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

Yunfeng Liang1, Baoyan Zhi1, Chengjun Hu2

  • 1China Coal Shaanxi Energy and Chemical Group Co. Ltd., Yulin, China.

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|July 13, 2026
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Summary

Intelligent unmanned mining uses genetic algorithms for shearer drum cutting trajectory optimization. This improves coal recovery rates and reduces errors in autonomous navigation cutting technology.

Keywords:
navigation miningthe genetic algorithmthe shearertrajectory optimization

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Area of Science:

  • Mining Engineering
  • Robotics
  • Artificial Intelligence

Background:

  • Modern coal mining demands enhanced safety and efficiency through intelligent unmanned systems.
  • Autonomous navigation cutting technology represents a key advancement in intelligent, fully mechanized mining.

Purpose of the Study:

  • To develop an accurate 3D coal seam model for shearer navigation.
  • To optimize the shearer drum cutting trajectory for reduced gangue and increased coal recovery.

Main Methods:

  • Constructing an accurate 3D coal seam model using implicit functions and cubic B-spline fitting.
  • Comparing BP neural network, RBF neural network, and genetic algorithms for trajectory optimization.
  • Implementing and simulating the genetic algorithm for shearer drum cutting path optimization.

Main Results:

  • The genetic algorithm significantly optimized the shearer drum cutting trajectory.
  • Maximum cutting trajectory error was reduced to 0.017 m after 100 generations.
  • Optimized results demonstrated superior performance compared to pre-optimization cubic B-spline fitting.

Conclusions:

  • The genetic algorithm provides effective theoretical and technical support for intelligent navigation mining.
  • This research advances the development of autonomous navigation cutting technology in coal mining.
  • Optimized cutting trajectories enhance operational efficiency and resource recovery in intelligent mining.