Path planning and tracking for autonomous mining articulated vehicles

This paper presents a path planning and tracking framework for autonomous mining articulated vehicles (AVs). The relation space method is proposed for path planning. In this method, a self-organising, competitive neural network is adopted to identify the space around the vehicle. Then, the vehicle's optimal driving direction is determined by using the spatial geometric relationships of the identified space. A proportional-integral-derivative (PID) controller is used to control the vehicle speed, whereas a model predictive control (MPC) is designed for steering control, where the path tracking error model is used for MPC development. In addition, the AV's dynamics model is built in Adams. The effect and sensitivity of the path-tracking controller are validated through the Matlab-Adams co-simulation. The test results show the satisfactory path-tracking performance.