Time-minimum trajectories through race tracks are determined by the drone's capability as well as the configuration of all gates (e.g., their shapes, sizes, and orientations). However, prior works neglect the impact of the gate configuration and formulate drone racing as a waypoint flight task, leading to conservative waypoint selection through each gate. We present a novel time-optimal planner that can account for gate constraints explicitly, enabling quadrotors to follow the most time-efficient waypoints at their single-rotor-thrust limits in tracks with hybrid gate types. Our approach provides comparable solution quality to the state-of-the-art but with a computation time orders of magnitude faster. Furthermore, the proposed framework allows users to customize gate constraints such as tunnels by concatenating existing gate classes, enabling high-fidelity race track modeling. Owing to the superior computation efficiency and flexibility, we can generate optimal racing trajectories for complex race tracks with tens or even hundreds of gates with distinct shapes. We validate our method in real-world flights and demonstrate that faster lap times can be produced by using gate constraints instead of waypoint constraints.

翻译：穿越赛道的耗时最短轨迹取决于无人机的性能以及所有门（如形状、尺寸和朝向）的配置。然而，先前的研究忽略了门配置的影响，将无人机竞速简化为航点飞行任务，导致通过每道门时采用保守的航点选择。我们提出了一种新颖的时间最优规划器，能够显式地考虑门约束，使四旋翼飞行器在混合门类型的赛道中，能够在单旋翼推力极限下沿最省时的航点飞行。我们的方法提供了与现有技术相当的求解质量，但计算速度快了数个数量级。此外，所提出的框架允许用户通过串联现有门类来自定义门约束（如隧道），实现高保真赛道建模。凭借卓越的计算效率和灵活性，我们能够为包含数十甚至上百个不同形状门的复杂赛道生成最优竞速轨迹。我们在真实世界飞行中验证了该方法，并证明使用门约束而非航点约束可以产生更快的单圈时间。