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

TL;DR

WESPR, a fast framework that predicts how environmental geometry affects local wind conditions, enabling proactive path planning and control adaptation is presented, and a 12.5-58.7% reduction in maximum trajectory deviation and a 24.6% improvement in stability are shown.

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.

Figures (9)

• Figure 1: A UAV navigating a turbulent environment. The red path shows the shortest route but crosses stronger winds; the green path follows a wind-aware trajectory through calmer regions.
• Figure 2: The WESPR path generation pipeline as described in Section \ref{['sec:method']}.
• Figure 3: Experimental pipeline. We capture an image of the environment's physical space and the positions of the fans, obstacles, and drone's start and goal (top left). We convert the image to an occupancy grid with fan positions (1s), and use that for our simulation (4s). Finally, we compute the drone's path (5s) and, while the drone flies, localizes it (bottom).
• Figure 4: We estimate the state of the Crazyflie drone by fusing odometry from its onboard Flow V2 sensor and position data from the overhead camera via HSV tracking.
• Figure 5: 3D illustration of our three environments with the drone at the start and the red circle as the goal. The red fan represents high-speed, while the white fan represents low-speed. The Base path is shown as a red line, while the WESPR path is green. The velocities shown in each environment are relative values, not absolute.