Design and Development of Autonomous Drone Traffic Control System

Rapid expansion of drone technology and urban air mobility necessitates advanced air traffic control (ATC) solutions. Traditional ATC systems struggle with limited signal coverage and congestion in high-density airspace, leading to inefficiencies and safety concerns. This study proposes an autonomous ATC drone equipped with advanced multi-sensor fusion, AI-driven traffic management algorithms, and adaptive communication protocols to enhance real-time air traffic monitoring and coordination. The drone functions as a mobile ATC unit, significantly improving situational awareness, traffic flow optimization, and collision avoidance in dynamic airspace. Simulation results demonstrate a 98.7% collision avoidance success rate, an average flight path deviation of 1.5 meters in signal-weak areas, and a response time of 1.2 seconds to traffic changes. Real-world tests also validate its effectiveness in urban and remote environments, highlighting its potential for seamless integration into existing ATC frameworks and large-scale deployment in complex airspace. Therefore this study paves the way for scalable, autonomous air traffic management solutions, thereby contributing to safer and more efficient aerial operations.