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  3. Volume 18, 2025
  4. Development of a Method for Optimal Selection of Unmanned Aerial Vehicle Flight Routes

Development of a Method for Optimal Selection of Unmanned Aerial Vehicle Flight Routes

SISN.
2025;
Volume 18
: pp. 76 - 88
Authors:
  1. Ihor Artomov
  2. Dmytro Krytskyi
  3. Alina Artomova
1
National Aerospace University (KhAI), Department of Information Technologies of Design, Kharkiv, Ukraine
2
National Aerospace University (KhAI), Department of Information Technologies of Design, Kharkiv, Ukraine
3
National Aerospace University (KhAI), Department of Information Technologies of Design, Kharkiv, Ukraine

This paper presents an in-depth study of innovative approaches to improving the methodology for flight planning of unmanned aerial vehicles (UAVs), particularly in the context of enhancing route planning efficiency under changing operational factors. For the first time, a comprehensive assessment of the effectiveness of choosing an optimal route among various developed options is conducted, sig- nificantly reducing the time and resources required to complete a mission. One of the main objectives of this research is the integration of advanced optimization algorithms into existing UAV platforms, enabling real-time adaptation to environmental changes.

The paper introduces new approaches for assessing and selecting optimal routes, taking into account complex topographical conditions, dynamic factors such as changing weather, and stringent time and resource constraints. The proposed algorithms employ advanced optimization methods, such as genetic algorithms, artificial neural network-based problem-solving techniques, and adaptive fore- casting strategies. All these tools are integrated with powerful Geographic Information Systems (GIS), significantly improving route accuracy and allowing for dynamic adjustments in real time.

A key aspect of the study is the minimization of energy consumption and flight duration, which is critical for improving efficiency and reducing operational costs for UAVs. The paper justifies why the proposed approaches are more effective than existing methodologies and how they provide significant advantages in various real-world scenarios, such as search operations, environmental monitoring, and cargo delivery.

Comparative analysis shows that the new methods not only achieve better route efficiency but also enhance the ability to adapt to unforeseen changes in environmental conditions. The results of the research may have a significant impact on the future development of UAV software, expanding the possibilities for creating next-generation flight control systems capable of ensuring more reliable and efficient operations in diverse applications.

In the future, real-world testing of the developed models is planned, which will allow for the verification of the effectiveness of the proposed methods in practice and assess their applicability under different operational conditions.

evaluation methodology
route planning
unmanned aerial vehicles (UAVs)
route optimization
flight efficiency
planning algorithms
flight control
  1. Artomova, A. & Artomov, I. (2025). Methodology for Assessing the Efficiency of Generated UAV Flight Route Plans for Optimal Selection. Smart Innovations in Energy and Mechanical Systems. SIEMS 2025. Lecture Notes in Networks and Systems, vol 1480. Springer, Cham. 233−243. https://doi.org/10.1007/978-3-031- 95191-6_22
  2. Artomova, A. & Artomov, I. (2025). Optimization of UAV Routes and Efficiency Enhancement Using Modern IT Technologies. Integrated Computer Technologies in Mechanical Engineering - 2024. ICTM 2024. Lecture Notes in Networks and Systems, vol 1473. Springer, Cham. 219−299. https://doi.org/10.1007/978-3-031- 94845-9_19
  3. Berezhnoy, A. A. & Kryzhanovsky, I. M. (2020). Complex tasks of the decision support system for planning flight routes of unmanned aerial vehicles. Control, navigation and communication systems, 1(59), 3−6. https://doi.org/10.26906/SUNZ.2020.1.003
  4. Berezhnoy, A., Kalacheva, V. & Rozhkov, N. (2019). Simulation of movement of dynamic objects in the system of sub-holding decision-making planning of routes of unmanned aerial vehicles. Information processing systems, 4(159), 44−49. https://journal-hnups.com.ua/index.php/soi/article/view/soi.2019.159.05/731
  5. Chumak, O., Dudko, M., & Dmitriyev, O. (2024). Ontology of movement route planning methods for unmanned aerial vehicles. Testing and Certification, 1(3), 69−77. https://doi.org/10.37701/ts.03.2024.10
  6. Delle Fave, F. M., Xu, Z., Rogers, A. & Jennings, N. R. (2016). Decentralised Coordination of Unmanned Aerial Vehicles for Target Search using the Max-Sum Algorithm. Journal of Autonomous Agents and Multi-Agent Systems, 30(6), 912−946. https://doi.org/10.1109/ICRA.2012.6225053
  7. Druzhinin, E. A., Kovalevsky, M. I., Pogudina, A. K. & Cheranovsky, V. A. (2021). Methods and information technologies of introduction of unmanned aerial vehicles into the airspace of Ukraine. Weapons systems and military equipment, 4(68), 84−90. https://doi.org/10.30748/soivt.2021.68.12
  8. Galinsky, D.O. & Kulish, R.V. (2023). Method of monitoring the state of stationary elements of objects of critical infrastructure by unmanned aerial vehicles using dynamic programming. Control, navigation and communication systems. Control, navigation and communication systems, 1(71), 10−14. https://doi.org/ 10.26906/SUNZ.2023.1.010
  9. Gorbach, V. Ya. & Bondarenko, Yu. L. (2020). Improved methodology for evaluating the effectiveness of the reconnaissance UAV I class flight route plan. Collection of scientific works of Kharkiv National Air Force University, 2(64), 45−52. https://doi.org/10.30748/zhups.2020.64.07
  10. Gulyanitsky, L. F. & Dubina, A. V. (2021). Solving the problem of placing rectangles on a semi-independent tape by local and taboo search algorithms. Scientific Bulletin of Uzhgorod University. Mathematics and Computer Science Series, 38(1), 123−136. https://doi.org/10.24144/2616-7700.2021.38(1)
  11. Jin, Y., Liao, Y., Minai, A. A. & Polycarpou, M. M. (2006). Balancing Search and Target Response in Cooperative Unmanned Aerial Vehicle (UAV) Teams. IEEE Transactions on Cybernetics, 36(3), 110−123. https://doi.org/10.1109/tsmcb.2005.861881
  12. Kamate, S. & Yilmazer, N. (2015). Application of Object Detection and Tracking Techniques for Unmanned Aerial Vehicles. Procedia Computer Science, 61, 436−441. https://doi.org/10.1016/j.procs.2015.09.183
  13. Lee, M.-H. & Yeom, S. (2018). Detection and Tracking of Multiple Moving Vehicles with a UAV. International Journal of Fuzzy Logic and Intelligent Systems, 18(3), 182−189. https://doi.org/10.5391/IJFIS.2018.18.3.182
  14. Novichenko, A. S. & Artomova, A. V. (2024). Problems and tasks of planning flight routes of unmanned aerial vehicles to increase the efficiency of object search. Information Processing Systems, 2(177), 32−40. https://doi.org/10.30748/soi.2024.177.04
  15. Oleksenko, O. O. & Garasimenko, V. V. (2021). Method of determining flight options for an unmanned aerial vehicle based on the max-mine ant algorithm. The current state of scientific research in IT-technologies, electronics, engineering, nanotechnology and transport sphere, 2, 4–12. https://doi.org/10.36074/csrite- enat.ed-2.01
  16. Plastiras, G., Kyrkou, C. & Theocharides, T. (2018). Efficient ConvNet-based Object Detection for Unmanned Aerial Vehicles by Selective Tile Processing. International Conference on Distributed Smart Cameras, 12, 1−6. https://doi.org/10.1145/3243394.3243692
  17. Shafik, W., Matinkhah, S.M., Shokoor, F. & Sharif, L. (2022). A reawakening of Machine Learning Application in Unmanned Aerial Vehicle: Future Research Motivation. EAI Endorsed Transactions on Internet of Things, 8(29), e3. https://doi.org/10.4108/eetiot.v8i29.987
  18. Sopov, I., Krytskyi, D., Artomova, A. & Artomov, I. (2025). Trust-based routing methodology in UAV swarm networks based on traffic analysis and anomaly detection. Innovative technologies and scientific solutions for industries. 2(32), 111–128. https://doi.org/10.30837/2522-9818.2025.2.111
  19. Sun, W. & Hao, M. (2021). A Survey of Cooperative Path Planning for Multiple UAVs. International Conference on Autonomous Unmanned Systems, 189–196. https://doi.org/10.1007/978-981-16-9492-9_20
  20. Tang, J., Duan, H. & Lao, S. (2023). Swarm intelligence algorithms for multiple unmanned aerial vehicles collaboration: a comprehensive review. Artificial Intelligence Review, 56, 4295−4327. https://doi.org/10. 1007/s10462-022-10281-7
  21. Timochko, O. I., Tristan, A. V., Chernavina, A. E. & Berezhnoy, A. A. (2020). Method for planning the route for conducting aerial reconnaissance of dynamic objects using unmanned aerial vehicles in forest-steppe terrain. Information processing systems, 3(162), 95−110. https://doi.org/10.30748/soi.2020.162.10
  22. Zhao, C., Liu, Y., Yu, L. & Li, W. (2021). Stochastic Heuristic Algorithms for Multi-UAV Cooperative Path Planning. 40th Chinese Control Conference, 7677–7682. https://doi.org/10.23919/CCC52363.2021.9549984
  23. Zhang, H., Xin, B., Dou, L.-H., Chen, J. & Hirota, K. (2020). A review of cooperative path planning of an unmanned aerial vehicle group. Frontiers of Information Technology and Electronic Engineering, 21, 1671–1694. https://doi.org/10.1631/FITEE.2000228