Intelligent Routing Strategies in UAV Swarm Operations
Serap Kilinc, Meltem Yıldırım Imamoğlu · 2025
This study explores the potential of artificial intelligence-based methods for route planning in unmanned aerial vehicle (UAV) swarm systems, with a particular focus on the advantages of the Q-learning algorithm in dynamic and uncertain environments, where traditional methods often fall short. A review of the literature reveals that swarm UAV mission planning typically relies on fixed-rule or predefined models, which impose significant limitations in real-time decision-making and adaptability to changing conditions. Q-learning’s ability to balance exploration and exploitation provides a flexible, adaptive structure for route optimization. Future work will focus on implementing this approach and examining its practical applications and operational impact in more detail.