Integrating swarm intelligence and edge computing for autonomous multi-drone operations
| Назва | Integrating swarm intelligence and edge computing for autonomous multi-drone operations |
| Назва англійською | Integrating swarm intelligence and edge computing for autonomous multi-drone operations |
| Автори | Leonid Romaniuk, Ihor Chykhira, Halyna Tulaidan, Andrii Holovko |
| Принадлежність | Ternopil Ivan Puluj National Technical University, Ternopil, Ukraine Ternopil Volodymyr Hnatyuk National Pedagogical University, Ternopil, Ukraine |
| Бібліографічний опис | Integrating swarm intelligence and edge computing for autonomous multi-drone operations / Leonid Romaniuk, Ihor Chykhira, Halyna Tulaidan, Andrii Holovko // Scientific Journal of TNTU. — Tern.: TNTU, 2025. — Vol 117. — No 1. — P. 67–75. |
| Bibliographic description: | Romaniuk L., Chykhira I., Tulaidan H., Holovko A. (2025) Integrating swarm intelligence and edge computing for autonomous multi-drone operations. Scientific Journal of TNTU (Tern.), vol 117, no 1, pp. 67–75. |
| DOI: | https://doi.org/10.33108/visnyk_tntu2025.01.067 |
| УДК | 621.396.96 |
| Ключові слова | autonomous trajectory optimization, deep reinforcement learning, multi-agent edge computing, collision avoidance metrics, real-time data processing, adaptive energy modulation. |
This study presents an adaptive PSO (Particle Swarm Optimization) algorithm as the foundation for a swarm intelligence approach in multi-UAV operations. The traditional PSO formula for particle velocity and position updates was modified to incorporate a variation strategy from Differential Evolution (DE), enabling UAVs to dynamically adjust their trajectories. The integration of deep reinforcement learning (DRL) further enhances the model's ability to optimize task offloading and computational distribution, ensuring that UAVs function as efficient edge nodes. An experimental evaluation was conducted to assess the proposed PSO-Edge method compared to other machine learning techniques, specifically Random Forest and Support Vector Machine (SVM). The experimental setup involved a simulation environment where UAVs were tasked to monitor data and execute missions over a defined area. The hardware included an Intel Xeon Gold 6248R CPU, 128 GB RAM, and an NVIDIA Tesla V100 GPU, with the simulation executed using Python 3.8. The proposed PSO-Edge algorithm demonstrated superior performance across multiple metrics: reducing task completion time by 42.1 minutes compared to Random Forest and SVM; achieving the lowest energy consumption per task at 28.9 Wh; demonstrating efficient communication with the least latency at 0.15 seconds; and achieving the highest task accuracy at 96%. The results confirm that the PSO-Edge method outperforms traditional machine learning approaches in task efficiency, energy consumption, communication latency, and accuracy. This highlights the benefits of integrating edge computing with the PSO algorithm, establishing it as a robust solution for multi-UAV operations. The findings have significant implications for optimizing UAV-based applications, particularly in environments requiring dynamic adaptation and efficient resource management. | |
| ISSN: | 2522-4433 |
| Перелік літератури |
1. Wang F., Zou Y., del Rey Castillo E., Ding Y., Xu Z., Zhao H.-W., Lim J. (2022). Automated UAV Path-Planning for High-Quality Photogrammetric 3D Bridge Reconstruction. Structure and Infrastructure Engineering, pp. 1–20. Doi: 10.1080/15732479.2022.2152840.
2. Lu X. (2023). Improved Path Planning Method for Unmanned Aerial Vehicles Based on Artificial Potential Field. Applied and Computational Engineering, pp. 64–71. Doi: 10.54254/2755-2721/10/20230142.
3. Atamanchuk A. V. (2022). Metod vyiavlennia ta identyfikatsii BPLA z zastosuvanniam neironnoi merezhi: kvalifikatsiina robota mahistra za spetsialnistiu “172 – telekomunikatsii ta radiotekhnika”. Ternopil: TNTU, pp. 89.
5. Fomin I. I. (2021). Zakhyst kanalu upravlinnia bezpilotnykh litalnykh aparativ vid nesanktsionovanoho dostupu : kvalifikatsiina robota mahistra za spetsialnistiu “125 – kiberbezpeka”. Ternopil: TNTU, pp. 66.
6. Tupytsia I. M., Kryvonos V. M., Kibitkin S. O., Ivashchuk L. A., Bielivtsov A. O. (2023) Kontseptualna model avtomatyzatsii protsesu deshyfruvannia danykh povitrianoi rozvidky z vykorystanniam tekhnolohii systemy shtuchnoho intelektu. Systems of Arms and Military Equipment, no. 1 (73), pp. 75–81. Doi: 10.30748/soivt.2023.73.09.
7. Oleksenko O. O., Avramenko O. V., Fedorov A. V., Snitsarenko V. V., Chernavina O. Ie. (2023) Zastosuvannia bezpilotnykh litalnykh aparativ zbroinymy sylamy Rosiiskoi Federatsii u viini proty Ukrainy. Science and Technology of the Air Force of Ukraine, no. 4 (49), pp. 37–42. Doi: 10.30748/nitps.2022.49.05.
8. Kartashov V. M., Oleinykov V. N., Sheiko S. A., Babkyn S. Y., Koryttsev Y. V., Zubkov O. V. (2018) Osobennosty obnaruzhenyia y raspoznavanyia malыkh bespylotnыkh letatelnыkh apparatov. Radyotekhnyka, no. 195, pp. 235–243.
9. Kartashov V., Oleynikov V., Koryttsev I. Processing and Recognition of Small Unmanned Vehicles’ Sound Signals. Department of Media Engineering and Information Radio Electronic Systems Kharkiv National University of Radio Electronics. Available at: http://openarchive.nure.ua/handle/document/ (accessed: 01.10.2024).
10. Nekhin M., Kanevskyi L., Myronchuk Yu. (2023). Formuvannia sukupnosti parametriv boiovykh mozhlyvostei udarnykh bezpilotnykh litalnykh aparativ na osnovi fasetnoi systemy klasyfikatsii. Zhytomyrskyi viiskovyi instytut imeni S. P. Korolova, Ukraina, pp. 87–99. Doi:10.33577/2312-4458.28. 2023.87-99.
11. Wong S. Y., Choe C. W. C., Goh H. H., Low Y. W., Cheah D. Y. S., Pang C. (2021) Power Transmission Line Fault Detection and Diagnosis Based on Artificial Intelligence Approach and Its Development in UAV: A Review. Arabian Journal for Science and Engineering, no. 46 (10), pp. 9305–9331. Doi: 10.1007/ s13369-021-05522-w.
12. You H. E. (2020) Mission-Driven Autonomous Perception and Fusion Based on UAV Swarm. Chinese Journal of Aeronautics, no. 33 (11), pp. 2831–2834.
13. Lin C., Han G., Qi X., du J., Xu T., Martinez-Garcia M. (2021) Energy-Optimal Data Collection for Unmanned Aerial Vehicle-Aided Industrial Wireless Sensor Network-Based Agricultural Monitoring System: A Clustering Compressed Sampling Approach. IEEE Transactions on Industrial Informatics, no. 17 (6), pp. 4411–4420.
14. Elghitani F. (2024) Dynamic UAV Routing for Multi-Access Edge Computing. IEEE Transactions on Vehicular Technology, pp. 1–11. Doi: 10.1109/TVT.2024.3360253.
15. Simo A., Dzitac S., Dzitac I., Frigura-Iliasa M., Frigura-Iliasa F. M. (2021) Air Quality Assessment System Based on Self-Driven Drone and LoRaWAN Network. Computer Communications, no. 175, pp. 13–24. Doi: 10.1016/j.comcom.2021.04.032.
16. Deng Y., Zhang H., Chen X., Fang Y. (2024) UAV-Assisted Multi-Access Edge Computing With Altitude-Dependent Computing Power. IEEE Transactions on Wireless Communications, no. 23, pp. 9404–9418. Doi: 10.1109/TWC.2024.3362375.
17. Romaniuk L., Chykhira I. (2020) Aerodynamic model of a group of uavs in aircraft space. Computer-integrated technologies: education, science, production. Lutsk, no. 38, pp. 59–66. Available at: https://doi. org/10.36910/6775-2524-0560-2020-38-10
18. Romaniuk L., Chykhira I., Tulaidan H., Mykytyshyn A. Model of motion route of unmanned aerial vehicles operations with obstacles avoidance. ICAAEIT 2021, 15–17 December 2021. Tern.: TNTU, Zhytomyr “Publishing house “Book-Druk“” LLC, 2021. P. 193–199. (Mathematical modeling in power engineering and information technologies).
19. Romaniuk L., Chykhira I., Kartashov V., Dombrovskyi I. (2023) UAV movement planning in mountainous terrain. Scientific Journal of TNTU, vol. 110, no. 2, pp. 15–22. Available at: https://doi.org/10.33108/ visnyk_tntu2023.02.015.
20. Romaniuk L., Bernas M., Kartashov V., Chykhira I., Tulaidan H. Aircraft automation principles as a basis for the use of information technologies CEUR Workshop Proceedings, 2024, 3742, pp. 270–282.
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| References: |
1. Wang F., Zou Y., del Rey Castillo E., Ding Y., Xu Z., Zhao H.-W., Lim J. (2022). Automated UAV Path-Planning for High-Quality Photogrammetric 3D Bridge Reconstruction. Structure and Infrastructure Engineering, pp. 1–20. Doi: 10.1080/15732479.2022.2152840.
2. Lu X. (2023). Improved Path Planning Method for Unmanned Aerial Vehicles Based on Artificial Potential Field. Applied and Computational Engineering, pp. 64–71. Doi: 10.54254/2755-2721/10/20230142.
3. Atamanchuk A. V. (2022). Metod vyiavlennia ta identyfikatsii BPLA z zastosuvanniam neironnoi merezhi: kvalifikatsiina robota mahistra za spetsialnistiu “172 – telekomunikatsii ta radiotekhnika”. Ternopil: TNTU, pp. 89.
5. Fomin I. I. (2021). Zakhyst kanalu upravlinnia bezpilotnykh litalnykh aparativ vid nesanktsionovanoho dostupu : kvalifikatsiina robota mahistra za spetsialnistiu “125 – kiberbezpeka”. Ternopil: TNTU, pp. 66.
6. Tupytsia I. M., Kryvonos V. M., Kibitkin S. O., Ivashchuk L. A., Bielivtsov A. O. (2023) Kontseptualna model avtomatyzatsii protsesu deshyfruvannia danykh povitrianoi rozvidky z vykorystanniam tekhnolohii systemy shtuchnoho intelektu. Systems of Arms and Military Equipment, no. 1 (73), pp. 75–81. Doi: 10.30748/soivt.2023.73.09.
7. Oleksenko O. O., Avramenko O. V., Fedorov A. V., Snitsarenko V. V., Chernavina O. Ie. (2023) Zastosuvannia bezpilotnykh litalnykh aparativ zbroinymy sylamy Rosiiskoi Federatsii u viini proty Ukrainy. Science and Technology of the Air Force of Ukraine, no. 4 (49), pp. 37–42. Doi: 10.30748/nitps.2022.49.05.
8. Kartashov V. M., Oleinykov V. N., Sheiko S. A., Babkyn S. Y., Koryttsev Y. V., Zubkov O. V. (2018) Osobennosty obnaruzhenyia y raspoznavanyia malыkh bespylotnыkh letatelnыkh apparatov. Radyotekhnyka, no. 195, pp. 235–243.
9. Kartashov V., Oleynikov V., Koryttsev I. Processing and Recognition of Small Unmanned Vehicles’ Sound Signals. Department of Media Engineering and Information Radio Electronic Systems Kharkiv National University of Radio Electronics. Available at: http://openarchive.nure.ua/handle/document/ (accessed: 01.10.2024).
10. Nekhin M., Kanevskyi L., Myronchuk Yu. (2023). Formuvannia sukupnosti parametriv boiovykh mozhlyvostei udarnykh bezpilotnykh litalnykh aparativ na osnovi fasetnoi systemy klasyfikatsii. Zhytomyrskyi viiskovyi instytut imeni S. P. Korolova, Ukraina, pp. 87–99. Doi:10.33577/2312-4458.28. 2023.87-99.
11. Wong S. Y., Choe C. W. C., Goh H. H., Low Y. W., Cheah D. Y. S., Pang C. (2021) Power Transmission Line Fault Detection and Diagnosis Based on Artificial Intelligence Approach and Its Development in UAV: A Review. Arabian Journal for Science and Engineering, no. 46 (10), pp. 9305–9331. Doi: 10.1007/ s13369-021-05522-w.
12. You H. E. (2020) Mission-Driven Autonomous Perception and Fusion Based on UAV Swarm. Chinese Journal of Aeronautics, no. 33 (11), pp. 2831–2834.
13. Lin C., Han G., Qi X., du J., Xu T., Martinez-Garcia M. (2021) Energy-Optimal Data Collection for Unmanned Aerial Vehicle-Aided Industrial Wireless Sensor Network-Based Agricultural Monitoring System: A Clustering Compressed Sampling Approach. IEEE Transactions on Industrial Informatics, no. 17 (6), pp. 4411–4420.
14. Elghitani F. (2024) Dynamic UAV Routing for Multi-Access Edge Computing. IEEE Transactions on Vehicular Technology, pp. 1–11. Doi: 10.1109/TVT.2024.3360253.
15. Simo A., Dzitac S., Dzitac I., Frigura-Iliasa M., Frigura-Iliasa F. M. (2021) Air Quality Assessment System Based on Self-Driven Drone and LoRaWAN Network. Computer Communications, no. 175, pp. 13–24. Doi: 10.1016/j.comcom.2021.04.032.
16. Deng Y., Zhang H., Chen X., Fang Y. (2024) UAV-Assisted Multi-Access Edge Computing With Altitude-Dependent Computing Power. IEEE Transactions on Wireless Communications, no. 23, pp. 9404–9418. Doi: 10.1109/TWC.2024.3362375.
17. Romaniuk L., Chykhira I. (2020) Aerodynamic model of a group of uavs in aircraft space. Computer-integrated technologies: education, science, production. Lutsk, no. 38, pp. 59–66. Available at: https://doi. org/10.36910/6775-2524-0560-2020-38-10
18. Romaniuk L., Chykhira I., Tulaidan H., Mykytyshyn A. Model of motion route of unmanned aerial vehicles operations with obstacles avoidance. ICAAEIT 2021, 15–17 December 2021. Tern.: TNTU, Zhytomyr “Publishing house “Book-Druk“” LLC, 2021. P. 193–199. (Mathematical modeling in power engineering and information technologies).
19. Romaniuk L., Chykhira I., Kartashov V., Dombrovskyi I. (2023) UAV movement planning in mountainous terrain. Scientific Journal of TNTU, vol. 110, no. 2, pp. 15–22. Available at: https://doi.org/10.33108/ visnyk_tntu2023.02.015.
20. Romaniuk L., Bernas M., Kartashov V., Chykhira I., Tulaidan H. Aircraft automation principles as a basis for the use of information technologies CEUR Workshop Proceedings, 2024, 3742, pp. 270–282.
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