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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.
Science Progress
|July 13, 2026
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.
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.
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