Path Optimization of Coal Mining Machine Based on Virtual Coal-Rock Interface

  • Dong Gang
  • Nie Zhen
  • Wang Peijun
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 1)


On the intelligent coal face, when coals fall down, the roller on the coal mining machine can recognize roof coal-rock interface and programme a motion path to avoid knifing the roof rock. It can not only effectively protect the rocker arm of coal mining machine but observably improve the quality of the raw coal produced on the working face. At present, the study on this subject focuses on two aspects: One is the study on technology for recognizing coal-rock interface based on all kinds of principles and the study on tech for projecting the slicing path of top roller; the other is the study on follow-up control technique which aims at the heightening system of coal mining machine. All the study on projecting the slicing path of top roller or the follow-up control of heightening system before did not take the size of the roller into account adequately, which caused the roof rock incised when the coals fell down. On the premise that the size of roller appearance was considered fully, this article puts forward the motion path optimizing of the coal mining machine’s top roller based on virtual coal-rock interface, which solves the problem of roof rock’s being sliced by top roller while working, and conducts a simulation experiment.


virtual coal-rock interface coal mining machine roller follow-up control path optimization 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Wang Jinhua, Huang Leting, Li Shoubing, et al. the development of fully mechanized working face technology and equipment [J]. Journal of Coal.Google Scholar
  2. 2.
    Ralston, Jonathon, Reid, et al. Sensing for advancing mining automation capability:A review of underground automation technology development[J]. International Journal of Mining Science & Technology. 2014, 24(3): 305-310.Google Scholar
  3. 3.
    Zhi-Peng X U, Wang Z B, Jin-Peng M I. Shearer self-adaptive memory cutting[J]. Journal of Chongqing University. 2011, 34(4): 134-140.Google Scholar
  4. 4.
    C. Houaidia, H. Idoudi, A. Van Den Bossche, T. Val, and L. A. Saidane, “Towards an Optimized Traffic-Aware Routing in Wireless Mesh Networks,” International Journal of Space-Based and Situated Computing, vol. 4, p. 217-232, 2014.Google Scholar
  5. 5.
    Li W, Luo C, Yang H, et al. Memory cutting of adjacent coal seams based on a hidden Markov model[J]. Arabian Journal of Geosciences. 2014, 7(12): 5051-5060.Google Scholar
  6. 6.
    Lin, Z. (2014). Clock-controlled generators with large period output sequences. International Journal of Grid & Utility Computing, 5(4), 278-285.Google Scholar
  7. 7.
    Wang B P, Wang Z C, Li Y X. Application of Wavelet Packet Energy Spectrum in Coal-Rock Interface Recognition[J]. Key Engineering Materials. 2011, 474-476: 1103-1106.Google Scholar
  8. 8.
    Houaidia, C., Idoudi, H., Bossche, A. V. D., Val, T., & Saidane, L. A. (2014). Towards an optimized traffic-aware routing in wireless mesh networks. International Journal of Space-Based and Situated Computing,4(3/4), 217-232.Google Scholar
  9. 9.
    Ralston J C. Automated longwall shearer horizon control using thermal infrared-based seam tracking[C]. 2012.Google Scholar
  10. 10.
    M. Puzar and T. Plagemann, “Data sharing in mobile ad-hoc networks-a study of replication and performance in the MIDAS data space,” International Journal of Space-Based and Situated Computing, vol. 1, p. 137-150, 2011.Google Scholar
  11. 11.
    Quan G T, Tan C, Hou H C, et al. Cutting Path Planning of Coal Shearer Base on Particle Swarm Triple Spline Optimization[J]. Coal Science & Technology. 2011.Google Scholar
  12. 12.
    Fan, Qigao, Li, et al. Control strategy for an intelligent shearer height adjusting system[J]. Mining Science & Technology. 2010, 20(6): 908-912.Google Scholar
  13. 13.
    Nesmachnow, S., & Iturriaga, S. (2013). Multiobjective grid scheduling using a domain decomposition based parallel micro evolutionary algorithm. International Journal of Grid & Utility Computing, 4(1).Google Scholar
  14. 14.
    Su X, Li W. SLIDING MODE ROBUSTNESS CONTROL STRATEGY OF SHEARER HEIGHT ADJUSTING SYSTEM UNDERGROUND COAL MINES[J]. Journal of Theoretical & Applied Information Technology. 2013, 12(2).Google Scholar
  15. 15.
    Khatib O. Real-time obstacle avoidance for manipu-lators and mobile robots[J]. International Journal of Robotics Research. 1985, 1(5): 500-505.Google Scholar
  16. 16.
    Li C, Jiang X, Wang W, et al. A Simplified Car-following Model Based on the Artificial Potential Field[J]. Procedia Engineering. 2016, 137: 13-20.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Mechanical EngineeringXi’an University of Science and TechnologyXi’anChina
  2. 2.Border Defence Academy of PLAXi’anChina

Personalised recommendations