A Survey on Master–Slave FPV Drone Systems for Military Reconnaissance and Coordinated Aerial Operations

Authors

  • S V Vikas Department of Electronic and Communications Engineering, BNM Institute of Technology, Affiliated to VTU Bengaluru, Karnataka, India Author
  • Srinivas BV Department of Electronic and Communications Engineering, BNM Institute of Technology, Affiliated to VTU Bengaluru, Karnataka, India Author
  • Sharan Sheshadri Department of Electronic and Communications Engineering, BNM Institute of Technology, Affiliated to VTU Bengaluru, Karnataka, India Author
  • Keerthi Kulkarni Department of Electronic and Communications Engineering, BNM Institute of Technology, Affiliated to VTU Bengaluru, Karnataka, India Author

DOI:

https://doi.org/10.32628/CSEIT25111719

Keywords:

UAV, Master–Slave Architecture, FPV Drone, Wireless Communication, Swarm Intelligence, Military Reconnaissance

Abstract

Research on master-slave and swarm-based drone architectures has accelerated due to the growing need for intelligent and coordinated unmanned aerial vehicle systems. Conventional drones' dependability in dynamic or GPS-denied environments is limited by their reliance on GPS and centralized ground stations. The literature on First-Person View and master-slave UAV systems is reviewed in this paper, with an emphasis on developments in swarm intelligence, formation control, and communication protocols. Numerous studies show how to improve UAV autonomy and coordination by combining adaptive communication models, block chain security, and reinforcement learning. The Master-Slave FPV Drone System for Military Reconnaissance is also covered in the paper as a real-world case study that uses nRF24L01 transceivers, Arduino Pro Mini microcontrollers, and MPU6050 IMUs to accomplish GPS-independent coordination. In addition to outlining a future roadmap toward robust, AI-driven multi-drone networks for defence and surveillance applications, the survey identifies research gaps in the areas of energy efficiency, secure communication, and large-scale swarm synchronization.

Downloads

Download data is not yet available.

References

A. Saha, A. Kumar, and A. K. Sahu, “FPV Drone with GPS Used for Surveillance in Remote Areas,” in 2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), Kolkata, India, Nov. 2017, doi: https://doi.org/10.1109/ICRCICN.2017.8234482 DOI: https://doi.org/10.1109/ICRCICN.2017.8234482

S. Dash, A. R. Wahap, F. A. P. Abdullah, W. M. F. B. W. M. Fuaad, S. F. S. Omar, and J. Pusppanathan, “A Basic Optical Lidar-Based FPV Drone Sport Configuration,” in Proceedings of the 2nd International Conference on Innovation and Technology in Sports (ICITS), Malaysia, Nov. 2023, pp. 321–336, doi: https://doi.org/10.1007/978-981-97-2561-0_23. DOI: https://doi.org/10.1007/978-981-97-3741-3_30

H. Khudov, R. Khudov, I. Khizhnyak, O. Makoveichuk, and V. Khudov, “Image Segmentation Methods for Kamikaze FPV Drones Targeting to Aid Critical Energy National Infrastructure Assets Protection,” in Systems, Decision and Control in Energy VII (Studies in Systems, Decision and Control, vol. 596), Jun. 2025, pp. 139–151, doi: https://doi.org/10.1007/978-3-031-62839-5_10. DOI: https://doi.org/10.1007/978-3-031-90462-2_7

N. P. K. S, M. Jatkar, P. Kumar, P. Arya, J. Kumari, and A. Garg, “Military Grade FPV Drone for Enemy Recognition: Precision,” in 2024 International Conference on Emerging Technologies and Innovation for Sustainability (EmergIN), Greater Noida, India, Dec.2024, doi: https://doi.org/10.1109/EmergIN63207.2024.10961345. DOI: https://doi.org/10.1109/EmergIN63207.2024.10961345

T. Sivenas, G. Koutromanos, and N. Larios, “Examining the Acceptance of First-Person View Drones Through Head-Mounted Displays in STEM Education by In-Service Teachers,” in Immersive Learning Research Network (iLRN 2025) (Communications in Computer and Information Science, vol. 2598), Aug. 2025, pp. 234–248, doi: https://doi.org/10.1007/978-3-031-93168-6_18. DOI: https://doi.org/10.1007/978-3-031-98080-0_15

N. V. Pragna, J. Srinivasan, G. M, A. J. S. Vishnu, R. Polavarapu, and A. Mohanty, “Flood Surveillance using FPV Drones,” in 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), Bengaluru, India, Jan. 2023, doi: https://doi.org/10.1109/IDCIoT56793.2023. 10053431. DOI: https://doi.org/10.1109/IDCIoT56793.2023.10053431

S. Misra, P. K. Deb, and K. Saini, “Dynamic Leader Selection in a Master-Slave Architecture-Based Micro UAV Swarm,” in 2021 IEEE Global Communications Conference (GLOBECOM), Madrid, Spain, Dec. 2021, doi: https://doi.org/10.1109/GLOBECOM46510.2021.9685538. DOI: https://doi.org/10.1109/GLOBECOM46510.2021.9685538

M. Pyvovar, O. Pohudina, A. Pohudin, and O. Kritskaya, “Simulation of Flight Control of Two UAVs Based on the ‘Master-Slave’ Model,” in Integrated Computer Technologies in Mechanical Engineering –2021, Feb. 2022, pp. 902–907, doi: https://doi.org/10.1007/978-3-030-94259-5_70. DOI: https://doi.org/10.1007/978-3-030-94259-5_70

A. Salimi-Badr, S. M. A. Ardehali, A. Faraji, and M. Abdoos, “A Master-Slave Approach for Simultaneously Controlling Two Drones when Carrying an Object,” Journal of Intelligent Computing and Software Engineering, 2025, doi: https://doi.org/10.48308/jicse.2025.239595.1064.

H. Khalil, S. U. Rahman, I. Ullah, I. Khan, A. J. Alghadhban, M. H. Al-Adhaileh, G. Ali, and M. ElAffendi, “A UAV-Swarm- Communication Model Using a Machine-Learning Approach for Search-and-Rescue Applications,” Drones, vol. 6, no. 12, p. 372, Nov.2022, doi: https://doi.org/10.3390/drones6120372. DOI: https://doi.org/10.3390/drones6120372

T. I. Zohdi, “A machine-learning enabled digital-twin framework for tactical drone-swarm design,” Computer Methods in Applied Mechanics and Engineering, 2025, doi: https://doi.org/10.1016/j.cma.2025.117999. DOI: https://doi.org/10.1016/j.cma.2025.117999

U. Arshad and Z. Halim, “Secure and optimized drone swarm operations with decentralized Adaptive Differential Evolution,” Computers & Electrical Engineering, 2025, doi: https://doi.org /10.1016/j.compeleceng.2025.110487. DOI: https://doi.org/10.1016/j.compeleceng.2025.110487

H. M. S. Ali, S. K. Jalal, M. W. Saab, S. K. Sulaiman, G. S. N. Ghno, and S. I. Mustafa, “Deciphering the Implications of Swarm Intelligence Algorithms in Efficiently Managing Drone Swarms,” in 2024 35th Conference of Open Innovations Association (FRUCT), Tampere, Finland, Apr. 2024, doi: https:// doi.org/10.23919/FRUCT61870.2024.10516408.

K. Illyass, Z. Baig, and N. Syed, “Robust and lightweighted mutual authentication scheme for drone swarm networks,” Journal of Network and Computer Applications, vol. 242, p. 104264, 2025, doi: https://doi.org/10.1016/j.jnca.2025.104264. DOI: https://doi.org/10.1016/j.jnca.2025.104264

Natti, M. (2023). Reducing Oracle RAC Wait Events by Using Instance-Specific Block Allocation for Production Applications. The Eastasouth Journal of Information System and Computer Science, 1(01), 65–68. https://doi.org/10.58812/esiscs.v1i01.447 DOI: https://doi.org/10.58812/esiscs.v1i01.447

None Murali Natti(2023). “Reducing postgreSQL read and write latencies through optimized fillfactor and hot percentages for high-update applications,” International Journal of Science and Research Archive, vol. 9, no. 2, pp. 1059–1062. doi: https://doi.org/10.30574/ijsra.2023.9.2.0657. DOI: https://doi.org/10.30574/ijsra.2023.9.2.0657

O. A. Amodu, R. Nordin, N. F. Abdullah, and A. Abu-Samah, “Altitude control and power allocation with reinforcement learning for sum capacity maximization in high-data-rate swarm drone interventions,” Computer Communications, vol. 229, p. 108257, 2025, doi: https://doi.org/10.1016/j.comcom.2025.108257. DOI: https://doi.org/10.1016/j.comcom.2025.108257

H. Li, F. Wang, and Z. Zhan, “Drone routing problem with swarm synchronization,” European Journal of Operational Research, vol. 319, no. 3, pp. 771–789, 2024, doi: https:// doi.org/10.1016/j.ejor.2023.10.015.

Downloads

Published

10-10-2025

Issue

Vol. 11 No. 5 (2025): September-October

Section

Research Articles

License

Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
S V Vikas, Srinivas BV, Sharan Sheshadri, and Keerthi Kulkarni, “A Survey on Master–Slave FPV Drone Systems for Military Reconnaissance and Coordinated Aerial Operations”, Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, vol. 11, no. 5, pp. 198–204, Oct. 2025, doi: 10.32628/CSEIT25111719.