Fast-Racing: 自律ドローンレースのためのSE(3)プランニングのオープンソース強力ベースライン
Fast-Racing: An Open-source Strong Baseline for SE(3) Planning in Autonomous Drone Racing
ドローンレース向けにSE(3)軌道生成をソフトペナルティ最適化として定式化し並列構造で高速化したプランナと、実環境を模した挑戦的なシミュレーション環境をオープンソースで提供した論文。
著者: Zhichao Han, Zhepei Wang, Neng Pan, Yi Lin, Chao Xu, Fei Gao
分類: cs.RO
原文アブストラクト
With the autonomy of aerial robots advances in recent years, autonomous drone racing has drawn increasing attention. In a professional pilot competition, a skilled operator always controls the drone to agilely avoid obstacles in aggressive attitudes, for reaching the destination as fast as possible. Autonomous flight like elite pilots requires planning in SE(3), whose non-triviality and complexity hindering a convincing solution in our community by now. To bridge this gap, this paper proposes an open-source baseline, which includes a high-performance SE(3) planner and a challenging simulation platform tailored for drone racing. We specify the SE(3) trajectory generation as a soft-penalty optimization problem, and speed up the solving process utilizing its underlying parallel structure. Moreover, to provide a testbed for challenging the planner, we develop delicate drone racing tracks which mimic real-world set-up and necessities planning in SE(3). Besides, we provide necessary system components such as common map interfaces and a baseline controller, to make our work plug-in-and-use. With our baseline, we hope to future foster the research of SE(3) planning and the competition of autonomous drone racing.
