Monitoring the current trajectories of autonomous heavy platforms moving along the quarry routes of mining enterprises

DOI: http://dx.doi.org/10.30686/1609-9192-2021-5-76-83
I.V. Chicherin, B.A. Fedosenkov, D.M. Dubinkin
T.F. Gorbachev Kuzbass State Technical University, Kemerovo, Russian Federation
Russian Mining Industry №5 / 2021 р. 76-83Читать на русскоя языке

Abstract: In order to obtain information about the generated current trajectories (CT) of unmanned mining dump trucks, in the software and hardware complexes of the computer-aided dispatching system (in the external control subsystem and the autonomous control subsystem) installed on-board of an (AHP), one-dimensional (scalar) continuous signals (hereinafter converted into discrete digital ones) with a time-dependent instantaneous frequency, the so-called chirp signals, are put in accordance with the current trajectories of the AHP. This approach makes it possible to continuously monitor and manage the dynamics of current AHP trajectories with a high degree of efficiency. Note that for the purpose of information-rich and semantically transparent representation of information about the current state of the AHP CT, the chirp signals of the CT are converted into multidimensional Cohen’s class time-frequency wavelet distributions. The Wigner-Ville distribution (hereinafter referred to as the Wigner distribution) is selected as a working tool for performing computational procedures in the hardware / software module. This distribution is based on the Gabor basis wavelet functions and the wavelet matching pursuit algorithm. The choice of Gabor wavelets as the main ones is explained by their sinusoidal-like shape, since they are sinusoidal signals modulated by the Gauss window. On the other hand, the analyzed 1D-signals indicating the current position of the AHP on the route are also sinusoidal-like. This makes it possible to approximate current signals with high accuracy based on their comparison with the wavelet functions selected from the redundant wavelet dictionary. This approximation is adaptive, since it is performed on separate local fragments of the signal analyzed depending on approximating wavelets. This is the essence of the wavelet matching pursuit algorithm. The resulting wavelet series is then transformed into the Wigner time-frequency distribution, which is used to form a corresponding CT. As an example, reconstructions of time-frequency distributions (TFD) are given, corresponding to the deviation of a certain CT to the left (the trajectory signal decreases exponentially) and to the right (the CT-signal increases) from the nominal axial trajectory (NAT). The calculated scalar signal and its TFD for the AHP CT deviating to the left from NAT are also presented. In addition, on the basis of theoretical explanations the calculated linear-increasing TFD is demonstrated, corresponding to the CT-deviation to the right from NAT, and the time invariant stationary TFD characterizing the movement of AHP along the NAT line. In conclusion, based on the results obtained, it is concluded that the most appropriate ways to monitor the current trajectories of AHP movement and procedures for processing the corresponding signals are the operations implemented in computer-aided subsystems of external and autonomous control and based on such concepts as the Cohen’s class wavelet distributions, Gabor redundant dictionary of wavelet functions, the wavelet matching pursuit algorithm, and the representation of technological chirp-signals, as well as frequency-stationary signals about the current AHP trajectories represented in the wavelet medium. In this connection, the authors concluded that the procedures realizing the current monitoring of AHP movement on open pit mine routes and implementing the process of analyzing a relevant dynamic change in current trajectories, described in the article and embedded in software and hardware autonomous and external control subsystems of “Smart quarry” are adequate for performing required functions. The introduction of the principles of computer-aided controlling the unmanned mining vehicles allows you to optimize labor costs for the operation of mining equipment, reduce the cost of current work, and attract highly qualified specialists for the development and operation of innovative transport equipment.

Keywords: autonomous heavy platforms, current trajectories, Gabor wavelets, wavelet matching pursuit algorithm, chirp signals, Cohen’s class distributions

Acknowledgments: The work was financially supported by the Ministry of Science and Higher Education of the Russian Federation under Agreement No. 075-11-2019-034 dated 22.11.2019 with KAMAZ PTC on the ‘Development and creation of hightech production of autonomous heavy platforms for unmanned mining operations within the Smart Open-Pit system’ Integrated Project with the participation of the T.F. Gorbachev Kuzbass State Technical University with respect to research, development and technological activities. The authors express their gratitude to the staff of the T.F. Gorbachev Kuzbass State Technical University (Institute of Information Technologies, Mechanical Engineering, and Motor Transport) in promoting to place the article manuscript in the periodical editorial office.

For citation: Shestakov K.I., Sokolov I.M., Pirogov M.A., Solovyov S.G. Monitoring the current trajectories of autonomous heavy platforms moving along the quarry routes of mining enterprises. Gornaya promyshlennost = Russian Mining Industry. 2021;(5):76–83. (In Russ.) DOI: 10.30686/1609-9192-2021-5-76-83.

Article info

Received: 19.08.2021

Revised: 15.09.2021

Accepted: 16.09.2021

Information about the authors

Ivan V. Chicherin – Candidate of Technical Sciences, Associate Professor, Head of the Department of Information and Computeraided Manufacturing Systems, T.F. Gorbachev Kuzbass State Technical University, Kemerovo, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Boris A. Fedosenkov – Professor, Doctor of Technical Sciences (Dr. Sc. – Engineering), Professor of the Department of Information and Computer-aided Manufacturing Systems, T.F. Gorbachev Kuzbass State Technical University, Kemerovo, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Dmitry M. Dubinkin – Candidate of Technical Sciences, Associate Professor, Associate Professor of The Metal Cutting Machines and Tools Department, T.F. Gorbachev Kuzbass State Technical University, Kemerovo, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

References

1. Chicherin I.V., Fedosenkov B.A., Syrkin I.S., Sadovets V.Iu., Dubinkin D.M. The concept of controlling the unmanned vehicles in open pit mining. Izvestiya vysshikh uchebnykh zavedenii. Gornyi zhurnal = News of the Higher Institutions. Mining Journal. 2020;(8):109–120. (In Russ.) https://doi.org/10.21440/0536-1028-2020-8-109-120

2. Aleshin B.S., Chernomorskiy A.I., Kuris E.D. Ground vehicle modules for solving the tasks of environmental monitoring in the mountainous areas. Ustoichivoe razvitie gornykh territorii = Sustainable Development of Mountain Territories. 2018;10(4):566–576. (In Russ.) https://doi.org/10.21177/1998-4502-2018-10-4-566-576

3. Dubinkin D.M. Justification of the need to create heavy platforms for open-pit mining. Gornoe oborudovanie i elektromekhanika = Mining Equipment and Electromechanics. 2020;(4):59–64. (In Russ.) https://doi.org/10.26730/1816-4528-2020-4-59-64

4. Dubinkin D.M. Current state of technics and technologies in the field of autonomous control movement vehicles of coal mine caree Gornoe oborudovanie i elektromekhanika = Mining Equipment and Electromechanics. 2019;(6):8–15. (In Russ.) https://doi.org/10.26730/1816-4528-2019-6-8-15

5. Voronov Yu., Voronov A., Grishin S., Bujankin A. Increasing the technical level of mining haul trucks. In: Tyulenev M., Zhironkin S., Khoreshok A., Vöth S., Cehlár M., Nuray D. (eds) E3S Web of Conferences. The Second International Innovative Mining Symposium (Devoted to Russian Federation Year of Environment), Kemerovo, November 20–22, 2017. 2017;21:03015. https://doi.org/10.1051/e3sconf/20172103015

6. Naranjo J.E., Clavijo M., Jimenez F., Gomez O., Rivera J.L., Anguita M. Autonomous vehicle for surveillance missions in off-road environment. In: IEEE Intelligent Vehicles Symposium (IV), Gotenburg, Sweden, June 19–22, 2016, pp. 98–103. https://doi.org/10.1109/IVS.2016.7535371

7. Nonami K., Kartidjo M., Yoon K.-J., Budiyono A. (eds) Autonomous Control Systems and Vehicles. Intelligent Unmanned Systems. Springer; 2013. 315 p. https://doi.org/10.1007/978-4-431-54276-6

8. Cheng H. Autonomous Intelligent Vehicles. Theory, Algorithms, and Implementation. Springer-Verlag London; 2011. 154 p. https://doi.org/10.1007/978-1-4471-2280-7

9. Taxonomy and Definitions for Terms Related to Driving Automation Systems for OnRoad Motor Vehicles. SAE International in United States; 2016. 30 p. https://doi.org/10.4271/j3016_201609

10. Shadrin S.S. Methodology of autonomous road vehicles, integrated in intellectual transport environment, driving control systems design: Dissert. Dr. Sc. (Engineering). 05.05.03 [Bauman State Technical University]. Moscow; 2017. 400 p. (In Russ.)

11. Shadrin S.S., Varlamov O.O., Ivanov A.M. Experimental autonomous road vehicle with logical artificial intelligence. Journal of Advanced Transportation. 2017:2492765. https://doi.org/10.1155/2017/2492765

12. Saykin A.M., Buznikov S.E., Karpukhin K.E. The analysis of technical vision problems typical for driverless vehicles. Research Journal of Pharmaceutical, Biological and Chemical Sciences. 2016;7(4):2053–2059. Available at: http://www.rjpbcs.com/pdf/2016_7(4)/[263].pdf

13. Mallat S. A wavelet tour of signal processing. 2nd ed. Academic Press, Springer; 1999. 664 p. Available at: https://www.elsevier.com/books/a-wavelet-tour-of-signal-processing/mallat/978-0-12-466606-1

14. Daubechies I. Ten lectures on wavelets. Philadelphia: Society for Industrial and Applied Mathematics (SIAM); 1992. 357 p.

15. Auger F., Chassande-Mottin E. Quadratic time-frequency analysis I: Cohen’s class. In: Timefrequency analysis: concepts and methods. ISTE; 2008, pp. 131–163.

16. Fedosenkov D.B., Simikova A.A., Kulakov S.M., Fedosenkov B.A. Cohen's class time-frequency distributions for measurement signals as a means of monitoring technological processes. Steel in Translation. 2019;49(4):252–256. https://doi.org/10.3103/S0967091219040065

17. Debnath L. Recent Developments in the Wigner-Ville Distribution and Time-Frequency Signal Analysis. PINSA. 2002;68(1):35–36.

18. Fedosenkov B.A., Fedosenkov D.B. Automated control of mixture preparation processes in wavelet medium. Kemerovo: Kemerovo Technological Institute of Food Industry; 2015. 188 p. (In Russ.)

19. Mallat S, Zhang Z. Matching pursuits with time-frequency dictionaries. IEEE Transactions on Signal Processing. 1993;41(12):3397– 3415. https://doi.org/10.1109/78.258082

20. Boashash B., Touati S., Auger F., Flandrin P., Chassande-Mottin E., Stanković L.J. et al. Measures, performance assessment, and enhancement TFDs. In: Boashash B. (ed.) Time-Frequency Signal Analysis and Processing. 2nd ed. Academic Press, Elsevier; 2016. Chapter 7, pp. 387–452. https://doi.org/10.1016/B978-0-12-398499-9.00007-8

21. Townsend S., Lee B. Sparse approximation and atomic decomposition: Considering atom interactions in evaluating and building signal representations. Dissertation … PhD. 2009. 260 p.

22. Fedosenkov D.B., Simikova A.A., Fedosenkov B.A. The synthesis of the modal system for controlling the feeding devices unit. In: Zhukov I.A. (ed.) Mechanical Engineering: Innovative Aspects of Development: Proceedings of the 2nd International Scientific-Practical Conference, St. Petersburg, March 15, 2019. St. Petersburg: SPbB SRC MS; 2019, pp. 162–166. (In Russ.) https://doi.org/10.26160/2618-6810-2019-2-162-166