  Quadrotors are agile platforms. With human experts, they can perform
extremely high-speed flights in cluttered environments. However, fully
autonomous flight at high speed remains a significant challenge. In this work,
we propose a motion planning algorithm based on the corridor-constrained
minimum control effort trajectory optimization (MINCO) framework. Specifically,
we use a series of overlapping spheres to represent the free space of the
environment and propose two novel designs that enable the algorithm to plan
high-speed quadrotor trajectories in real-time. One is a sampling-based
corridor generation method that generates spheres with large overlapped areas
(hence overall corridor size) between two neighboring spheres. The second is a
Receding Horizon Corridors (RHC) strategy, where part of the previously
generated corridor is reused in each replan. Together, these two designs
enlarge the corridor spaces in accordance with the quadrotor's current state
and hence allow the quadrotor to maneuver at high speeds. We benchmark our
algorithm against other state-of-the-art planning methods to show its
superiority in simulation. Comprehensive ablation studies are also conducted to
show the necessity of the two designs. The proposed method is finally evaluated
on an autonomous LiDAR-navigated quadrotor UAV in woods environments, achieving
flight speeds over 13.7 m/s without any prior map of the environment or
external localization facility.
