# Wind Estimation in Unmanned Aerial Vehicles with Causal Machine Learning

arXiv: https://arxiv.org/abs/2407.01154 · HTML: https://mlsanity.com/paper/2407.01154/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 43 references that are in the map.
- First posted on arXiv 1 Jul 2024.

## Builds on (most cited first, 12 of 12)

- [Curiosity-driven Exploration by Self-supervised Prediction](https://mlsanity.com/paper/1705.05363/) (2017)
- [Large-Scale Study of Curiosity-Driven Learning](https://mlsanity.com/paper/1808.04355/) (2018)
- [Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning](https://mlsanity.com/paper/1509.08731/) (2015)
- [Causal Machine Learning: A Survey and Open Problems](https://mlsanity.com/paper/2206.15475/) (2022)
- [Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning](https://mlsanity.com/paper/1708.02190/) (2017)
- [Active Learning of Inverse Models with Intrinsically Motivated Goal Exploration in Robots](https://mlsanity.com/paper/1301.4862/) (2013)
- [Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning](https://mlsanity.com/paper/2010.03110/) (2020)
- [A Survey on Causal Reinforcement Learning](https://mlsanity.com/paper/2302.05209/) (2023)
- [Curiosity Driven Exploration of Learned Disentangled Goal Spaces](https://mlsanity.com/paper/1807.01521/) (2018)
- [Causal Multi-Agent Reinforcement Learning: Review and Open Problems](https://mlsanity.com/paper/2111.06721/) (2021)
- [Computational Theories of Curiosity-Driven Learning](https://mlsanity.com/paper/1802.10546/) (2018)
- [Autonomous Robotic Arm Manipulation for Planetary Missions using Causal Machine Learning](https://mlsanity.com/paper/2403.00470/) (2024)

## Abstract (arXiv)

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.