Система керування безпілотними літальними апаратами на основі глибинного навчання

Abstract

The paper is devoted to the development and analysis of an unmanned aerial vehicle (UAV) control system based on deep learning methods. Modern UAV applications require high levels of autonomy, adaptability, and reliability in complex and dynamic environments where traditional control approaches may be insufficient. The proposed approach integrates deep neural networks into key components of the control system, including perception, localization, trajectory planning, and decision-making. Particular attention is paid to the use of computer vision techniques and deep reinforcement learning for autonomous navigation and obstacle avoidance. The advantages of deep learning-based control systems, such as the ability to process unstructured data and adapt to changing conditions, are discussed. At the same time, challenges related to computational complexity, data requirements, and real-world deployment are analyzed. The study demonstrates that combining deep learning methods with classical control algorithms can significantly improve the performance and autonomy of UAV systems. The results highlight the перспективність of intelligent control systems for future aerospace and robotics applications