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

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.