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

張寧 Optimal Trajectory Generation for Quadrotor Teach-And-Repeat
連接：https://pan.baidu.com/s/1x0CmuOXiLu_BHQFfhnrwSA 提取碼：9npg

Optimal Trajectory Generation for Quadrotor Teach-and-Repeat

四旋翼重複示教的最優軌跡生成

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，咱們提出了一種適用於四旋翼示教重複應用的新穎運動規劃框架。 咱們的方法不是控制無人機精確地遵循教學路線，而是將任意的人爲操縱的軌跡轉換爲拓撲等效的軌跡，從而保證了安全性，平滑性和運動學上可行的預期攻擊性。咱們提出的規劃框架在時間和空間兩個方面都優化了軌跡。在空間層中，發現了一個以走廊爲表明的自由空間，該自由空間在拓撲上與教學路徑等效。 而後，在飛行通道內生成了一個最小衝擊的分段軌跡。 在時間層中，軌跡被從新參數化以得到在運動動力學約束下的最小時間時間軌跡。空間和時間優化都被表述爲凸程序，而且是迭代完成的。 所提出的方法已集成到完整的四旋翼系統中，而且通過驗證可在具備挑戰性的室內和室外環境中執行激進的戰鬥。