Optimization of Path and Trajectory for Underground Mining Machines

Abstract: Underground mining machines play a critical role in the mining industry, enablingexcavation of valuable minerals from subterranean deposits. The efficiency of mining op-erations relies heavily on the effectiveness of these machines. Path and trajectory opti-mization techniques are important for improving the effectiveness by reducing the timeand resources required to excavate minerals while ensuring the safety of miners.This thesis explores the application of Model Predictive Control (MPC) in optimizingLoad-Haul-Dump (LHD) routes in underground mining operations. The research ques-tions focus on the utilization of MPC, considerations of dynamic vehicle behaviors, in-tegration of constraints and comparison of optimized routes in terms of cycle times andoverall mining process efficiency.The thesis adopts a two-controller approach, comprising longitudinal and lateral con-trollers, to effectively control the steering angle and optimize the vehicles path. The er-ror dynamics model accurately describes the vehicles position and orientation, enablingprecise route planning and execution. By developing and implementing an MPC-basedalgorithm, the routes are optimized, resulting in improvements in travel time.Dynamic vehicle behaviors, including position, orientation, longitudinal speed andsteering rate, are considered through the kinematic model of the articulated vehicle. Thisensures accurate representation and control of the vehicles movements.Constraints on the admissible state and control input, such as speed limitations, accel-eration limitations and steering angle limitations, are integrated into the MPC-based pathplanning algorithm. Collision avoidance with mine walls is also addressed through sim-ulation in MATLAB, incorporating the mine map. This analysis ensures the safety of thevehicles trajectory and highlights the importance of balancing optimization objectives withoperational constraints and safety requirements.Comparing the optimized LHD routes generated by the MPC-based algorithm revealsdifferences in travel time, with reductions ranging from 1-3 seconds. However, the specificmine conditions and constraints considered in this study may limit significant improve-ments in travel time.The findings of this research have implications for the mining industry, researchersand practitioners in autonomous vehicle control and optimization. The proposed methodenhances the efficiency and productivity of underground mining operations.Future work could explore alternative optimization approaches, refine the existingMPC-based algorithm and consider a more comprehensive vehicle dynamics model. Addi-tionally, investigating different mine environments and incorporating realistic sensor data,uncertainties and noise would improve the reliability and applicability of the method inreal-world mining scenarios.In conclusion, this study contributes to the understanding of applying MPC in LHDroute optimization, paving the way for further research. The utilization of a two-controllerapproach, error dynamics model and careful consideration of weighting parameters en-ables more effective route planning and execution. Continued advancements in au-tonomous vehicle control and optimization in the mining industry will lead to increasedproductivity, efficiency and indirectly safety in underground mining operations.