AutoTune：高速飛行のためのコントローラ自動チューニング
AutoTune: Controller Tuning for High-Speed Flight
高速飛行ではコントローラのパラメータ感度が高く手動調整が難しいため、事前知識を必要とせず多峰性の最適化空間に対応するサンプリングベースの自動チューニング手法AutoTuneを提案し、シミュレーションと実機で既存手法を最大90%上回る軌道完遂性能を示した。
著者: Antonio Loquercio, Alessandro Saviolo, Davide Scaramuzza
分類: cs.RO, cs.LG
原文アブストラクト
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.
