Multirotor Estimation of Wind

Project Information

MEW

Grant agreement ID: 101154194

DOI

10.3030/101154194

Project terminated on 17 October 2025

EC signature date 4 March 2024

Start date 17 June 2024

End date 16 June 2026

Funded under

• Marie Skłodowska-Curie Actions (MSCA)

Total cost

No data

EU contribution

€ 187 624,32

Coordinated by

UNIVERSITEIT TWENTE
Netherlands

Periodic Reporting for period 1 - MEW (Multirotor Estimation of Wind)

Reporting period: 2024-06-17 to 2026-06-16

Summary of the context and overall objectives of the project

The MEW (Multirotor Estimation of Wind) project addresses the measurement of unpredictable impact of wind forces on multirotor systems, which is a critical barrier to the widespread adoption of drones. Wind disturbances cause instability, degraded image quality, and safety concerns, limiting applications of drones in sectors such as infrastructure inspection, agriculture, delivery, and industrial monitoring. The EU drone market is projected to exceed €10 billion annually by 2035, making the improvement of reliability under challenging real-world conditions both economically and strategically important.
The project uses the following techniques to develop new wind estimation solutions:

1. A novel Acoustic Resonance Sensor (ARS) for direct wind velocity measurements.
2. An Equivariant Filter (EqF) for state estimation that takes advantage of the geometry inherent to multirotor vehicles.
3. Reduced Order Modelling (ROM) to decribe wind dynamics clearly and effectively for application.

The MEW project will help drones to operate safely and effectively in windy environments, unlocking new industrial applications and addressing societal concerns about drone safety.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

The response of an Acoustic Resonance Sensor (ARS) to various wind conditions was completed in the laboratory, and equations for an Equivariant Filter (EqF) were derived for the estimation of multirotor and wind states. Substantial custom software was built for the ARS to be able to interface with standard robotics software tools such as the Robotics Operating System (ROS). The response of the ARS was successfully and accurately modelled as an additive Gaussian process with time lag. An experimental setup was developed with a large fan to produce a known wind that could be measured by the ARS and compared to its reported wind speed. The model of the ARS response was extended to describe the sensor behaviour at different angles to the incoming wind. This was done by modifying the experimental setup to allow for two servo motors to change the angle between the sensor and the wind autonomously and continuously. Further software was also created to control these motors and record their states over time.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

This project proposed a novel random walk stochastic model for the Acoustic Resonance Sensor (ARS), which was validated in laboratory experiments with wind measured at a standard deviation of 0.28 m/s. This was achieved at a wind speed of 4.8 m/s, therefore the average prediction error was indeed less than 20% of the maximum wind speed. Additionally, the sensor lag was identified as 0.14 seconds. This model is a key outcome beyond the state of the art of wind estimation in aerial robotics. Potential impacts include significantly improved flight of multirotor systems in challenging high-wind conditions.