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

2026-03-10

AI Summary

This study addresses the challenge of real-time wind field perception and predictive trajectory planning in complex environments, where local airflow is significantly perturbed by surrounding geometry. To this end, we propose WESPR—a lightweight, real-time framework that jointly models environmental geometry and local wind dynamics. By integrating geometric awareness with meteorological data, WESPR rapidly predicts wind-induced disturbances and co-optimizes energy-efficient, safety-aware flight trajectories alongside adaptive control policies. Experimental validation on a Crazyflie quadrotor demonstrates substantial improvements over wind-agnostic adaptive controllers: trajectory deviations are reduced by 12.5%–58.7%, and flight stability increases by 24.6%.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• wind-adaptive
• quadrotor
• environmental geometry
• real-time wind prediction
• safe flight

Innovation

• wind-adaptive planning
• real-time wind field estimation
• geometric perception
• energy-efficient trajectory optimization
• quadrotor robust control