%0 Journal Article
%T 基于改进人工势场法的车辆路径规划与跟踪<br>Vehicles Path Planning and Tracking Based on an Improved Artificial Potential Field Method
%A 唐志荣
%A 冀杰
%A 吴明阳
%A 方京城
%A 陈明哲&lt
%A br&gt
%A TANG Zhi-rong
%A JI Jie
%A WU Ming-yang
%A FANG Jing-cheng
%A CHEN Ming-zhe
%J 西南大学学报(自然科学版)
%D 2018
%R 10.13718/j.cnki.xdzk.2018.06.025
%X 为保证智能车辆在转向避撞过程中的主动安全性，基于改进的人工势能场模型进行了转向避撞路径规划，利用椭圆化距离代替传统斥力势场中的实际距离，同时，引入道路边界斥力场模型，从而在较小车道空间内获得汽车避撞局部路径.另外，建立了以前轮转角为控制变量的三自由度车辆动力学模型，并利用模型预测控制算法对规划路径进行了跟踪. CarSim/Simulink联合仿真结果表明，利用改进的人工势场法可获得平顺且安全的局部避撞路径，而提出的模型预测控制算法具有良好的路径跟踪性能.<br>In order to ensure the active safety of intelligent vehicles in the process of collision avoidance by active steering, path planning for collision avoidance is carried out based on an improved artificial potential field model. The elliptical distance is used instead of the actual distance in the traditional repulsive potential field. At the same time, the repulsion field model of the boundary is introduced so as to obtain the local path for collision avoidance in a small lane space. In addition, the three-degree-of-freedom vehicle dynamics model with the front wheel steering angle as the control variable is established, and the planning path is tracked by the model predictive control algorithm. The results of CarSim/Simulink co-simulation experiments demonstrate that the improved artificial potential field method can obtain a smooth and safe local path for collision avoidance, and the model predictive control algorithm proposed has a good path tracking performance
%K 人工势场法
%K 路径规划
%K 路径跟踪
%K 模型预测控制
%K 目标函数&lt
%K br&gt
%K artificial potential field method
%K path planning
%K path tracking
%K model predictive control
%K cost function
%U http://xbgjxt.swu.edu.cn/jsuns/html/jsuns/2018/6/201806025.htm