面向自主无人机竞速的时间最优过门规划器
Time-Optimal Gate-Traversing Planner for Autonomous Drone Racing
AI总结：
针对无人机竞速中忽略门配置导致路径保守的问题，提出一种时间最优过门规划器，在单旋翼推力限制下建模不同门约束，以秒级计算生成更省时轨迹，并通过仿真与真实飞行验证其可进一步缩短单圈时间。
摘要：
在无人机竞速中，时间最短轨迹受到无人机能力、赛道布局以及门配置（例如其形状和尺寸）的影响。然而，以往研究忽略了门的配置，仅将无人机竞速简化为航点通过任务。这种建模方式往往导致穿越门时选择保守路径，因为门的空间潜力未被充分利用。为解决这一问题，我们提出了一种时间最优规划器，能够忠实建模具有不同配置的门约束，从而在考虑单旋翼推力限制的同时生成更省时的轨迹。我们的方法在计算效率上表现出色，仅需几秒钟即可计算出无人机通过包含数十个不同门的赛道时的完整状态和控制轨迹。大量仿真与实验证实了所提方法的有效性，表明考虑门的配置可以进一步缩短单圈时间。我们在真实飞行中验证了该规划器，并展示了穿越赛道的超极限飞行轨迹。
英文摘要：
In drone racing, the time-minimum trajectory is affected by the drone's capabilities, the layout of the race track, and the configurations of the gates (e.g., their shapes and sizes). However, previous studies neglect the configuration of the gates, simply rendering drone racing a waypoint-passing task. This formulation often leads to a conservative choice of paths through the gates, as the spatial potential of the gates is not fully utilized. To address this issue, we present a time-optimal planner that can faithfully model gate constraints with various configurations and thereby generate a more time-efficient trajectory while considering the single-rotor-thrust limits. Our approach excels in computational efficiency which only takes a few seconds to compute the full state and control trajectories of the drone through tracks with dozens of different gates. Extensive simulations and experiments confirm the effectiveness of the proposed methodology, showing that the lap time can be further reduced by taking into account the gate's configuration. We validate our planner in real-world flights and demonstrate super-extreme flight trajectory through race tracks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
