Skip to main content
Log in

A Virtual Planning Method for Spatial Pose and Performance Fusion Advancement of Mining and Transportation Equipment in Complex Geological Environment

  • Published:
Mining, Metallurgy & Exploration Aims and scope Submit manuscript

Abstract

The operation of intelligent coal mining in complex geological environment is still unable to realize automation. In order to realize the autonomous cutting of fully mechanized mining equipment and improve coal production, a mining and transportation equipment dynamic advancement path planning method based on spatial kinematics coordinate deduction prediction and real-time correction prediction of coal-rock state identification of equipment cutting current index are proposed. Firstly, the spatial motion law of the mining and transportation equipment is analyzed, and the mathematical model of the spatial motion of the mining and transportation equipment is established. The relationship between the adjustment of the shearer rocker arm and the pose change of the scraper conveyor is determined, and the coordinate deduction model of the spatial advancement position of the equipment is determined, which provides a theoretical basis for the advancement path planning of the mining and transportation equipment. Then, on the basis of the mathematical model construction, combined with real geological data and equipment information, the three-dimensional dynamic planning of the advancement path of the mining and transportation equipment is carried out from the two dimensions of the horizontal advancement direction and the shearer walking direction. At the same time, the relationship between the cutting current index of shearer and the state recognition of coal-rock mass is analyzed, and the coal-rock mass state identification of cutting current and the adjustment of rocker arm model is constructed, and the real-time dynamic correction of three-dimensional path is carried out, so that the planned path can better adapt to the sudden change of real geological environment. Then, using the unity3d virtual reality technology, a simulation system for the horizontal advancement path planning of the virtual mining and transportation equipment is constructed, which simulates the real advancement operation process of the mining and transportation equipment and conducts advancement training. Finally, the dynamic advancement scheme update prediction based on the existing actual cutting but ahead of the actual cutting is realized in the virtual scene, and the optimal scheme is transmitted into the existing prediction cutting system to guide the actual physical equipment mining. The path planning method can control the coordinate position error of the advancing cutting path within 15 mm, which provides ideas for the research on the advancing path planning of fully mechanized equipment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Wang GF, Liu F, Meng XJ (2019) Research and practice of coal mine intellectualization (primary stage). Coal Sci Technol 47(8):1–36

    Google Scholar 

  2. Ge SR, Hao SQ, Zhang SH (2020) Present situation and key technologies of intelligent coal mining technology in China. Coal Sci Technol 48(7):28–46

    Google Scholar 

  3. Jonathon CR, Chad OH, Mark TD (2017) Longwall automation: trends, challenges and opportunities. Int J Min Sci Technol 27(5):733–739

    Article  Google Scholar 

  4. Fu RK, Zhang CY, Zhang H (2017) Discovery and outlook on intelligent sensing and control technology of mine fully-mechanized mining. Coal Sci Technol 45(9):72–78

    Google Scholar 

  5. Wang WH, Jiang LS, Wang QW (2021) Present situation and prospect of intelligent mining technology in fully mechanized mining face of coal mine. China Coal 47(11):51–55

    Google Scholar 

  6. Syd SP, Feng D, Jingyi C (2019) Automation in U.S. longwall coal mining: a state-of-the-art review. Int J Min Sci Technol 29(02): 151–159

  7. Peng SP (2020) Research status and prospect of geological guarantee system for coal mine safety and high efficiency mining in China. J China Coal Soc 45(7):2331–2345

    Google Scholar 

  8. Cheng JY, Zhu MB, Wang YH (2019) Cascade Construction and key technology of geological model of coal intelligent precision mining face. J China Coal Soc 44(8):2285–2295

    Google Scholar 

  9. Cheng JY, Liu WM, Wang YH (2020) Experimental study on cascade optimization of geological model of transparent working face in intelligent mining. Coal Sci Technol 48(7):118–126

    Google Scholar 

  10. Liu WL, Zhang XL, Wang SB (2020) 3D model construction and dynamic correction technology of coal seam in coal mining face. J China Coal Soc 45(06):1973–1983

    Google Scholar 

  11. Ge SR, Zhang F, Wang SB (2020) Research on technical architecture of digital twin intelligent mining face. J China Coal Soc 45(6):1924–1936

    Google Scholar 

  12. Xie JC, Liu SG, Wang XW (2022) Framework for a closed-loop cooperative human Cyber-Physical System for the mining industry driven by VR and AR: MHCPS. Comput Ind Eng 168:108050

    Article  Google Scholar 

  13. Berg LP, Vance JM (2017) Industry use of virtual reality in product design and manufacturing: a survey. Virtual Reality 21:1–17

    Article  Google Scholar 

  14. Liagkou V, Salmas D, Stylios C (2019) Realizing virtual reality learning environment for industry 4.0. Procedia CIRP 79(2):712–717

  15. Dobrescu R, Merezeanu D, Mocanu S (2019) Process simulation platform for virtual manufacturing systems evaluation. Comput Ind 104(1):131–140

    Article  Google Scholar 

  16. Rozmus M, Tokarczyk J, Michalak D (2021) Application of 3D scanning, computer simulations and virtual reality in the redesigning process of selected areas of underground transportation routes in coal mining industry. Energies 14(9):2589

    Article  Google Scholar 

  17. Li SH, Xie JC, Ren F (2021) Virtual straightening of scraper conveyor based on the position and attitude solution of industrial robot model. Int Coal Sci Technol. https://doi.org/10.1007/s40789-020-00389-y

  18. Xie JC, Yang ZJ, Wang XW (2018) Cooperative solving method of chute postures in the bending section of a scraper conveyor. Adv Mech Eng 10(3):1–13

    Article  Google Scholar 

  19. Li JL, Siang S, Xie JC (2021) Construction method of dynamic 3D geological model based on shearer cutting path. J Northeast Univ 42(05):706–712

    Google Scholar 

  20. Zhang Q, Wang XW, Xie JC (2017) Positioning and attitude adjustment of shearer based on strapdown inertial navigation system. Industry and Mine Automation 43(10):83–89

    Google Scholar 

  21. Li JL, Liu Y (2020) Cutting path planning technology of shearer based on virtual reality. Appl Sci 10(3):771

    Article  Google Scholar 

  22. Zhang H, Hou YB, Sun ZM (2021)An optimal algorithm for planning shearer trailing drum cutting path. Shock Vibr. 2021:1354705

  23. Zhang D (2021) Height adjustment model and obstacle avoidance strategy of unmanned shearer drum. Coal Mine Machinery 42(07):53–56

    Google Scholar 

  24. Liu CS, Liu YT, Liu RH (2022) Associated load characteristic model of shearer cutting state and coal rock recognition. J China Coal Soc 47(01):527–540

    Google Scholar 

  25. Liu YG (2017) Unmanned adaptive variable speed cutting control method of shearer. J S-Cent Univ Natl (Nat Sci Ed) 48(06):1513–1521

  26. van Wyk G, Els DNJ, Akdogan G (2014) Discrete element simulation of tribological interactions in rock cutting. Int J Rock Mech Min 65(2014):8–19

  27. Si L (2014) Cutting path planning of shearer based on coal seam distribution prediction. J China U Min Techno 43(03):464–471

    Google Scholar 

  28. Wang SB, Wang SJ, Ge ZL (2018) A mathematical model and error analysis of shearer cutting path based on its attitude. Math Probl Eng 2018:1790602

    Google Scholar 

  29. Zhuang CB, Liu JH, Xiong H (2018) Digital twin-based smart production management and control framework for the complex product assembly shop-floor. Int J Adv Manuf Tech 96:1149–1163

    Article  Google Scholar 

  30. Xie JC, Wang XW, Yang ZJ (2019) Design and operation mode of production system of fully mechanized working face based on digital twinning. Comput Integra Manuf Sys 25(6):1381–1391

    Google Scholar 

  31. Xie JC, Li SH, Wang XW (2022) A digital smart product service system and a case study of the mining industry: MSPSS. Adv Eng Inform 53:101694

Download references

Funding

This research was supported by the National Natural Science Foundation of China (grant no. 52004174), the Major Scientific and Technological Innovation Programs of "Unveil the List" in Shanxi (no. 202101020101021), the Shanxi “1331” Project, the Key Project of the Chinese Society of Academic Degrees and Graduate Education (grant no. 2020ZDA12), the Scientific Research Planning for Higher Education in 2022 (grant no. 22SZH0306), and the Central Government Guides Local Science and Technology Development Funds Projects (YDZJSX2022A014).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Juanli Li.

Ethics declarations

Conflict of Interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dong, M., Xie, J., Li, J. et al. A Virtual Planning Method for Spatial Pose and Performance Fusion Advancement of Mining and Transportation Equipment in Complex Geological Environment. Mining, Metallurgy & Exploration 40, 231–251 (2023). https://doi.org/10.1007/s42461-022-00693-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42461-022-00693-y

Keywords

Navigation