论文标题
在存在动态障碍的情况下，实时的长距离轨迹轨迹重建了MAV
Real-Time Long Range Trajectory Replanning for MAVs in the Presence of Dynamic Obstacles
论文作者
论文摘要
实时的远程本地计划是一项具有挑战性的任务，尤其是在存在动态障碍的情况下。我们提出了一个能够实时执行本地重建的完整系统。在系统初始化阶段需要所需的轨迹；系统开始初始化系统的子组件，包括点云处理器，轨迹估计器和计划器。之后，多旋转航空车开始在给定的轨迹上移动。当它检测到障碍物时，它将轨迹从当前姿势恢复到结合所需轨迹的预定义距离。点云处理器用于确定车辆周围最接近的障碍。为了进行重新植物，将快速探索的随机树（RRT*）用于两种修改，可以以毫秒为单位进行计划。一旦我们对所需的路径进行了回答，便会计算速度成分（X，Y和Z）和偏航速率。这些值以恒定频率发送到控制器，以自动操纵车辆。最后，我们分别评估了每个组件，并在模拟和真实环境中测试了完整的系统。
Real-time long-range local planning is a challenging task, especially in the presence of dynamics obstacles. We propose a complete system which is capable of performing the local replanning in real-time. Desired trajectory is needed in the system initialization phase; system starts initializing sub-components of the system including point cloud processor, trajectory estimator and planner. Afterwards, the multi-rotary aerial vehicle starts moving on the given trajectory. When it detects obstacles, it replans the trajectory from the current pose to pre-defined distance incorporating the desired trajectory. Point cloud processor is employed to identify the closest obstacles around the vehicle. For replanning, Rapidly-exploring Random Trees (RRT*) is used with two modifications which allow planning the trajectory in milliseconds scales. Once we replanned the desired path, velocity components(x,y and z) and yaw rate are calculated. Those values are sent to the controller at a constant frequency to maneuver the vehicle autonomously. Finally, we have evaluated each of the components separately and tested the complete system in the simulated and real environments.
