FlowDrone: Wind Estimation and
Gust Rejection on UAVs using Fast-response Hot-wire Flow Sensors

David Snyder	Daphne Barretto
Anirudha Majumdar	Marcus Hultmark

Princeton University


[PDF]	[Video]

	In Proceedings: ICRA 2023.
	Hardware Build Guide.

This video, “Improving Drone Performance in Wind with Novel, Fast, Sensors” received an Outstanding Presentation Award ($1,500) at the 2023 Princeton Research Day.

Abstract: Unmanned aerial vehicles (UAVs) are finding use in applications that place increasing emphasis on robustness to external disturbances including extreme wind. However, traditional multirotor UAV platforms do not directly sense wind; conventional flow sensors are too slow, insensitive, or bulky for widespread integration on UAVs. Instead, drones typically observe the effects of wind indirectly through accumulated errors in position or trajectory tracking. In this work, we integrate a novel flow sensor based on micro-electro-mechanical systems (MEMS) hot-wire technology developed in our prior work onto a multirotor UAV for wind estimation. These sensors are omnidirectional, lightweight, fast, and accurate. In order to achieve superior tracking performance in windy conditions, we train a ‘wind-aware’ residual-based controller via reinforcement learning using simulated wind gusts and their aerodynamic effects on the drone. In extensive hardware experiments, we demonstrate the wind-aware controller outperforming two strong ‘wind-unaware’ baseline controllers in challenging windy conditions.

FlowDrone System Overview

FlowDrone uses MAST (MEMS Anemometry Sensing Tower), our novel omnidirectional flow sensor, to estimate a wind vector onboard the drone. MAST functions using micro-electro-mechanical (MEMS) hot-wires, which have a sensing bandwidth of 500+ Hz, significantly faster than conventional pressure-based or ultrasonic methods. The MAST voltages are read into the Raspberry Pi, and a neural network sensor model (trained on wind-tunnel data) estimates a corresponding wind direction and magnitude. A short time series of wind data, in addition to the drone's state (from the flight computer), is fed into a learning-based control policy. The output of the control policy is a body rate and thrust setpoint, which is sent back to the flight controller and determines how the drone should react to the incoming gust.

Wind-Aware Control

The wind-aware control policy is comprised of two parts: a standard attitude PID controller, and a residual policy that augments the attitude PID controller with learned gust rejection. This captures the baseline behavior of the attitude PID controller, while giving the policy an opportunity to learn and use additional information from the MAST flow sensors. The residual policy is learned in simulation using reinforcement learning, in a variety of simulated wind gusts. The residual policy takes as input a short history of wind estimates, and thus has the opportunity to learn wind structure and ultimately to predict the forces acting on the drone.

Results: Gust Rejection

We compare the performance of our wind-aware controller against two strong baselines. The first baseline (wind-unaware) has the opportunity to learn a residual policy in the same simulated gusts, but without the MAST flow sensor. Any performance improvements of wind-aware over wind-unaware validate the benefit of MAST. The second baseline (baseline) are simply the attitude PID controller, with no learned residual component. Any improvements over baseline validate the benefit of the learned residual policy.

Across 15x trials, we see that wind-aware (ours -- in blue) outperforms wind-unaware, which in turn outperforms baseline. We see that wind-aware both deviates the least during the gust (Left), and also maintains a tighter concentration around the position setpoint throughout the gust (Right). These experiments demonstrate the benefits of fast flow sensing for the purposes of flow prediction and gust rejection onboard UAVs.

Citing our Work:

If you find our work to be relevant to your research, please consider citing our pair of papers:

Fast-response hot-wire flow sensors for wind and gust estimation on UAVs

Nathaniel Simon, Alexander Piqué, David Snyder, and 3 more authors

Measurement Science and Technology 2022

Abs arXiv Bib PDF

Due to limitations in available sensor technology, unmanned aerial vehicles (UAVs) lack an active sensing capability to measure turbulence, gusts, or other unsteady aerodynamic phenomena. Conventional in situ anemometry techniques fail to deliver in the harsh and dynamic multirotor environment due to form factor, resolution, or robustness requirements. To address this capability gap, a novel, fast-response sensor system to measure a wind vector in two dimensions is introduced and evaluated. This system, known as ‘MAST’ (for MEMS Anemometry Sensing Tower), leverages advances in microelectromechanical (MEMS) hot-wire devices to produce a solid-state, lightweight, and robust flow sensor suitable for real-time wind estimation onboard a UAV. The MAST uses five pentagonally-arranged microscale hot-wires to determine the wind vector’s direction and magnitude. The MAST’s performance was evaluated in a wind tunnel at speeds up to 5 m/s and orientations of 0 - 360 degrees. A neural network sensor model was trained from the wind tunnel data to estimate the wind vector from sensor signals. The average error of the sensor is 0.14 m/s for speed and 1.6 degrees for direction. Furthermore, 95% of measurements are within 0.36 m/s error for speed and 5.0 degree error for direction. With a bandwidth of 570 Hz determined from square-wave testing, the MAST stands to greatly enhance UAV wind estimation capabilities and enable capturing relevant high-frequency phenomena in flow conditions.
@article{simon2022fast, title = {Fast-response hot-wire flow sensors for wind and gust estimation on UAVs}, author = {Simon, Nathaniel and Piqu{\'e}, Alexander and Snyder, David and Ikuma, Kyle and Majumdar, Anirudha and Hultmark, Marcus}, journal = {Measurement Science and Technology}, volume = {34}, number = {2}, pages = {025109}, year = {2022} }

FlowDrone: wind estimation and gust rejection on UAVs using fast-response hot-wire flow sensors

Nathaniel Simon, Allen Z Ren, Alexander Piqué, and 4 more authors

In International Conference on Robotics and Automation (ICRA) 2023

Abs arXiv Bib PDF Website video

Unmanned aerial vehicles (UAVs) are finding use in applications that place increasing emphasis on robustness to external disturbances including extreme wind. However, traditional multirotor UAV platforms do not directly sense wind; conventional flow sensors are too slow, insensitive, or bulky for widespread integration on UAVs. Instead, drones typically observe the effects of wind indirectly through accumulated errors in position or trajectory tracking. In this work, we integrate a novel flow sensor based on micro-electro-mechanical systems (MEMS) hot-wire technology developed in our prior work onto a multirotor UAV for wind estimation. These sensors are omnidirectional, lightweight, fast, and accurate. In order to achieve superior tracking performance in windy conditions, we train a ‘wind-aware’ residual-based controller via reinforcement learning using simulated wind gusts and their aerodynamic effects on the drone. In extensive hardware experiments, we demonstrate the wind-aware controller outperforming two strong ‘wind-unaware’ baseline controllers in challenging windy conditions.
@inproceedings{simon2023flowdrone, title = {FlowDrone: wind estimation and gust rejection on UAVs using fast-response hot-wire flow sensors}, author = {Simon, Nathaniel and Ren, Allen Z and Piqu{\'e}, Alexander and Snyder, David and Barretto, Daphne and Hultmark, Marcus and Majumdar, Anirudha}, booktitle = {International Conference on Robotics and Automation (ICRA)}, pages = {5393--5399}, year = {2023}, organization = {IEEE}, }

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