Pashko A.D., Velikanov V.S. Development of the Concept of Situation-Adaptive Mobile Autonomous Platforms for Confined Spaces in the Mining And Metallurgical Industry

DOI: 10.18503/1995-2732-2026-24-3-5-13

Abstract

In the mining and metallurgical industry (MMI), operations in confined spaces (underground workings, process equipment, and utility systems) are associated with a high incidence of occupational injuries (mortality rates 3-5 times higher than the industrial average) and significant economic losses due to equipment downtime (20-30% of the available operating time). Existing robotic solutions are insufficiently adapted to conditions characterized by sensor-deprived environments, high dust levels, and dynamic changes in the operating environment. The aim of this study is to develop the conceptual foundations, architecture, and scientific and methodological framework (including formal models) for the creation of situation-adaptive mobile autonomous platforms (SAMPs) designed to perform tasks safely and efficiently in confined spaces within the MMI. The research methods include a systematic analysis of occupational injury statistics and economic losses, the Failure Mode and Effects Analysis (FMEA) methodology for formalizing requirements, simulation modeling using digital twins (Unity and Unreal Engine with PhysX), laboratory prototyping on “artificial mine” and “thermobaric chamber” test benches, and field testing at industrial facilities. The scientific novelty lies in proposing the concept of SAMPs based on the principles of modularity, contextual autonomy, and deep cyber-physical integration; developing a hybrid SLAM model for navigation in sensor-deprived environments (a positioning error of 0.8m over 500m compared with 4.2m when using lidar alone); and developing an energy consumption model that optimizes fuel costs and battery degradation. As a result, typical confined spaces in the MMI have been systematized and requirements for the platforms have been formalized. It is demonstrated that the implementation of SAMPs can reduce occupational injuries in target operations by 70-90%, increase productivity by 15-25%, and reduce repair costs by 25-40%. The payback period for a fleet of five platforms is estimated at 2.5-3 years. The practical significance of the study lies in providing MMI enterprises with a structured methodology for transitioning to robotic execution of operations in confined spaces. Further development should include experimental validation using full-scale prototypes, development of self-learning algorithms for semantic defect analysis, and the establishment of industry-specific standards for data exchange with digital twins.

Keywords

mobile autonomous platform, confined space, mining and metallurgical industry, situation adaptivity, mathematical model, total cost of ownership, industrial safety

For citation

Pashko A.D., Velikanov V.S., Bulganina M.Yu. Development of the Concept of Situation-Adaptive Mobile Autonomous Platforms for Confined Spaces in the Mining and Metallurgical Industry. Vestnik Magnitogorskogo Gosudarstvennogo Tekhnicheskogo Universiteta im. G.I. Nosova [Vestnik of Nosov Magnitogorsk State Technical University]. 2026, vol. 24, no. 3, pp. 5-13. https://doi.org/10.18503/1995-2732-2026-24-3-5-13

Alexey D. Pashko – PhD (Eng.), Associate Professor of the Department of Automation and Computer Technology, Ural State Mining University, Ekaterinburg, Russia. Email: This email address is being protected from spambots. You need JavaScript enabled to view it.. ORCID 0009-0008-3296-5014

Vladimir S. Velikanov – Dr. Sci. (Eng.), Professor, Department of Hoisting and Hauling Machines and Robots, Ural Federal University named after the first President of Russia B.N. Yeltsin, Ekaterinburg, Russia; Professor, Department of Automatics and Computer Technologies, Ural State Mining University, Ekaterinburg, Russia. Email: This email address is being protected from spambots. You need JavaScript enabled to view it.. ORCID 0000-0001-5581-2733

Marina Yu. Bulganina – PhD (Eng.), Docent, Department of Metallurgical and Rotary Machines, Ural Federal University named after the first President of Russia B.N. Yeltsin, Ekaterinburg, Russia. Email: This email address is being protected from spambots. You need JavaScript enabled to view it.. ORCID 0009-0009-7833-7846

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