Synchronized path planning and tracking for front and rear axles in articulated wheel loaders

Wheel loaders are multi -function construction equipment, yet their complex kinematics and coupled front -rear axle relationships pose significant challenges in path planning and tracking control. This paper presents a synchronous path planning method and a reinforcement learning -based tracking controller for these axles. Initially, a kinematic model and tracking error model are developed based on steering radius analysis and verified through simulations. The Reeds-Shepp curve is used to plan the V-shaped path for the front axle, with the rear axle path derived from the kinematic model. Using reinforcement learning, a path tracking controller is developed with a preview model of tracking error. Performance is validated through simulations and field tests, showing a position error reduction of 25% and 37.5% for two paths, and heading error reductions of 27.8% and 27.3%. The results also indicate improved stability in heading and articulation angles during tracking.