Unified Modeling of Trajectory Planning and Tracking for Unmanned Vehicle

被引：0
作者：
Xu Y. [1 ]
Lu L.-P. [1 ]
Chu D.-F. [2 ]
Huang Z.-C. [2 ]
机构：
[1] College of Computer Science and Technology, Wuhan University of Technology, Wuhan
[2] Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2019年 / 45卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Artificial potential field; Model predictive control; Tracking control; Trajectory planning; Unmanned vehicle;
D O I：
10.16383/j.aas.2018.c170431
中图分类号：
学科分类号：
摘要：
Trajectory planning and tracking control of unmanned vehicles are the keys to autonomy. Generally, trajectory planning and tracking control are two functions in charge of generating reference trajectory according to the vehicle surrounding information and vehicle state information, and controlling vehicle motions according to the reference trajectory, respectively. In this paper, a unified modeling method to integrate trajectory planning and tracking control is presented. Based on the artificial potential field approach and vehicle dynamics modeling, the optimization algorithm of model predictive control is used to select the optimal local trajectory defined by the artificial potential field as the reference trajectory, which can be then tracked through vehicle motion control. A joint simulation of CarSim and MATLAB/Simulink shows that this method can effectively accomplish obstacle avoidance for the unmanned vehicle in several traffic scenarios. Copyright © 2019 Acta Automatica Sinica. All rights reserved.
引用
收藏
页码：799 / 807
页数：8
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