AI Integration in Autonomous Collaborative Combat Unmanned Aircraft Squadron

Abstract

Artificial Intelligence (AI) has become one of the most influential technologies driving the evolution of modern aerospace systems. The integration of AI into unmanned aerial combat platforms enables rapid decision-making, autonomous navigation, intelligent mission planning, and coordinated swarm operations with minimal human intervention. This paper presents a conceptual framework for an AI-enabled Autonomous Collaborative Combat Unmanned Aircraft Squadron, centred around a Mother Unmanned Autonomous Combat Vehicle (MUACV) that commands and coordinates multiple unmanned combat and reconnaissance aircraft. The proposed architecture combines Deep Learning, Deep Reinforcement Learning, sensor fusion, regression analysis, optimization techniques, transformer-based reasoning, and adaptive electromagnetic stealth management to improve mission effectiveness in highly contested environments. The study highlights how AI can reduce pilot workload, improve survivability, shorten combat decision cycles, and provide a scalable solution for future air superiority missions while maintaining meaningful human oversight.