RL-based Swarm Intelligence in Autonomous Drone Navigation for Energy-Optimized Surveillance and Reconnaissance

Abstract

Autonomous drone operations in large-scale and dynamic environments face significant challenges related to energy efficiency, communication reliability, and scalability. Traditional single-drone systems and centralized swarm architectures often suffer from limited adaptability, communication bottlenecks, and single points of failure. To address these limitations, this paper proposes a decentralized autonomous swarm framework integrating Deep Reinforcement Learning (DRL) for local decision-making and Federated Reinforcement Learning (FRL) for swarm-level coordination. The proposed system enables drones to collaboratively learn navigation policies while operating independently without continuous centralized control. A fuel-aware reward optimization framework is introduced to balance target detection performance and energy consumption. The framework is evaluated against classical navigation approaches including A*, Greedy, and Random baselines within multiple simulation environments developed using Python, Pygame, FastAPI, and Unity. Experimental results demonstrate that the proposed FRL-based framework achieves superior cumulative reward performance in dynamic environments while maintaining robust decentralized coordination. The results validate the effectiveness of combining DRL and FRL for scalable, adaptive, and energy-efficient autonomous drone swarm navigation.