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WESPR is introduced as a framework for quadrotors to predict wind conditions based on environmental geometry and local weather data, enabling proactive path planning and control adaptation. The system integrates geometric perception with weather data to estimate wind fields and compute cost-efficient paths in real-time. Experimental validation on a Crazyflie drone in turbulent obstacle courses demonstrates a 12.5-58.7% reduction in trajectory deviation and a 24.6% improvement in stability compared to wind-agnostic control.
Drones can now proactively navigate turbulent environments thanks to a fast wind-prediction framework that integrates geometric perception and local weather data.
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.
