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ArticleName On some aspects of increasing the target productivity of unmanned mine dump trucks
DOI 10.17580/em.2021.02.15
ArticleAuthor Sizemov D. N., Temkin I. O., Deryabin S. A., Vladimirov D. Ya.
ArticleAuthorData

VIST Group LLC, Moscow, Russia:

Sizemov D. N., Technical Advisor to CEO, Candidate of Engineering Sciences

 

National University of Science and Technology MISiS, Moscow, Russia:
Temkin I. O., Head of Department of Automated Control Systems, Doctor of Engineering Sciences, temkin.io@misis.ru
Deryabin S. A., Head of Laboratory at the Department of Automated Control Systems

 

Zyfra Group, Moscow, Russia:
Vladimirov D. Ya., Deputy General Director, Candidate of Engineering Sciences

Abstract

This article considers one of the approaches to solving the problem of improving the efficiency of the functioning of unmanned open pit transport. The actual data on the movements of robotic dump trucks within the framework of a continuous transport and technological cycle at one of mining sites of a coal mine are analyzed. During the study, the movement times in the loaded and empty states are recorded. In addition, the time of passing by dump trucks of individual sections of the transport route is monitored, in order to empirically determine the speed reserves for each robot. As a result, several options have been obtained to increase the target performance of an autonomous dump truck by changing the speed modes of movement in certain sections. One of the variants is presented in the paper as an illustrative example. The paper also briefly discusses possible approaches to formalizing the procedure for determining the optimal driving modes of robotic dump trucks, depending on the terrain and features of the route as well as the roadbed condition.

The work has been implemented by support of the Russian Science Foundation grant; project No.19-17-00184.

keywords Robotic dump truck, digital transformation, optimization models, open-pit mining, Industry 4.0, quarry, route segments, unmanned mining transport systems, robot target productivity, autonomous haulage systems
References

1. Oxborrow J. R. Implementation of an Autonomous Haulage System on retrofitted haul trucks at Nevada Gold Mines Goldstrike operations. SME Annual Conference and Expo2020. 2020. 159172.
2. Bhattacharyya S. S., Shah Y. Emerging technologies in Indian mining industry: an exploratory empirical investigation regarding the adoption challenges. Journal of Science and Technology Policy Management. 2021. DOI: 10.1108/JSTPM-03-2021–0048
3. Lukichev S. V., Nagovitsin O. V. Digital transformation of mining industry: Past, Present and Future. Gornyi Zhurnal. 2020. No. 9. pp. 13–18. DOI: 10.17580/gzh.2020.09.01
4. Ishimoto H., Hamada T. Safety Concept and Architecture for Autonomous Haulage System in Mining. Proceedings of the 37th International Symposium on Automation and Robotics in Construction. 2020. pp. 377–384. DOI: 10.22260/isarc2020/0054
5. Vladimirov D. Y., Klebanov A. F., Kuznetsov I. V. Digital transformation of surface mining and new generation of open-pit equipment. Gornaja Promyshlennost. 2020. No. 6. pp. 10–12.
6. Sobolev A. A. Review of case history of unmanned dump trucks. Gornyi Zhurnal. 2020. No. 4. pp. 51–55. DOI:
10.17580/gzh.2020.04.10
7. Gaber T., Jazouli Y. E., Eldesouky E., Ali A. Autonomous haulage systems in the mining industry: cybersecurity, communication and safety issues and challenges. Electronics. 2021. Vol. 10(11). DOI: 10.3390/electronics10111357
8. Akbari A., Bernardini S. Informed autonomous exploration of subterranean environments. IEEE Robotics and Automation Letters. 2021. Vol. 6(4). pp. 7957–7964.
9. Malavolta I., Lewis G. A., Schmerl B., Lago P., Garlan D. Mining guidelines for architecting robotics software. Journal of Systems and Software. 2021. Vol. 178(2). DOI: 10.1016/j.jss.2021.110969
10. Price R., Cornelius M., Burnside L., Miller B. Mine planning and selection of autonomous trucks. Springer Series in Geomechanics and Geoengineering. 2020. pp. 203–212. DOI: 10.1007/978-3-030-33954-8_26
11. Ali D., Frimpong S. Artificial intelligence, machine learning and process automation: existing knowledge frontier and way forward for mining sector. Artificial Intelligence Review. 2020. Vol. 53(8). pp. 6025–6042.
12. Androulakis V., Sottile J., Schafrik S., Agioutantis Z. Concepts for Development of Autonomous Coal Mine Shuttle Cars. IEEE Transactions on Industry Applications. 2020. Vol. 56(3). pp. 3272–3280.
13. Klebanov A. F. Automation and robotization in opencast mining: experience in digital transformation. Gornaya Promyshlennost. 2020. No. 1. pp. 8–11.
14. Voronov Y., Voronov A., Makhambayev D. Current State and Development Prospects of Autonomous Haulage at Surface Mines. E3S Web of Conferences. 2020. DOI: 10.1051/e3sconf/202017401028
15. Nagovitsyn O. V., Voznyak M. G. Robotic mine management. GIAB. 2021. No. 5(1). pp. 326–335.
16. Paredes D., Fleming-Muñoz D. Automation and robotics in mining: Jobs, income and inequality implications. The Extractive Industries and Society. 2021. Vol. 8(1). pp. 189–193.
17. Temkin I., Myaskov A., Deryabin S., Konov I., Ivannikov A. Design of a Digital 3D Model of Transport–Technological Environment of Open-Pit Mines Based on the Common Use of Telemetric and Geospatial Information. Sensors. 2021. No. 21(18). DOI: 10.3390/s21186277
18. Zinoviev V. V., Starodubov A. N., Nikolaev P. I. Modular system of simulation modeling of conventional and robotic mining technologies. IOP Conference Series: Earth and Environmental Science. 2021. No. 773(117). 0120672019. DOI: 10.1088/1755-1315/773/1/012067
19. Ristovski K., Gupta C., Harada K., Tang H. K. Dispatch with confidence: Integration of machine learning, optimization and simulation for open pit mines. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017. pp. 1981–1989. DOI: 10.1145/3097983.3098178
20. Sun X., Zhang H., Tian F., Yang L. The Use of a Machine Learning Method to Predict the Real-Time Link Travel Time of Open-Pit Trucks. Mathematical Problems in Engineering. 2018. pp. 1–14. DOI: 10.1155/2018/4368045
21. Temkin I. O., Klebanov D. A., Deryabin S. A., Konov I. S. Haul road condition determination under controlled interaction of robotic elements in open pit mining and transport system. Gornyi Zhurnal. 2018. No. 1. pp. 78–82. DOI: 10.17580/gzh.2018.01.14
22. Ali D., Frimpong S. DeepHaul: a deep learning and reinforcement learning-based smart automation framework for dump trucks. Progress in Artificial Intelligence. 2021. No. 10(2). pp. 157–180.
23. Temkin I. O., Myaskov A. V., Deryabin S. A., Rzazade U. A. Digital twins and modeling of the transporting-technological processes for on-line dispatch control in open pit mining. Eurasian Mining. 2020. No. 2. pp. 55–58. DOI: 10.17580/em.2020.02.13

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