张宁 Efficient Trajectory Planning for High Speed Flight in Unknown Environments
高效飞行在未知环境中的有效轨迹规划
链接：https://pan.baidu.com/s/1l0HtSOU-6QSojq7ELrmLIA 提取码：ayc1
Markus Ryll, John Ware, John Carter and Nick Roy
There has been considerable recent work in motion planning for UAVs to enable aggressive, highly dynamic flight in known environments with motion capture systems. However, these existing planners have not been shown to enable the same kind of flight in unknown, outdoor environments. In this paper we present a receding horizon planning architecture that enables the fast replanning necessary for reactive obstacle avoidance by combining three techniques. First, we show how previous work in computationally efficient, closed-form trajectory generation method can be coupled with spatial partitioning data structures to reason about the geometry of the environment in real-time. Second, we show how to maintain safety margins during fast flight in unknown environments by planning velocities according to obstacle density. Third, our recedinghorizon, sampling-based motion planner uses minimum-jerk trajectories and closed-loop tracking to enable smooth, robust, high-speed flight with the low angular rates necessary for accurate visual-inertial navigation. We compare against two state-of-the-art, reactive motion planners in simulation and benchmark solution quality against an offline global planner. Finally, we demonstrate our planner over 80 flights with a combined distance of 22km of autonomous quadrotor flights in an urban environment at speeds up to 9.4ms-1.
最近在无人机的运动规划方面进行了大量工作，以便在具有运动捕捉系统的已知环境中实现积极，高度动态的飞行。然而，这些现有的规划者尚未被证明能够在未知的室外环境中实现同样的飞行。在本文中，我们提出了一种后退的地平线规划架构，通过结合三种技术，可以实现反应性避障所需的快速重新规划。首先，我们展示了先前在计算上有效的闭合轨迹生成方法中的工作如何与空间划分数据结构相结合，以实时推理环境的几何形状。其次，我们展示了如何通过根据障碍物密度规划速度来在未知环境中快速飞行期间保持安全裕度。第三，我们的后退运动，基于采样的运动规划器使用最小冲击轨迹和闭环跟踪，以实现平稳，稳健，高速飞行，具有精确视觉惯性导航所需的低角速率。我们将模拟和基准解决方案质量方面的两个最先进的反应式运动规划器与一个全局规划师进行比较。 最后，我们展示了超过80个飞行计划器，在城市环境中，自动四旋翼飞行器的总距离为22km，速度高达9.4ms-1。