Research on Autonomous Positioning and Cutting Path Optimization of Coal Mining Machines Based on Multi-Sensor Fusion

To address engineering challenges in complex operating conditions—including low positioning accuracy of single sensors, poor adaptability of static cutting paths, and insufficient stability in intelligent operations—this study investigates autonomous positioning and cutting path optimization for coal mining machines using multi-sensor fusion. By improving the adaptive fusion algorithm and developing a six-degree-of-freedom pose estimation model, high-precision positioning under complex conditions is achieved. Considering geological conditions, equipment constraints, and safety requirements at the working face, a multi-objective optimization function was established with enhanced intelligent optimization algorithms to enable dynamic cutting path planning. Simulation results demonstrate a 62.5% improvement in positioning accuracy, reduced cutting energy consumption and equipment wear, providing robust technical support for intelligent autonomous coal mining operations.