Path Tracking of Improved Pure Pursuit Model of Wheeled Harvester Based on PSO Algorithm

In order to improve path tracking accuracy of wheeled harvester, a pure pursuit path tracking algorithm with prediction characteristics based on particle swarm improvement was proposed．A kinematic model of wheeled harvester was established, and a pure pursuit path tracking algorithm based on kinematic model of wheeled harvester was derived．Based on harvester heading error and lateral error, an affiliation function with prediction characteristics was constructed, and optimal forward-looking distance was determined in real time by particle swarm optimization（PSO）algorithm using adaptive weighting coefficients．A straight path tracking road test and an "8" curve path tracking road test were conducted using a corn harvester as experimental platform．Experimental results showed that at a speed of 1.5 m/s, the maximum lateral error of linear path tracking was 4.39 cm, and the maximum heading error was 2.31 °; at a speed of 1 m/s, the maximum lateral error of curve path tracking was 5.24 cm, and the maximum heading error was 2.41 °．The improved path tracking algorithm has good path tracking performance for both straight and curved paths, meeting requirements of field operation of wheeled harvesters．