Approaches to position determination of powered walking support in a mine working

M.S. Nikitenko, D.Yu. Khudonogov, S.A. Kizilov
Federal Research Center of Coal and Coal Chemistry of Siberian Branch of the Russian Academy of Sciences, Kemerovo, Russian Federation
Russian Mining Industry №4/ 2026 p. 147-152

Abstract: The walking roof support, viewed as a mobile walking robot, requires autonomous identification of its position in an underground mine working. However, existing means (laser rangefinders, lidars, ultrasonic sensors) have limitations, e.g. their sensitivity to dust, moisture, and ambient temperature. There are no systematic solutions for determining the position of the walking roof support relative to the face, floor, and components of the mining machines adapted to the conditions of coal mines. The aim of this work was to study efficiency of the proposed methods for determining position of the walking roof support based on machine vision technologies with low requirements for computational resources. A number of specialized techniques have been developed, in particular: (1) determining the distance to the face by the projection angle of a light marker; (2) assessing the floor terrain by extracting pixels with bright luminescence (the Shi–Tomasi algorithm) and constructing a height matrix; (3) detecting the safety line intersections by constructing a technical position map of the support relative to the roadheader with virtual boundaries (the Clamp/Rake functions). As a result, (1) 3D height profiles in pixels were obtained; (2) detection of the roadheader crossing the safety line from left and right was implemented; (3) a generalized structural diagram of the methods for determining the walking roof support position was presented. A combination of the developed machine vision techniques and methods enables the automatic control system of the walking roof support to move and maneuver at the face taking into account the current situation. The efficiency is achieved through optimization of algorithms that have relatively low demand for computational resources of the onboard computers.

Keywords: roof support, walking roof support, development workings, autonomy, automated control, machine vision, pattern recognition, light marker, laser grid

Acknowledgements: The work was carried out within the framework of the state assignment of the Federal Research Center for Coal and Coal Chemistry of the Siberian Branch of the Russian Academy of Sciences, project FWEZ-2024-0025 “Development of scientific foundations for the creation of autonomous and automated mining machines, equipment, technical and control systems based on promising digital and robotic technologies (prolongation)”.

For citation: Nikitenko M.S., Khudonogov D.Yu., Kizilov S.A. Approaches to position determination of powered walking support in a mine working. Russian Mining Industry. 2026;(4):147–152. https://doi.org/10.30686/1609-9192-2026-4-147-152

Article info

Received: 19.03.2026

Revised: 21.05.2026

Accepted: 08.06.2026

Information about authors

Mikhail S. Nikitenko – Cand. Sci. (Eng.), Associate Professor, Head of Laboratory of Advanced Methods of Mining Systems Control, Federal Research Center of Coal and Coal Chemistry of Siberian Branch of the Russian Academy of Sciences, Kemerovo, Russian Federation; https://orcid.org/0000-0001-8752-1332; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Danila Yu. Khudonogov – Researcher, Federal Research Center of Coal and Coal Chemistry of Siberian Branch of the Russian Academy of Sciences, Kemerovo, Russian Federation; https://orcid.org/0000-0002-0208-790X; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Sergey A. Kizilov – Researcher, Federal Research Center of Coal and Coal Chemistry of Siberian Branch of the Russian Academy of Sciences, Kemerovo, Russian Federation; https://orcid.org/0000-0003-2554-1383; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

References

1. Pu Y. An analysis of laser distance measuring by different laser rangefinders. Theoretical and Natural Science. 2024;38:235–239. https://doi.org/10.54254/2753-8818/38/20240585

2. Bui X.-N., Drebenstedt C., Nguyen H., Duc N.V., Hieu T.Q., Long N.Q., Ninh L.N. Smart mining - opportunities and challenges for Vietnam’s mining industry. In: Hai T.T. (ed.) Proceedings of the International Workshop on Advances in Surface Mining for Environmental Protection and Sustainable Development-2025 (ASMES-2025), Hanoi, Vietnam, October 17, 2025. Hanoi: Transport Publishing House; 2025, pp. 24–41.

3. Yu X., Marinov M. A Study on recent developments and issues with obstacle detection systems for automated vehicles. Sustainability. 2020;12(8):3281. https://doi.org/10.3390/su12083281

4. Sonawane M.G., Patel N.S. Review of obstacle detection by ultrasonic and laser sensor for automated guided vehicles. In: Pawar P.M. et al. (eds) Techno-societal 2022: Proceedings of the 4th International Conference on Advanced Technologies for Societal Applications. Cham: Springer; 2024. Vol. 1, pp. 985–992. https://doi.org/10.1007/978-3-031-34644-6_101

5. Dunn M., Reid P., Malos J. Development of a protective enclosure for remote sensing applications – case study: laser scanning in underground coal mines. Resources. 2020;9(5):56. https://doi.org/10.3390/resources9050056

6. Tampier C., Mascaró M., Ruiz-del-Solar J. Autonomous loading system for load-haul-dump (LHD) machines used in underground mining. Applied Sciences. 2021;11(18):8718. https://doi.org/10.3390/app11188718

7. Ren Z., Wang L., Bi L. Robust GICP-Based 3D LiDAR SLAM for underground mining environment. Sensors. 2019;19(13):2915. https://doi.org/10.3390/s19132915

8. Bogdanova K.A. Application of SLAM algorithms for three-dimensional modelling of mine workings. Izvestiya Tulskogo Gosudarstvennogo Universiteta. Tekhnicheskie Nauki. 2021;(2):134–140. (In Russ.)

9. Memon S., Arain R.H., Mallah G.A., Rehman S., Barkat J., Siddiqui M.A. A review on deep learning-based approaches for image dehazing. Spectrum of Engineering Sciences. 2024;2(3):310–328.

10. Su Y., Bu W., Chen J., Li S., Liu M. Design of the autonomous path planning system for mining robots based on stereo vision. In: In: Liu XJ., Nie Z., Yu J., Xie F., Song R. (eds) Proceedings of the 4th International Conference on Intelligent Robotics and Applications (ICIRA 2021), Yantai, China, October 22–25, 2021. Cham: Springer; 2021. Part 4, pp. 596–605. https://doi.org/10.1007/978-3-030-89092-6_54

11. Khramov V.V. A method of describing objects in a scene of a robot vision system by means of fuzzy Delaunay triangulation using an adaptive mesh. Modern Information Technologies and IT-Education. 2020;16(4):833–840. (In Russ.) https://doi.org/10.25559/SITITO.16.202004.833-840

12. Kramarov S.O., Mityasova O.Yu., Temkin I.O., Khramov V.V. Delaunay triangulation-based methodology of intelligent navigation and control of mobile objects. Mining Informational and Analytical Bulletin. 2021;(2):87–98. (In Russ.) https://doi.org/10.25018/0236-1493-2021-2-0-87-98

13. Lupenko N., Bohush R., Chen H. Analysis of methods for distance estimation to an object from a single video camera image using neural networks. Vestnik of Polotsk State University. Part C. Fundamental Sciences. 2024;(2):24–33. (In Russ.) https://doi.org/10.52928/2070-1624-2024-43-2-24-33

14. Duginov D.E. Identification of object dimensions using a reference object. Science Bulletin. 2021;2(1):52–54. (In Russ.) Available at: https://www.вестник-науки.рф/article/3993 (accessed: 28.03.2026).

15. Mustafah Y.M., Azman A.W., Ani M.H. Object distance and size measurement using stereo vision system. Advanced Materials Research. 2012;622-623:1373–1377. https://doi.org/10.4028/www.scientific.net/amr.622-623.1373

16. Du Y., Zhang H., Liang L., Zhang J., Song B. Applications of machine vision in coal mine fully mechanized tunneling faces: a review. IEEE Access. 2023;11:102871–102898. https://doi.org/10.1109/ACCESS.2023.3317048

17. Cherkasov P.V., Nikitenko M.S., Kizilov S.A. Substantiation of the parameters of the stand for measuring the volume of produced rock mass using machine vision and projection of a grid of laser lines. Naukoemkie Tekhnologii Razrabotki i Ispolzovaniya Mineralnykh Resursov. 2024;(10):120–126. (In Russ.)

18. Shi J., Tomasi Good features to track. In: 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, June 1–23, 1994. IEEE; 1994, pp. 593–600. https://doi.org/10.1109/CVPR.1994.323794

19. Mu Z., Li Z. A novel shi-tomasi corner detection algorithm based on progressive probabilistic Hough transform. In: 2018 Chinese Automation Congress (CAC), Xi’an, China, November 30 – December 2, 2018. IEEE; 2018, pp. 2918–2922. https://doi.org/10.1109/CAC.2018.8623648