  Due to noisy actuation and external disturbances, tuning controllers for
high-speed flight is very challenging. In this paper, we ask the following
questions: How sensitive are controllers to tuning when tracking high-speed
maneuvers? What algorithms can we use to automatically tune them? To answer the
first question, we study the relationship between parameters and performance
and find out that the faster the maneuver, the more sensitive a controller
becomes to its parameters. To answer the second question, we review existing
methods for controller tuning and discover that prior works often perform
poorly on the task of high-speed flight. Therefore, we propose AutoTune, a
sampling-based tuning algorithm specifically tailored to high-speed flight. In
contrast to previous work, our algorithm does not assume any prior knowledge of
the drone or its optimization function and can deal with the multi-modal
characteristics of the parameters' optimization space. We thoroughly evaluate
AutoTune both in simulation and in the physical world. In our experiments, we
outperform existing tuning algorithms by up to 90% in trajectory completion.
The resulting controllers are tested in the AirSim Game of Drones competition,
where we outperform the winner by up to 25% in lap-time. Finally, we show that
AutoTune improves tracking error when flying a physical platform with respect
to parameters tuned by a human expert.
