AutoTune: Controller Tuning for High-Speed Flight

TL;DR

AutoTune is a novel sampling-based controller tuning algorithm designed for high-speed flight, demonstrating significant improvements over existing methods in simulation, physical tests, and competitive drone racing scenarios.

Contribution

We introduce AutoTune, a new automatic controller tuning algorithm that handles multi-modal parameter spaces without prior knowledge, specifically optimized for high-speed drone flight.

Findings

AutoTune outperforms existing tuning algorithms by up to 90% in trajectory completion.

AutoTune reduces lap times by up to 25% in drone racing.

AutoTune improves tracking error compared to human-tuned parameters.

Abstract

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.