体动力学仿真，这限制了实时适应新环境和条件的能力。为填补这一空白，我们推出了WESPR（Wind Environment Simulation and Prediction for Robotics），这是一个快速框架，用于预测环境几何结构如何影响局部风况，从而实现主动路径规划和控制策略调整。 我们的轻量级流程结合了几何感知和本地气象数据来估算风场、计算成本效益的路径，并调整控制策略——所有这些都在10秒内完成。我们在Crazyflie无人机导航湍流障碍赛道上验证了WESPR的效果。结果表明，与没有考虑风况的自适应控制器相比，使用WESPR后轨迹最大偏差减少了12.5%至58.7%，稳定性提高了24.6%。
URL
https://arxiv.org/abs/2603.09194
