  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.
