Intelligent coal mining: An approach to generating optimal Shearer's cutting trajectories

Intelligent unmanned mining technology is very important for modern coal mining to improve the safety and efficiency. Autonomous navigation cutting technology is an emerging trend of intelligent fully mechanized coal mining technology. This paper presents the construction method of the accurate 3D coal seam model as determination of the navigation map of the shearer. The cutting plane implicit function was induced to generating the section of the coal seam model, and the coal seam roof and floor boundary curves were achieved by the cubic B-spline curve fitting. By comparing the optimization effects of the BP neural network, the RBF neural network and the genetic algorithm, the genetic algorithm is finally selected for the shearer drum cutting trajectory optimization to reduce the gangue rate and improve the coal recovery rate. The simulation steps of the genetic algorithm are described in the paper, whose experimental results show that after 100 generations of optimization by the genetic algorithm. The maximum error of the cutting trajectory is reduced to 0.017 m, which is significantly better than the cubic B-spline fitting results before optimization. The findings of the paper provide the theoretical and technical support for the development of intelligent navigation mining technology of the shearer.