Modeling obstacle avoidance strategies in UAV groups
- Details
- Category: Content №2 2025
- Last Updated on 26 April 2025
- Published on 30 November -0001
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Authors:
T.Keribayeva, orcid.org/0000-0001-7380-098x, Civil Aviation Academy, Almaty, the Republic of Kazakhstan
K.Koshekov, orcid.org/0000-0002-9586-2310, Civil Aviation Academy, Almaty, the Republic of Kazakhstan
K.Alibekkyzy*, orcid.org/0000-0002-6732-4363, D.Serikbayev East Kazakhstan Technical University, Oskemen, the Republic of Kazakhstan
A.Koshekov, orcid.org/0000-0001-7373-1494, Civil Aviation Academy, Almaty, the Republic of Kazakhstan
M.Ivanova, orcid.org/0000-0002-1130-0186, Dnipro University of Technology, Dnipro, Ukraine
* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2025, (2): 206 - 213
https://doi.org/10.33271/nvngu/2025-2/206
Abstract:
Purpose. Modeling strategies for increasing the efficiency of unmanned technologies by combining small unmanned aerial vehicles into groups, developing methods and algorithms for decentralized control of operating modes using internal data transmission channels based on VLC laser technologies.
Methodology. This study was performed using system analysis, mathematical and computer modeling, and hardware implementation of optoelectronic devices. Effective obstacle avoidance by swarms of unmanned aerial vehicles (UAVs) was considered. Modeling of the complex motion of a UAV swarm was based on Newton’s second law with obstacle recognition along the trajectory. A three-dimensional domain with fixed obstacles was used for numerical modeling. Thirteen UAVs in a cube position were considered, which moved synchronously from the left wall (from the inlet to the outlet), avoiding obstacles in their path. Each drone made an avoidance decision, taking into account the position of the obstacle and its current position in space. The visualization was carried out using graphs of different types of obstacle avoidance. This process was repeated 10 times, after which the UAV trajectories were analyzed and compared. The results obtained demonstrated the effectiveness of the proposed algorithm.
Findings. The analysis of the interaction of system components, taking into account their mutual influence and dependencies, allowed the development of effective control strategies and coordination of several UAVs. This area of research is important for increasing the efficiency and reliability of group UAVs in various conditions and tasks, which reflects modern requirements for autonomous systems and their management.
Originality. For the first time, a substantiated approach to increasing the efficiency and reliability of group UAVs, taking into account their interaction and dependencies, is proposed to develop an effective management strategy for them, taking into account the current position in space and the coordinates of obstacles.
Practical value. Practical application of the proposed algorithm will ensure increased UAV efficiency based on the use of IG UAVs with receiving and transmitting systems based on VLC laser technologies to solve a wide range of applied tasks: monitoring of land resources, emergency and rescue operations, cartography and transport services, in military affairs.
Keywords: UAV, strategy, control, models, trajectory, obstacle detection, types of transformation
References.
1. Koshekov, K., Pirmanov, I., Alibekkyzy, K., Belginova, S., Karymsakova, I., Karmenova, M., & Baidildina, A. (2023). Digital twins technology in the educational process of the aviation equipment repair. Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), 32(2), 752-762. https://doi.org/10.11591/ijeecs.v32.i2.pp752-762
2. Kushleyev, A., Mellinger, D., & Kumar, V. (2012). Towards a swarm of agile micro quadrotors. Conference: Robotics: Science and Systems. Auton, Robo. https://doi.org/10.15607/RSS.2012.VIII.028
3. Cicala, M., D’Amato, E., Notaro, I., & Mattei, M. (2020). Scalable Distributed State Estimation in UTM Context. Sensors, 20(9), 2682. https://doi.org/10.3390/s20092682
4. Bassolillo, S. R., D’Amato, E., Notaro, I., Ariante, G., Del Core, G., & Mattei, M. (2022). Enhanced Attitude and Altitude Estimation for Indoor Autonomous UAVs. Drones, 6(1), 18. https://doi.org/10.3390/drones6010018
5. D’Amato, E., Nardi,V. A., Notaro, I., & Scordamaglia, V. A. (2021). А Particle Filtering Approach for Fault Detection and Isolation of UAV IMU Sensors: Design. Implementation and Sensitivity Analysis. Sensors, 21(9), 3066. https://doi.org/10.3390/s21093066
6. Elmokadem, T., & Savkin, A. V. (2021). Computationally-Efficient Distributed Algorithms of Navigation of Teams of Autonomous UAVs for 3D Coverage and Flocking. Drones, 5(4), 124. https://doi.org/10.3390/drones5040124
7. Koshekov, К. Т., Seidakhmetov, B. K., Savostin, А. А., Anayatova, R. K., & Fedorov, I. O. (2021). Aviation Profiling Method Based on Deep Learning Technology for Emotion Recognition by Speech Signal. Transport and Telecommunication Journal, 22(4), 471-481. https://doi.org/10.2478/ttj-2021-0037
8. Hildmann, H., Kovacs, E., Saffre, F., & Isakovic, A. (2019). Nature-inspired drone swarming for real-time aerial data-collection under dynamic operational constraints. Drones, 3(3), 71. https://doi.org/10.3390/drones3030071
9. Xu, C., Zhang, K., Jiang, Y., Niu, S., Yang, T., & Song, H. (2021). Communication Aware UAV Swarm Surveillance Based on Hierarchical Architecture. Drones, 5(2), 33. https://doi.org/10.3390/drones5020033
10. Kalantayevskaya, N., Latypov, S., Murat, K., Koshekov, K., & Savostin, A. (2022). Design of decision-making support system in power grid dispatch control based on the forecasting of energy consumption. Cogent Engineering, 9(1). https://doi.org/10.1080/23311916.2022.2026554
11. Bassolillo, S. R., Blasi, L., D’Amato, E., Mattei, M., & Notaro, I. (2021). Decentralized Triangular Guidance Algorithms for Formations of UAVs. Drones, 6(1), 7. https://doi.org/10.3390/drones6010007
12. Soltan, A. M., Kopzhassarov, B. T., Belginova, S., Vais, Y. A., Azamatova, Z., & Khassenova, Z. T. (2023). Digital risk assessment and prediction in technology process stages of ore-streams. Journal of Theoretical and Applied Information Technology, 101(3), 1322-1332. ISSN: 1992-8645, E-ISSN: 1817-3195.
13. YingKai, Ma., & ShuRong, Li (2023). UAV Path Planning Based on Improved Artificial Potential Field Method. Proceedings of 2023 Chinese Intelligent Systems Conference, 761-777. https://doi.org/10.1007/978-981-99-6882-4_62
14. Modares, J., Mastronarde, N., & Dantu, K. (2019). Simulating Unmanned Aerial Vehicle swarms with the UB-ANC emulator. International Journal of Micro Air Vehicles, 11. https://doi.org/10.1177/1756829319837668
15. Sanchez-Aguero, V., Valera, F., Vidal, I., & Tipantu, A. C. (2020). Hesselbach, X. Energy-Aware Management in Multi-UAV Deployments: Modelling and Strategies. Sensors, 20(10), 2791. https://doi.org/10.3390/s20102791
16. Lu, Y., Xue, Zh., Xia, G.-S., & Zhang, L. (2018). A survey on vision-based UAV navigation. Geo-spatial information science, 21(1), 21-32. https://doi.org/10.1080/10095020.2017.1420509
17. Alibekkyzy, K., Keribayeva, T., Koshekov, K., Baidildina, A., Bugubayeva, A., & Azamatova, Zh. (2024). Development of an algorithm for integrated UAV groups using visible light communication technology. Indonesian Journal of Electrical Engineering and Computer Science, 36(1), 41-52. https://doi.org/10.11591/ijeecs.v36.i1.pp41-52
18. Alenezi, M. (2020). Ontology-based context-sensitive software security knowledge management modeling. International Journal of Electrical and Computer Engineering (IJECE), 10(6), 6507-6520. https://doi.org/10.11591/IJECE.V10I6.PP6507- 6520
19. Brust, M., Danoy, G., Bouvry, P., Gashi, D., Pathak, H., & Gonchalves, M. (2017). Defending Against Intrusion of Malicious UAVs with Networked UAV Defense Swarms. IEEE 42 nd Conference on Local Computer Networks Workshops (LCN Workshops). https://doi.org/10.1109/LCN.Workshops.2017.71
20. Strydom, R., Deneulle, A., & Srinivasan, M. (2016). Bio-Inspired Principles Applied to the Guidance. Navigation and Control of UAS, Aerospace, 3(3), 21. https://doi.org/10.3390/aerospace3030021
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