RESEARCH on high-efficiency parallel simulation systems for unmanned mines

To overcome technical bottlenecks of insufficient model accuracy, data transmission latency, and low-efficiency large-scale scene rendering in open-pit mine unmanned haulage systems, this study proposes a parallel mine simulation system integrating digital twin and swarm intelligence. Firstly, a 16-DOF nonlinear coupled dynamic model for mining trucks (covering body, suspension, tires, and electro-hydraulic steering systems) is constructed, integrated with centimeter-level accuracy mining environment models generated via UAV oblique photogrammetry, achieving high-precision multi-physics field coupling modeling. Secondly, a data preprocessing pipeline based on cellular automata and car-following models is designed to optimize multi-agent coordination logic, achieving virtual-real interaction response time ≤50 ms and 22% improvement in transport scheduling efficiency. Further, an adaptive level-of-detail (LOD) rendering technology based on the Geometry Clipmap framework is proposed, leveraging CPU-GPU multithreaded computing to realize real-time rendering of ultra-large-scale terrains and 45-truck clusters at millisecond-level speeds, reducing rendering latency by 40%. Experimental validation confirms that the system supports multi-vehicle collaborative parallel simulation within a 0.5s iteration cycle, providing a high-precision, low-latency, and strongly real-time algorithm verification platform for unmanned mining operations.