WESPR: Wind-adaptive Energy-Efficient Safe Perception & Planning for Robust Flight with Quadrotors

TL;DR

WESPR is a rapid framework that predicts environmental wind effects using geometric perception and weather data, enabling proactive drone navigation and control adaptation in turbulent conditions, demonstrated on Crazyflie drones.

Contribution

We introduce WESPR, a lightweight, real-time system that estimates wind fields from environment geometry and weather data for improved drone flight stability.

Findings

Significant reduction in trajectory deviation (12.5-58.7%)

Enhanced stability (24.6%) over wind-agnostic controllers

Real-time wind prediction within 10 seconds

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.