(untitled) (ENVIRONMENT. TECHNOLOGY. RESOURCES. Proceedings of the International Scientific and Practical Conference, 2024)
The combination of Artificial Intelligence (AI) and unmanned aerial vehicles (UAVs), sometimes known as drones, has become a revolutionary approach in modern military and security operations. The purpose of this study is to explore and assess the efficiency of AI-enabled autonomous navigation and decision-making systems for drones in defense security applications. Through a comprehensive literature review, researchers analyze the various AI techniques and algorithms used in these systems, including machine learning, deep learning, and reinforcement learning. The study examines different aspects of autonomous drone navigation, such as sensors, decision-making modules, communication systems, and countermeasure systems. By reviewing scholarly articles and existing studies, researchers gain insights into the hardware and software components, including GPS modules, IMUs, cameras, and other sensors. This analysis provides a clear understanding of the current state of AI-enabled drone technology for defense security and identifies potential areas for future research and improvement. This research study discusses the working of AI-enabled drone autonomous navigation and decision-making systems designed primarily for defense security applications. The study starts by explaining the structure of drone navigation systems, which includes a wide range of hardware and software components. These comprise GPS modules for tracking location, inertial measurement units (IMUs) for estimating attitude, and cameras for seeing the environment. By incorporating these sensors into a sturdy structure, drones are able to detect their surroundings and man oeuvre independently in intricate situations. The effectiveness of AI-enabled drone navigation relies heavily on the application of sophisticated artificial intelligence techniques and algorithms. Machine learning algorithms, such as deep neural networks and reinforcement learning, are crucial in improving the decision-making abilities of drones. AI algorithms allow drones to dynamically adjust their navigation tactics, optimize flight trajectories, and intelligently respond to unforeseen obstacles or hazards by analyzing large volumes of sensor data in real-time. Furthermore, this research explores the datasets being employed in the training and evaluation of AI models for the purpose of drone navigation and decision-making. These datasets contain varied environmental conditions, topographical features, and security scenarios experienced in defensive operations.