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

Abstract

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

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

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