National University of Science and Technology—NUST MISIS, Moscow, Russia

Temkin I. O., Head of Department, Doctor of Engineering Sciences, temkin.io@misis.ru
Deryabin S. A., Senior Lecturer

Konov I. S., Associate Professor, Candidate of Engineering Sciences

National University of Science and Technology—NUST MISIS, Moscow, Russia¹ ; Al-Furat AL-Awast Technical University, Kufa, Iraq²
Al-Saeedi A. A. К.^1,2, Post-Graduate Student, Assistant lecturer

This article discusses the issues of path planning for movement of an autonomous heavy-duty dump truck along haul roads in an open pit mine. Taking into account the heterogeneity and dynamic nature of the technological environment, the issues of the efficiency and quality of the path planning of are extremely relevant. As a basis for calculations, a digital open pit model is considered, with technological zones and haul roads described as their geometric surfaces fragmented into same-type regular polygons. In this case, the workspace represents a set of discrete points with specified coordinates — the centers of geometric primitives, and the segments connecting them represent the possible directions of travel of an autonomous dump truck. The combinations of these segments, which are assigned certain weights, form a sequence of reference points of a potential route for the robots to move along. Thus, the planning objective in this study is formulated as the search for the optimal value of a certain cost function that takes into account condition of a roadway. The procedure for constructing a graph which acts as a basic model for determining the best route for a mobile robot is briefly discussed. As an algorithm for finding the optimal sequence of points, it is proposed to use a modification of reliable Dijkstra’s algorithm, with the running speed being increased owing to the original implementation of parallel computing. In terms of the real-life fragments of haul roads, using the predefined terminal points and the constructed graphs of high dimensionality, the edge weights of which determine the values of the cost function, various routes for dump trucks are constructed and studied. The modeling results confirmed the efficiency of the proposed approach while limiting the use of standard computing resources.

1. Rubio F., Valero F., Llopis-Albert C. A review of mobile robots: Concepts, methods, theoretical framework, and applications. International Journal of Advanced Robotic Systems. 2019. Vol. 16, Iss. 2. DOI: 10.1177/1729881419839596
2. Lukichev S. V., Nagovitsin O. V. Digital transformation of mining industry: Past, present and future. Gornyi Zhurnal. 2020. No. 9. pp. 13–18.
3. Vladimirov D. Ya., Klebanov A. F., Kuznetsov I. V. Digital transformation of surface mining and new generation of open-pit equipment. Mining Industry Journal. 2020. No. 6. pp. 10–12.
4. Sobolev А. А. Review of case history of unmanned dump trucks. Gornyi Zhurnal. 2020. No. 4. pp. 51–55.
5. Fang Y., Peng X. Micro-factors-aware scheduling of multiple autonomous trucks in open-pit mining via enhanced metaheuristics. Electronics. 2023. Vol. 12, Iss. 18. ID 3793.
6. Kansake B., Frimpong S., Ali D. Multi-body dynamic modelling of ultra-large dump truck-haul road interactions towards haul road design integrity. International Journal of Mining, Reclamation and Environment. 2019. Vol. 34, Iss. 9. pp. 649–671.
7. Nagovitsyn O. V., Voznyak M. G. Impact of robotic technologies on open mining safety. MIAB. 2022. No. 12(1). pp. 52–62.
8. Sizemov D. N., Temkin I. O., Deryabin S. A., Vladimirov D. Y. On some aspects of increasing the target productivity of unmanned mine dump trucks. Eurasian Mining. 2021. No. 2. pp. 68–73.
9. 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.
10. Chernyshova Y. S., Savelyev B. I., Solodov S. V., Pronichkin S. V. Applying distributed ledger technologies in megacities to face anthropogenic burden challenges. IOP Conference Series: Earth and Environmental Science. 2022. Vol. 1069(1). ID 012028
11. 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. Vol. 21, Iss. 18. ID 6277.
12. Hsu D., Latombe, J. C., Motwani R. Path planning in expansive configuration spaces. International Journal of Computational Geometry & Applications. 1999. Vol. 9, No. 04n05. pp. 495–512.
13. Xiang S., Gao H., Liu Z., Gosselin C. Dynamic point-to-point trajectory planning for three degrees-of-freedom cable-suspended parallel robots using rapidly exploring random tree search. Journal of Mechanisms and Robotic. 2020. Vol. 12, Iss. 4. ID 041007.
14. Jasika N., Alispahic N., Elma A. et al. Dijkstra’s shortest path algorithm serial and parallel execution performance analysis. MIPRO 2012 Proceedings of the 35^th International Convention, IEEE. 2012. pp. 1811–1815.
15. Gunawan D., Napianto R., Borman R. I., Hanifah I. Implementation of Dijkstra’s algorithm in determining the shortest path (Case study: Specialist doctor search in Bandar Lampung). International Journal of Information System and Computer Science. 2019. Vol. 3(3). pp. 98–106.
16. Buzachis A., Celesti A., Galletta A., Wan J., Fazio M. Evaluating an application aware distributed Dijkstra shortest path algorithm in hybrid cloud/edge environments. IEEE Transactions on Sustainable Computing. 2022. Vol. 7, Iss. 2. pp. 289–298.
17. Awari R. Parallelization of shortest path algorithm using OpenMP and MPI. International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud). 2017. pp. 304–309. DOI: 10.1109/I-SMAC.2017.8058360
18. Huang L., Wang L., Shao J. et al. Parallel processing transport model MT3DMS by using OpenMP. International Journal of Environmental Research and Public Health. 2018. Vol. 15, Iss. 6. ID 1063.
19. Prasad A., Krishnamurthy S. K., Kim Y. Acceleration of Dijkstra’s algorithm on multi-core processors. ICEIC: 2018 International Conference on Electronics, Information, and Communication. 2018. DOI: 10.23919/ELINFOCOM.2018.8330701
20. Al-Saeedi A. A. K., Shamaeva O. Y., Khairi T. W. Improving the efficiency of sparse matrix class processing by using the SPM-CSR parallel algorithm and OpenMP technology. REEPE: 2022 4^th International Youth Conference on Radio Electronics, Electrical and Power Engineering. 2022. DOI: 10.1109/REEPE53907.2022.9731504