Planning High-speed Smooth Quadrotor Trajectories In Unknown Environments
Mr. Ren Yunfan
PhD candidate in the Mechanical Engineering Dept.
Date & Time
Thursday, 28 April 2022
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 13.7m/s without any prior map of the environment or external localization facility.
Robotics and Control