Wind Estimation in Unmanned Aerial Vehicles with Causal Machine Learning

TL;DR

This paper presents a causal machine learning method to estimate wind conditions affecting UAVs solely from their trajectory data, eliminating the need for specialized sensors and enhancing flight safety in various weather scenarios.

Contribution

It introduces a novel causal curiosity approach combining time series classification and clustering for wind estimation without additional sensors.

Findings

Effective wind environment classification across three scenarios

Optimized UAV maneuvers improve wind estimation accuracy

Potential for sensorless wind-aware UAV trajectory planning

Abstract

In this work we demonstrate the possibility of estimating the wind environment of a UAV without specialised sensors, using only the UAV's trajectory, applying a causal machine learning approach. We implement the causal curiosity method which combines machine learning times series classification and clustering with a causal framework. We analyse three distinct wind environments: constant wind, shear wind, and turbulence, and explore different optimisation strategies for optimal UAV manoeuvres to estimate the wind conditions. The proposed approach can be used to design optimal trajectories in challenging weather conditions, and to avoid specialised sensors that add to the UAV's weight and compromise its functionality.