Development of Autonomous Control Systems for Unmanned Aerial Vehicles

Abstract

The evolution of Unmanned Aerial Vehicles (UAVs) has transformed the landscape of modern aerospace engineering, enabling applications across defense, logistics, agriculture, and disaster management. Central to these advancements is the development of autonomous control systems that empower UAVs to operate with minimal human intervention. This paper explores the design principles, algorithms, and architectures behind autonomous UAV control systems. Key aspects such as sensor fusion, adaptive control, machine learning integration, and communication protocols are examined. Furthermore, the paper discusses challenges related to navigation, decision-making under uncertainty, and safety assurance. The study concludes with insights into future trends, including swarm intelligence, real-time optimization, and AI-driven mission adaptability.