Wind Estimation in Unmanned Aerial Vehicles with Causal Machine Learning
This work addresses the problem of reducing weight and maintaining functionality in UAVs for applications in aviation and robotics, but it appears incremental as it applies existing causal methods to a specific domain.
The paper tackled wind estimation in UAVs without specialized sensors by using only trajectory data and a causal machine learning approach, achieving the ability to design optimal trajectories in challenging weather conditions like constant wind, shear wind, and turbulence.
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.