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.
翻译:四方是灵活的平台。 有了人类专家, 他们可以在杂乱的环境中执行极高速的飞行。 但是, 高速完全自主的飞行仍是一个重大挑战。 在这项工作中, 我们提出一个基于走廊限制的最低控制努力轨迹优化( MINCO) 框架的运动规划算法。 具体地说, 我们使用一系列重叠的域以代表环境的自由空间, 并提议两个新的设计, 使算法能够实时地规划高速的二次轨迹。 一个是以抽样为基础的走廊生成方法, 在两个相邻的空域之间产生有大面积重叠的区域( 整个走廊大小 ) 。 第二个是后退地平线走廊( RHC) 战略, 在每个重新规划中重新使用先前产生的走廊的一部分。 这两项设计共同根据 quadrotor 的当前状态扩大走廊空间, 从而允许二次轨迹以高速进行操作。 我们用其他状态的测算法来测定我们的算法, 在模拟中显示其优越性区域( 范围是整个走廊大小 ) 。 第二个是后退地走廊走廊( LAV ) 的外部环境最终评估了 13 。