基于RRT与MPC的智能车辆路径规划与跟踪控制研究*

doi:10.19562/j.chinasae.qcgc.2020.09.002

摘要/Abstract

摘要： 为分析智能车辆实时规划和跟踪控制的相互影响关系,基于改进的快速随机搜索树规划算法(improved-RRT)与线性时变的模型预测控制算法,提出了一种智能车路径规划与跟踪控制系统的构架。首先,采用目标导向、节点修剪、曲线拟合和最优路径选择等方法对基础RRT规划算法进行改进,保证规划路径满足车辆运动学约束并趋近最优解。然后,基于线性时变模型预测控制算法,实现智能车对期望路径的稳定控制。硬件在环仿真结果表明,车速为36 km/h,规划步长为2 m,规划周期为0.1 s时,侧向加速度小于0.2g,满足安全性和实时性要求。最后,分析了车速、规划步长和规划周期等因素对实时规划和稳定跟踪的影响。

关键词: 智能车, 改进的快速随机搜索树, 线性时变的模型预测控制, 硬件在环仿真

Abstract: In order to analyze the mutual influence between real-time planning and tracking control of smart car, a new architecture of path planning and tracking control for intelligent vehicle is proposed based on improved rapidly-exploring random tree (RRT) algorithm and linear time-varying model predictive control (LTV-MPC) algorithm. Firstly, basic RRT algorithm is modified by target orientation, node pruning, curve fitting and optimal path selection to ensure the planned path meets the vehicle kinematic constraint requirements and approaches the optimal solution. Then, the stability control on the desired path of intelligent vehicle is achieved based on LTV-MPC algorithm. The results of hardware-in-the-loop simulation show that with a vehicle speed of 36 km/h, a planning step of 2 m and a planning cycle of 0.1 s, the lateral acceleration is less than 0.2g, meeting the requirements of safety and real-time performance. Finally, the effects of factors such as vehicle speed, planning step and planning cycle on real-time planning and stability tracking are analyzed

Key words: intelligent vehicle, improved-RRT, LTV-MPC, hard-in-the-loop simulation

周维, 过学迅, 裴晓飞, 张震, 余嘉星. 基于RRT与MPC的智能车辆路径规划与跟踪控制研究^*[J]. 汽车工程, 2020, 42(9): 1151-1158.

Zhou Wei, Guo Xuexun, Pei Xiaofei, Zhang Zhen, Yu Jiaxing. Study on Path Planning and Tracking Control for Intelligent Vehicle Based on RRT and MPC[J]. Automotive Engineering, 2020, 42(9): 1151-1158.

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基于RRT与MPC的智能车辆路径规划与跟踪控制研究^*

Study on Path Planning and Tracking Control for Intelligent Vehicle Based on RRT and MPC

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