Research on Path Planning and Tracking Control of Autonomous Vehicles Based on Improved RRT* and PSO-LQR

Path planning and tracking control are essential parts of autonomous vehicle research. Regarding path planning, the Rapid Exploration Random Tree Star (RRT*) algorithm has attracted much attention due to its completeness. However, still suffers from slow convergence high randomness. tracking, Linear Quadratic Regulator (LQR) is widely used in various applications efficient stability ease implementation. relatively empirical selection weight matrix can affect effect. This study suggests a framework for vehicles based on an upgraded RRT* Particle Swarm Optimization (PSO-LQR) address abovementioned issues. Firstly, according driving characteristics vehicles, variable sampling area limit generation random points, significantly reducing number iterations. At same time, improved Artificial Potential Field (APF) method was introduced into algorithm, which speed algorithm. Utilizing pruning maximum steering angle constraint cubic B-spline achieve optimization, continuous curvature that conforms precise obtained. In addition, optimizing LQR using POS path-tracking accuracy. Finally, this article’s simulated compared with RRT*, target bias P-RRT*. Simulink–Carsim joint simulation platform, PSO-LQR track planned at different speeds. The results show optimizes search by 34.40% iteration 33.97%, respectively, generated paths continuous. accuracy about 59% LQR, higher. position error heading were controlled within 0.06 m 0.05 rad, verifying effectiveness feasibility proposed framework.