Real Time Local Wind Inference for Robust Autonomous Navigation

2026-03-31

AI Summary

This study addresses the challenge of degraded navigation robustness and energy efficiency in aerial robots operating in dense urban environments due to the lack of real-time wind field awareness. To overcome this limitation, the authors propose a novel paradigm for real-time local wind field inference that requires no prior environmental information. By fusing onboard LiDAR ranging data with sparse in-situ wind speed measurements, they develop a data-driven model for local wind prediction and, for the first time, integrate it into a receding horizon optimal controller to jointly optimize collision avoidance and energy efficiency. Combining deep learning, fluid dynamics, and optimal control, the method achieves real-time performance on embedded hardware. Simulations demonstrate significantly reduced collision rates and energy consumption, while wind tunnel experiments validate the algorithm’s feasibility and effectiveness on physical platforms.

Abstract

This thesis presents a solution that enables aerial robots to reason about surrounding wind flow fields in real time using on board sensors and embedded flight hardware. The core novelty of this research is the fusion of range measurements with sparse in situ wind measurements to predict surrounding flow fields. We aim to address two fundamental questions: first, the sufficiency of topographical data for accurate wind prediction in dense urban environments; and second, the utility of learned wind models for motion planning with an emphasis on energy efficiency and obstacle avoidance. Drawing on tools from deep learning, fluid mechanics, and optimal control, we establish a framework for local wind prediction using navigational LiDAR, and then incorporate local wind model priors into a receding-horizon optimal controller to study how local wind knowledge affects energy use and robustness during autonomous navigation. Through simulated demonstrations in diverse urban wind scenarios we evaluate the predictive capabilities of the wind predictor, and quantify improvements to autonomous urban navigation in terms of crash rates and energy consumption when local wind information is integrated into the motion planning. Sub-scale free flight experiments in an open-air wind tunnel demonstrate that these algorithms can run in real time on an embedded flight computer with sufficient bandwidth for stable control of a small aerial robot. Philosophically, this thesis contributes a new paradigm for localized wind inference and motion planning in unknown windy environments. By enabling robots to rapidly assess local wind conditions without prior environmental knowledge, this research accelerates the introduction of aerial robots into increasingly challenging environments.

Problem

• wind inference
• autonomous navigation
• urban environments
• aerial robots
• real-time perception

Innovation

• local wind inference
• sensor fusion
• deep learning
• receding-horizon control
• autonomous navigation