弹道
无人机
路径(计算)
计算机科学
人工智能
实时计算
计算机视觉
控制工程
模拟
工程类
操作系统
天文
遗传学
生物
物理
作者
Fei Gao,Luqi Wang,Boyu Zhou,Xin Zhou,Jie Pan,Shaojie Shen
出处
期刊:IEEE Transactions on Robotics
[Institute of Electrical and Electronics Engineers]
日期:2020-10-01
卷期号:36 (5): 1526-1545
被引量:93
标识
DOI:10.1109/tro.2020.2993215
摘要
In this article, we propose a complete and robust system for the aggressive flight of autonomous quadrotors. The proposed system is built upon on the classical teach-and-repeat framework, which is widely adopted in infrastructure inspection, aerial transportation, and search-and-rescue. For these applications, a human's intention is essential for deciding the topological structure of the flight trajectory of the drone. However, poor teaching trajectories and changing environments prevent a simple teach-and-repeat system from being applied flexibly and robustly. In this article, instead of commanding the drone to precisely follow a teaching trajectory, we propose a method to automatically convert a human-piloted trajectory, which can be arbitrarily jerky, to a topologically equivalent one. The generated trajectory is guaranteed to be smooth, safe, and dynamically feasible, with a human preferable aggressiveness. Also, to avoid unmapped or moving obstacles during flights, a fast local perception method and a sliding-windowed replanning method are integrated into our system, to generate safe and dynamically feasible local trajectories onboard. We name our system as teach-repeat-replan. It can capture users' intention of a flight mission, convert an arbitrarily jerky teaching path to a smooth repeating trajectory, and generate safe local replans to avoid unexpected collisions. The proposed planning system is integrated into a complete autonomous quadrotor with global and local perception and localization submodules. Our system is validated by performing aggressive flights in challenging indoor/outdoor environments. We release all components in our quadrotor system as open-source ros packages.
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