Інтеграція штучного інтелекту в ергатичні системи управління безпілотними авіаційними системами

Abstract

The rapid development of unmanned aerial systems (UAS) involves increasingly complex human-machine (ergatic) interactions. During dynamic operations, operators frequently face severe cognitive overload due to massive data streams, leading to a loss of situational awareness and delayed decision-making. This study explores the integration of artificial intelligence (AI) technologies into UAS control loops to mitigate human cognitive limitations. By employing computer vision based on convolutional ne ural networks (CNNs) for automated object detection and predictive analytics for system diagnostics, AI can handle routine and computationally heavy tasks. This enables a paradigm shift from manual piloting to intentbased supervisory control. The synergy between high-speed AI processing and human adaptability ensures ethical oversight and creative problem-solving in uncertain environments. Ultimately, AI integration transforms the operator's role into that of a system manager, significantly enhancing decision-making quality, operational efficiency, and overall flight safety.