FlowDrone: Wind Estimation and Gust Rejection on UAVs Using Fast-Response Hot-Wire Flow Sensors

TL;DR

This paper introduces a novel MEMS hot-wire flow sensor integrated on UAVs for real-time wind estimation and demonstrates a reinforcement learning-based controller that significantly improves drone stability in windy conditions.

Contribution

It presents a lightweight, fast, and accurate flow sensor for UAVs and a wind-aware control strategy trained via reinforcement learning for better gust rejection.

Findings

The flow sensor accurately measures omnidirectional wind in real-time.

The wind-aware controller outperforms baseline controllers in challenging wind conditions.

Hardware experiments validate the effectiveness of the proposed system.

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. See: https://youtu.be/KWqkH9Z-338.