Path planning and tracking for autonomous mining articulated vehicles. (29th July 2019)

• Record Type: Journal Article
• Authors: Dou, Fengqian; Huang, Yanjun; Liu, Li; Wang, Hong; Meng, Yu; Zhao, Lianqiang
• Is Part Of: International journal of heavy vehicle systems. Volume 26:Number 3/4(2019)
• Page Start: 315
• Page End: 333
• Publication Date: 2019-07-29
• Subjects: articulated vehicle -- AV -- RSM -- relation space method -- path planning and tracking -- MPC -- model predictive control; Motor vehicles -- Design and construction -- Periodicals; Commercial vehicles -- Design and construction -- Periodicals; Motor vehicles -- Design and construction; Periodicals; 629.224
• Languages: English
• ISSNs: 1744-232X

Abstract

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.