Path planning is one of the key technologies for unmanned driving of underground intelligent vehicles. Due to the complexity of the drift environment and the vehicle structure, some improvements should be made to adapt to underground mining conditions. This paper proposes a path planning method based on an improved RRT* (Rapidly-Exploring Random Tree Star) algorithm for solving the problem of path planning for underground intelligent vehicles based on articulated structure and drift environment conditions. The kinematics of underground intelligent vehicles are realized by vectorized map and dynamic constraints. The RRT* algorithm is selected for improvement, including dynamic step size, steering angle constraints, and optimal tree reconnection. The simulation case study proves the effectiveness of the algorithm, with a lower path length, lower node count, and 100% steering angle efficiency.

The main issue in short-term planning optimisation for underground mining is organising the mining process with limited resources in the form of equipment and materials to satisfy production targets and stable feed grade requirements. In this paper, an integrated optimisation model is proposed based on an individual generation algorithm and an improved Genetic Algorithm to simultaneously optimise stope extraction sequencing and timing, extracted ore grade and equipment dispatching. The model objectives are to shorten the time gap between the stope mining processes and the overall working time. When the uncertainty of equipment working time is taken into account in a short-term scheduling model, the Monte Carlo simulation is applied to evaluate the risk of not meeting the production target. A modification strategy is defined to evaluate equipment failure. Consequently, any available equipment is automatically reassigned to the mining site to replace the broken-down equipment. A case study is used to validate the model in the Sanshandao gold mine of China to formulate an optimal monthly schedule. Compared with the conventional approach, the new model could reduce the variance of ore tonnage and feed grade and improve the equipment allocation efficiency. Discussions are presented to address the uncertainty.

The choice of transportation system has an important impact on the production efficiency and economic behavior of underground mines. Trackless vehicle transportation has gradually become the main method in underground mines because mining companies have realized that mining efficiency can be improved using advanced vehicle mechanization and automation in the mining process. The extracted ore is loaded onto trucks by loaders in situ, then the trucks drive to ore passes for unloading Trucks load and unload ore in a cyclical manner between stopes and ore passes. Numerous trucks drive in tunnels simultaneously to achieve production targets, and there are interactions and influences among trucks, such as blocking and queuing, due to limited underground space. To address this issue, a transportation route model was built, and the ore transportation process was divided into three parts, including ore loading, truck transportation, and ore unloading. The simulation method was applied to optimize the number of loaders and trucks under the constraints of stope production capacity, transportation route and capacity, and vehicle capacity, to achieve the optimal vehicle utilization rate and transportation capability. The Monte Carlo simulation method was utilized to take the uncertainties of the transportation parameters into account to improve the robustness of the simulation results. The model was verified using the case study of an underground gold mine located in Shandong Province, China, with the objective of accomplishing optimal truck–loader matching considering various stopes in a mining area.

With the increase in environmental awareness worldwide, the filling mining method has attracted extensive attention because this method can realize safe and green mining in underground metal mines. Recycling waste tailings in stopes to control underground rock movement, and surface settlement can reduce waste environmental pollution while ensuring mining safety. Although many test data are required to support the formulation of the mine backfill scheme, the advanced management tool of backfill test data is insufficient. In this study, a new data management method that is suitable for backfill experiments is proposed. First, this study analyses the main system requirements, including experimental business process modeling, experimental process combing, and a multidimensional query of experimental data. Then, the backfill test business flow and data flow are summarized to establish the backfill test business model and experimental index system. Then, many system functions are designed, including backfill experiment management, experimental data query, backfill knowledge maintenance, and system management. Finally, a backfill test data management system is developed based on B/S architecture. Developing a data interface, having a built‐in test formula and customizing a multidimensional data analysis enable the system to solve the problems in data collection, data accounting, and data analysis. After being put into use in the Backfilling Engineering Laboratory of a group in Shandong, this system improved the data‐sharing rate and utilization rate and provided a convenient data management tool for the laboratory.

Production scheduling management is crucial for optimizing mine productivity. With the trend towards intelligent mines, a lean scheduling management mode is required to align with intelligent conditions. This paper proposes a lean scheduling framework, based on short interval control as an effective tool to adapt intelligent scheduling in underground mines. The framework shortens the production monitoring and adjustment cycle to near-real-time, enabling timely corrective measures to minimize schedule deviations and improve overall production efficiency. An intelligent scheduling platform is implemented by adopting the digital twin platform framework, the intelligent scheduling mobile terminal module, and the integrated scheduling control cockpit module. The results indicate that the platform is effective in promoting mine intelligence by providing benefits in information transparency, flexible scheduling, lean production, and scientific decision-making. The proposed framework provides a practical solution for implementing intelligent scheduling in underground mines, contributing to the overall improvement of mine productivity. Overall, this paper provides insights for implementing intelligent scheduling in underground mines.