泡泡一分钟：Optimal Trajectory Generation for Quadrotor Teach-And-Repeat

张宁 Optimal Trajectory Generation for Quadrotor Teach-And-Repeat
链接：https://pan.baidu.com/s/1x0CmuOXiLu_BHQFfhnrwSA 提取码：9npg

Optimal Trajectory Generation for Quadrotor Teach-and-Repeat

四旋翼重复示教的最优轨迹生成

Fei Gao, Luqi Wang, Kaixuan Wang, William Wu, Boyu Zhou, Luxin Han and Shaojie Shen

In this paper, we propose a novel motion planning framework for quadrotor teach-and-repeat applications. Instead of controlling the drone to precisely follow the teaching path, our method converts an arbitrary jerky human-piloted trajectory to a topologically equivalent one,which is guaranteed to be safe, smooth, and kinodynamically feasible with an expected aggressiveness. Our proposed planning framework optimizes the trajectory in both spatial and temporal aspects.In the spatial layer, a flight corridor is found to represent the free space which is topologically equivalent with the teaching path. Then a minimum-jerk piecewise trajectory is generated within the flight corridor. In the temporal layer, the trajectory is reparameterized to obtain a minimum-time temporal trajectory under kinodynamic constraints. The spatial and temporal optimizations are both formulated as convex programs and are done iteratively. The proposed method is integrated into a complete quadrotor system and is validated to perform aggressive flights in challenging indoor and outdoor environments.

在本文中，我们提出了一种适用于四旋翼示教重复应用的新颖运动规划框架。 我们的方法不是控制无人机精确地遵循教学路线，而是将任意的人为操纵的轨迹转换为拓扑等效的轨迹，从而保证了安全性，平滑性和运动学上可行的预期攻击性。我们提出的规划框架在时间和空间两个方面都优化了轨迹。在空间层中，发现了一个以走廊为代表的自由空间，该自由空间在拓扑上与教学路径等效。 然后，在飞行通道内生成了一个最小冲击的分段轨迹。 在时间层中，轨迹被重新参数化以获得在运动动力学约束下的最小时间时间轨迹。空间和时间优化都被表述为凸程序，并且是迭代完成的。 所提出的方法已集成到完整的四旋翼系统中，并且经过验证可在具有挑战性的室内和室外环境中执行激进的战斗。