Abstract: Mine auxiliary transportation material scheduling is an important part of coal mine safety and efficient production. The transportation environment in coal mines is complex, accidents occur frequently, and the demand for materials is widely distributed. The transportation of materials mainly relies on manual shift scheduling, which is difficult to dispatch vehicles and has a low degree of information transparency. It has become an important direction for the intelligent construction of coal mines. With the research and application of auxiliary transportation robots, material distribution is gradually achieving continuous transportation and is moving towards intelligent dispatching. Aiming at the problem of auxiliary transportation material scheduling in mines, it is mapped to the horizontal transportation of automated terminals, and a centralized dynamic scheduling method for auxiliary transportation robots based on digital twins is proposed. To minimize the maximum transportation time of auxiliary transportation robots while ensuring conflict-free path planning, a two-stage scheduling model is proposed, which integrates balanced task allocation and addresses constraints such as robot endurance, load capacity, and potential malfunctions. A two-layer algorithm is designed by combining the cultural genetic algorithm with the Dijkstra algorithm incorporating time window constraints to solve the model. Once the generated dispatching plan is simulated and validated through a digital twin system, the dispatching system centrally controls the transportation robots and fixed equipment to collaboratively execute the transportation tasks. Based on the actual auxiliary transportation operations of a mine in Guizhou Province, a digital twin system for auxiliary transportation was designed and implemented. Following the verification of virtual-physical consistency, simulation analyses and experimental tests were conducted to evaluate the performance of the proposed model and algorithm. The results show that the dynamic scheduling strategy for auxiliary transportation robots can rationally allocate scheduling tasks, detect and resolve robot conflicts, and the task allocation balance rate reaches 94.5%. Analyze the relationship between factors such as the number of orders, the number of vehicles, and the utilization rate of sections and the dispatching of transportation robots, improve the utilization rate of robots, and optimize the allocation of underground resources; Analyze the relationship between factors such as the number of orders, the number of vehicles, and the utilization rate of sections and the dispatching of transportation robots, improve the utilization rate of robots, and optimize the allocation of underground resources; Compared with manual scheduling, the dynamic scheduling strategy reduced the maximum order completion time by 34.4%, achieved an anti-disturbance stability of 98.3%, and reached a robot equipment utilization rate of 92.79%. The proposed model and algorithm demonstrated their effectiveness in avoiding operational conflicts among underground robots and in implementing interference-resistant scheduling, thereby enhancing the scheduling efficiency of auxiliary transportation robots.