  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.
