Federated Energy-Aware Deep Reinforcement Learning for GNSS-Independent Swarm UAV Autonomy

Abstract

Achieving scalable swarm autonomy in Global Navigation Satellite System (GNSS)-denied and communication-constrained environments remains an open challenge at the intersection of robotics, distributed optimization, and reinforcement learning. Existing unmanned aerial vehicle (UAV) autonomy frameworks typically decouple navigation, perception, and distributed learning, while assuming centralized coordination or reliable global positioning. This paper introduces a unified federated deep reinforcement learning architecture that enables GNSS-independent multi-UAV autonomy through the principled integration of multi-modal perception, decentralized policy optimization, energy-aware control, and edge-compliant inference. The proposed framework formulates joint navigation and dynamic target tracking as a partially observable Markov decision process optimized via Proximal Policy Optimization (PPO) over structured motion primitives. A communication-efficient federated learning mechanism enables distributed policy convergence under non-independent and identically distributed (non-IID) agent experiences without sharing raw data, establishing a scalable alternative to centralized training. To address sim-to-real discrepancies, the architecture incorporates domain randomization, structured sensor noise modeling, and curriculum-based training to promote robust zero-shot deployment. Multi-agent simulation experiments evaluate the swarm-level and federated-learning behavior of the proposed framework, while single-UAV field deployment evidence using a DJI Matrice 100 platform supports the feasibility of the onboard sensing, perception, and edge-inference pipeline under realistic outdoor conditions. The evaluation demonstrates stable decentralized convergence, improved energy efficiency relative to centralized baselines, robust target-tracking performance under GNSS-denied conditions, and real-time edge-compliant inference. The results establish that federated reinforcement learning can serve as a viable systems-level foundation for resilient, energy-aware, and scalable aerial swarm intelligence, advancing the state of the art in distributed autonomous robotics.