Fast-Racing: An Open-Source Strong Baseline for $\mathrm{SE}(3)$ Planning in Autonomous Drone Racing
IEEE Robotics and Automation Letters(2021)
摘要
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
$\mathrm{SE}(3)$
, whose non-triviality and complexity hindering a convincing solution in our community by now. To bridge this gap, this letter proposes an open-source baseline, which includes a high-performance
$\mathrm{SE}(3)$
planner and a challenging simulation platform tailored for drone racing. We specify the
$\mathrm{SE}(3)$
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
$\mathrm{SE}(3)$
. 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
$\mathrm{SE}(3)$
planning and the competition of autonomous drone racing.
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关键词
Motion and path planning,performance evaluation and benchmarking,optimization and optimal control
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