Parallel Driving in Mining: ACP based Prediction and Tracking of Heavy-duty Vehicle

The autonomous driving of heavy-duty tracked vehicles (HTVs) is prone to significant position and posture deviation at the end of walking part in trajectory tracking control, which may affect the efficiency and safety of vehicle operation seriously. Taking mining-used HTVs as an example, the driving environment is complex and harsh with numerous disturbances, making it difficult to conduct on-site experiments on autonomous driving. In this paper, a parallel mining HTVs autopilot system composed of artificial system and physical system is established based on the ACP (artificial system, computational experiment, parallel execution) method to solve the problem of difficult computational experiments of mining HTVs in the shaft roadway. The optimization of control strategies of miningused HTVs is completed through computational experiments in different scenarios. After the optimization of the strategies through computational experiments, the trajectory tracking effect was improved by 12.05% and 13.77% in two different scenarios, respectively; The average deviation angle optimization effect during operation reached 75.76% and 78.45% respectively, and the average deviation optimization effect reached 53.47% and 58.08%, respectively. Finally, bidirectional control and collaborative evolution were achieved through the interaction of virtual and real data between artificial system and physical system. The effectiveness of the control method was demonstrated by theoretical analysis and parallel execution tests.