弹道
计算机科学
水准点(测量)
稳健性(进化)
数学优化
运动规划
图形
规划师
拓扑图
拓扑(电路)
机器人
控制理论(社会学)
数学
人工智能
理论计算机科学
生物化学
基因
组合数学
物理
化学
大地测量学
地理
控制(管理)
天文
作者
Hongkai Ye,Xin Zhou,Zhepei Wang,Chao Xu,Jian Chu,Fei Gao
出处
期刊:IEEE robotics and automation letters
日期:2021-04-01
卷期号:6 (2): 494-501
被引量:24
标识
DOI:10.1109/lra.2020.3047798
摘要
In this letter, we propose a lightweight yet effective Topology Guided Kinodynamic planner (TGK-Planner) for quadrotor aggressive flights with limited onboard computing resources. The proposed system follows the traditional hierarchical planning workflow, with novel designs to improve the robustness and efficiency in both the pathfinding and trajectory optimization sub-modules. Firstly, we propose the topology guided graph, which roughly captures the topological structure of the environment and guides the state sampling of a sampling-based kinodynamic planner. In this way, we significantly improve the efficiency of finding a safe and dynamically feasible trajectory. Then, we refine the smoothness and continuity of the trajectory in an optimization framework, which incorporates the homotopy constraint to guarantee the safety of the trajectory. The optimization program is formulated as a sequence of quadratic programmings (QPs) and can be iteratively solved in a few milliseconds. Finally, the proposed system is integrated into a fully autonomous quadrotor and validated in various simulated and real-world scenarios. Benchmark comparisons show that our method outperforms state-of-the-art methods with regard to efficiency and trajectory quality. Moreover, we will release our code as an open-source package.
科研通智能强力驱动
Strongly Powered by AbleSci AI