APPLICATION OF ARTIFICIAL INTELLIGENCE FOR SWARM SYSTEMS MANAGMENT

O.A. Kravchuk, V.D. Samoilov

Èlektron. model. 2024, 46(6):29-42

https://doi.org/10.15407/emodel.46.06.029

ABSTRACT

The use of artificial intelligence methods in swarm systems of unmanned aerial vehicles is studied. The basic artificial intelligence (AI) algorithms that ensure adaptive and intelligent swarm behavior are presented, and their application in real-world scenarios is analyzed. Particular attention is paid to the current problems and limitations of swarm systems, such as system scalability, communication reliability, adaptation to a dynamic environment, etc. Promising directions for the development of AI-based algorithms aimed at increasing the efficiency, stability, and survivability of swarms are outlined.

KEYWORDS

unmanned aerial vehicles, swarm system, swarm of unmanned aerial vehicles, artificial intelligence.

REFERENCES

  1. Beni,, Wang, J. (1993). Swarm intelligence in cellular robotic systems. In robots and biological systems: Towards a new bionics? Pp. 703-712. Springer Berlin Heidelberg. URL: https://doi.org/10.1007/978-3-642-58069-7_38
  2. Liu, G., Van Huynh, N., Du, H. et al. Generative AI for unmanned vehicle swarms: challenges, applications and opportunities. arXiv, 2024. URL: http://arxiv.org/abs/2402.18062
  3. Tan, Y., Wang, J., Liu, J. Unmanned systems security: Models, challenges, and future directions. IEEE Network. Vol. 34, Issue 4. Pp. 291- URL: https://doi.org/10.1109/ MNET.001.1900546
  4. McEnroe, P., Wang, S., Liyanage, M. (2022). A survey on the convergence of edge computing and AI for uavs: opportunities and challenges. IEEE internet of things journal. Pp. 15435-15459. URL: https://doi.org/10.1109/jiot.2022.3176400
  5. Ahmadzadeh, A., Jadbabaie, A., Kumar, V. et al. (2006). Multi-UAV cooperative surveillance with spatio-temporal specifications. Proceedings of the 45th IEEE conference on decision and control, San Diego, CA, USA, 13-15 December 2006. Pp. 5293- URL: https://doi.org/10.1109/CDC.2006.377157
  6. Nigam, N., Bieniawski, S., Kroo, I. et al. (2012). Control of multiple UAVs for persistent surveillance: algorithm and flight test results. IEEE transactions on control systems technology. Vol. 20, no. 5. Pp. 1236- URL: https://doi.org/10.1109/tcst.2011.2167331
  7. Scherer, J., Rinner, B. (2020). Multi-UAV surveillance with minimum information idleness and latency constraints. IEEE robotics and automation letters. Vol. 5, no. 3. Pp. 4812- URL: https://doi.org/10.1109/lra.2020.3003884
  8. Yan, R., Pang, S., Sun, H. et al. (2010). Development and missions of unmanned surface vehicle. Journal of marine science and application. Vol. 9, no. 4. Pp. 451- URL: https://doi.org/10.1007/s11804-010-1033-2
  9. Masoud, A.A. Decentralized, self-organizing, potential field-based control for individually motivated, mobile agents in a cluttered environment: a vector-harmonic potential field approach. IEEE transactions on systems, man, and cybernetics - part A: systems and humans. Vol. 37, no. 3. Pp. 372- URL: https://doi.org/10.1109/TSMCA.2007.893483
  10. Hu, Y., Chen, M., Saad, W. et al. (2021). Distributed multi-agent meta learning for trajectory design in wireless drone networks. IEEE journal on selected areas in communications. Vol. 39, no. 10. Pp. 3177- URL: https://doi.org/10.1109/jsac.2021.3088689
  11. Ding, Y., Yang, Z., Pham, Q.-V. et al. (2023). Distributed machine learning for UAV swarms: computing, sensing, and semantics. arXiv, URL: http://arxiv.org/abs/2301.00912
  12. Konečný, J., McMahan, H.B., Yu, F. X. et al. Federated learning: strategies for improving communication efficiency. arXiv, 2017. URL: http://arxiv.org/abs/1610.05492
  13. Konečný, J., McMahan, H.B., Ramage, D. et al. Federated optimization: distributed machine learning for on-device intelligence. arXiv, 2016. URL: http://arxiv.org/abs/1610.02527
  14. Yang, Q., Liu, Y., Chen, T. et al. Federated machine learning: concept and applications. Todayʼs AI still faces two major challenges. arXiv, 2019. URL: http://arxiv.org/abs/1902. 04885
  15. Niknam, S., Dhillon, H.S., Reed, J.H. Federated learning for wireless communications: motivation, opportunities and challenges. arXiv, 2020. URL: http://arxiv.org/abs/1908.06847
  16. Yang, Z., Chen, M., Wong, K.-K. et al. (2022). Federated learning for 6G: applications, challenges, and opportunities. Engineering. Vol. 8. P. 33- URL: https://doi.org/ 10.1016/ j.eng.2021.12.002
  17. Federated learning: challenges, methods, and future directions / T. Li et al. IEEE signal processing magazine. 2020. Vol. 37, no. 3. Pp. 50- URL: https://doi.org/10.1109/msp.2020.2975749
  18. Fallah, A., Mokhtari, A., Ozdaglar, A. (2020). Personalized federated learning with theoretical guarantees: a model-agnostic meta-learning approach. Advances in neural information processing systems (2020). Curran Associates, Inc., Pp. 3557- URL: https://proceedings.neurips.cc/paper_files/paper/2020/file/24389bfe4fe2eba8bf9aa9203a44cdad-Paper.pdf
  19. Distributed learning in wireless networks: recent progress and future challenges / M. Chen et al. IEEE journal on selected areas in communications. 2021. Vol. 39, no. 12. Pр. 3579- URL: https://doi.org/10.1109/jsac.2021.3118346
  20. Teerapittayanon, S., McDanel, B., Kung, H.T. (2017). Distributed deep neural networks over the cloud, the edge and end devices. 2017 IEEE 37th international conference on distributed computing systems (ICDCS), Atlanta, GA, USA, 5-8 June 2017. URL: https://doi.org/10.1109/icdcs.2017.226.
  21. Gupta, O., Raskar, R. (2018). Distributed learning of deep neural network over multiple agents. Journal of network and computer applications. Vol. 116. Pp. 1- URL: https://doi.org/10.1016/j.jnca.2018.05.003.
  22. Singh, A., Vepakomma, P., Gupta, O. et al. (2019). Detailed comparison of communication efficiency of split learning and federated learning. arXiv, URL: http://arxiv.org/abs/1909.09145
  23. Liu, X., Deng, Y., Mahmoodi, T. (2022). A novel hybrid split and federated learning architecture in wireless UAV networks. ICC 2022 — IEEE international conference on communications, Seoul, Korea, Republic of, 16-20 May 2022. URL: https://doi.org/ 10.1109/ icc45855.2022.9838867
  24. Byrne, M. (2023). The disruptive impacts of next generation generative artificial intelligence. CIN: computers, informatics, nursing. Vol. 41, no. 7. Pp. 479- URL: https://doi.org/ 10.1097/cin.0000000000001044
  25. Research on unmanned surface vehicles environment perception based on the fusion of vision and lidar / W. Zhang et al. IEEE access. 2021. Vol. 9. Pp. 63107- URL: https://doi.org/10.1109/access.2021.3057863
  26. Zhang,, Fu, M. (2023). Research on unmanned system environment perception system methodology. Lecture notes in networks and systems. Cham, Pp. 219-233. URL: https://doi.org/10.1007/978-3-031-38082-2_17
  27. A latent encoder coupled generative adversarial network (LE-GAN) for efficient hyperspectral image super-resolution / Y. Shi et al. IEEE transactions on geoscience and remote sensing. 2022. P. 1. URL: https://doi.org/10.1109/tgrs.2022.3193441
  28. Co-Visual pattern-augmented generative transformer learning for automobile geo-localization / J. Zhao et al. Remote sensing. 2023. Vol. 15, no. 9. P. 2221. URL: https://doi.org/10.3390/rs15092221
  29. Ponnimbaduge Perera, T.D., Jayakody, D.N.K., Sharma, S.K. et al. Simultaneous wireless information and power transfer (SWIPT): recent advances and future challenges. IEEE communications surveys & tutorials. Vol. 20, no 1. Pp. 264- URL: https://doi.org/10.1109/COMST.2017.2783901
  30. Wen, W., Jia, Y., Xia, W. Federated learning in swipt-enabled micro-uav swarm networks: a joint design of scheduling and resource allocation. 2021 13th international conference on wireless communications and signal processing (WCSP), Changsha, China, 20-22 October 2021. 2021. URL: https://doi.org/10.1109/wcsp52459.2021.9613446
  31. Pan, S.J., Yang, Q. (2010). A survey on transfer learning. IEEE transactions on knowledge and data engineering. Vol. 22, no. 10. Pp. 1345- URL: https://doi.org/10.1109/tkde.2009.191
  32. Hospedales, T., Antoniou, A., Micaelli, P. et al. Meta-Learning in Neural Networks: A Survey. arXiv, 2020. URL: https://doi.org/10.48550/ARXIV.2004.05439.

Full text: PDF