SIMULATION MODEL OF UNMANNED AERIAL VEHICLE MAINTENANCE

Autores/as

DOI:

https://doi.org/10.18623/rvd.v23.6637

Palabras clave:

UAV, AnyLogic, Simulation Modeling, Maintenance

Resumen

A review of modern methods and approaches to UAV research is provided. In the context of the digital transformation of the maintenance process, the advantages of using the AnyLogic multi-approach environment for solving operational reliability problems are substantiated. The integration of AnyLogic discrete-event components for describing maintenance processes and agent-based logic for representing the life cycle of each individual drone allows for highly accurate prediction of service infrastructure bottlenecks and minimization of aircraft system downtime under intensive operating conditions. This paper explores the use of the AnyLogic environment to optimize maintenance processes and improve the operational readiness of aircraft. A comprehensive simulation model of the maintenance system is proposed, combining a discrete-event approach and agent-based modeling. This paper explores the use of the AnyLogic environment to optimize maintenance processes and improve the combat readiness of aircraft. A comprehensive simulation model of the maintenance system is proposed, combining a discrete-event approach and agent-based modeling. Algorithms for dynamic resource management of repair units are implemented. The model allows for assessing UAV fleet availability factors based on preventive maintenance strategies and onboard equipment failure rates, and determining resource allocation (technical personnel, equipment, and parts) under both scheduled and predictive maintenance conditions. It can be used to select maintenance strategies, manage resources, forecast operational availability, and optimize maintenance schedules.

Citas

Ahmad, S., Ahmad, R., Al-Shamayleh, A., Nimma, D., Zaman, M., Ivkovic, N., Cengiz, K., Akhunzada, Ad., & Haider, E. (2025). Flight into the future: a holistic review of AI-trends, vision, and challenges in drones technology. International Journal of Dynamics and Control, 59(59). https://doi.org/10.1007/s10462-025-11449-7

Alobaidy, M., Al-Turjman, F., & Mostarda, L. (2025). Post-quantum cryptography for secure UAV communications: A comprehensive survey. Journal of Network and Computer Applications, 234, 103982. https://doi.org/10.1016/j.jnca.2024.103982

Avramchikov V.M., Timokhovich A.S., Rozhnov I.P. (2024). Digital transformation in the aviation industry: opportunities and prospects. The Eurasian Scientific Journal, 16(3), 04ECVN324. https://esj.today/PDF/04ECVN324.pdf

Barannik, Elena & Abramov, Evgeny & Basyuk, Anatoly & Sushkin, Nikita. (2021). Spoofing Attack Detection Method for UAV Navigation System. Informatics and Automation, 20, 1368-1394. https://doi.org/10.15622/ia.20.6.7.

BlueKei Solutions. (2023). Using Simulation Modeling for Efficient Drone Application in Agriculture. AnyLogic Case Studies. [Electronic resource]. https://www.anylogic.com

Bompilwar, R., Rathor, S. P. S., Sinha, A., & Das, D. (2022). Safe-to-fly: An on-board intelligent fault diagnosis system with AutoML for unmanned aerial vehicles. 2022 IEEE Delhi Section Conference (DELCON), 1–5. https://doi.org/10.1109/DELCON54057.2022.9752852

Borisov, A. S. & Bogachenko, N. F. (2025). Uav routing algorithms in urban environments: current state and prospects. Mathematical Structures and Modeling, 4 (76), 95–108.

Fomichev, A. G. (2024). Digital transformation of management in the aviation industry: challenges and prospects. Humanities, social-economic and social sciences, (4), 254-259. https://doi.org/10.23672/SAE.2024.4.4.032

Hakani, R., & Rawat, A. (2024). Edge Computing-Driven Real-Time Drone Detection Using YOLOv9 and NVIDIA Jetson Nano. Drones, 8(11), 680. https://doi.org/10.3390/drones8110680

Hassan, M., et al. (2024). Blockchain-based secure authentication and key management for Internet of Drones (IoD). IEEE Internet of Things Journal, 11(4), 5678-5695. https://doi.org/10.1016/j.jnca.2024.10398210.1109/JIOT.2023.3321544

https://doi: 10.24147/2222-8772.2025.4.95-108

Kozlov, I. A. (2024). Essence of simulation modeling and prospects for its development. Herald of Science, 1 (77), 119-133. https://elibrary.ru/item.asp?id=68531169

Kozlov, K. V., & Makarenko, S. I. (2024). Algorithms for cryptographic protection of command communication lines for group UAV flights. Systems of Control, Communication and Security, (2), 112-135. https://doi.org/10.1016/j.jnca.2024.10398210.24412/2410-9916-2024-2-112-135

Kulikov A.V. (2024). Creating the simplest structural model of an unmanned aerial vehicle for simulation modeling in the Anylogic environment. Young Scientist, 23 (522), 86-89. https://moluch.ru/archive/522/115384

Liu, Lujie & Yang, Jun. (2023). Dynamic operations and maintenance of an unmanned aerial vehicle swarm for continuous emergency communication. Computers & Industrial Engineering, 184(1), 109564. https://doi.org/10.1016/j.cie.2023.109564

Macer, D. B., Jennions, I. K., & Avdelidis, N. P. (2025). A Review of an Ontology-Based Digital Twin to Enable Condition-Based Maintenance for Aircraft Operations. Applied Sciences, 15(20), 11136. https://doi.org/10.3390/app152011136

Makarenko, S. I., & Kozlov, K. V. (2025). Automated control system for unmanned aerial vehicles when they jointly figure out combat missions. Systems of Control, Communication and Security, (1), 131-155. https://doi.org/10.24412/2410-9916-2025-1-131-155

Moreno-Jacobo, D., Toledo-Nin, G., Ochoa-Zezzatti, A., Torres, V., Estrada-Otero, F. (2021). Evaluation of Drones for Inspection and Control in Industry 4.0. In: Ochoa-Zezzatti, A., Oliva, D., Juan Perez, A. (eds) Technological and Industrial Applications Associated with Intelligent Logistics. Lecture Notes in Intelligent Transportation and Infrastructure. Springer, Cham. https://doi.org/10.1007/978-3-030-68655-0_29

Osco, L. P., Marcato Junior, J., Ramos, A. P. M., Jorge, L. A. de C., Fatholahi, S. N., Silva, J. de A., Matsubara, E. T., Pistori, H., Gonçalves, W. N., & Li, J. (2021). A review on deep learning in UAV remote sensing. International Journal of Applied Earth Observation and Geoinformation, 102, 102456. https://doi.org/10.1016/j.jag.2021.102456

Patel, Aanchal & Cherukuri, Aswani Kumar. (2025). Analysis of Light-Weight Cryptography Algorithms for UAV-Networks. https://doi.org/10.48550/arXiv.2504.04063

Qiao, J., Guo, J., Zhang, Y., & Li, Y. (2026). Intelligent Assessment Framework of Unmanned Air Vehicle Health Status Based on Bayesian Stacking. Batteries, 12(2), 62. https://doi.org/10.3390/batteries12020062

Samulenkov Yu.I., Filatova Ya.A., Gruzd A.D. (2021). Aircraft maintenance system simulation mathematical model construction. Civil Aviation High Technologies, 24(4):38-49. https://doi.org/10.26467/2079-0619-2021-24-4-38-49

Walker, M. (2023, September).Maintenance Workflow Optimization: Streamlining Aircraft Repair Processes. [Conference presentation]. AnyLogic Conference 2023. https://www.anylogic.com

Zhang, L., & Wang, J. (2025). Physical layer security in UAV-assisted wireless networks: Challenges and future trends. IEEE Communications Surveys & Tutorials. https://doi.org/10.1109/COMST.2024.3412098

Zheleznyakov, A. O., Sidorchuk, V. P., & Podrezov, S. N. (2022).Simulation model of the system for maintenance and repair of electronic equipment. Proceedings of the Moscow Aviation Institute, (123). https://doi.org/10.34759/trd-2022-123-26

Zimnikov, D.V. (2023). Simulation model of the maintenance system for complexes with unmanned aerial vehicles. Aerospace MAI Journal, 30(3), 53-58. https://mai.ru/publications/index.php?ID=176873

Descargas

Publicado

2026-05-19

Cómo citar

Baturina, N. (2026). SIMULATION MODEL OF UNMANNED AERIAL VEHICLE MAINTENANCE. Veredas Do Direito, 23(8), e6637. https://doi.org/10.18623/rvd.v23.6637