MODELS AND METHODS OF MODERN TECHNICAL AND SOFTWARE SOLUTIONS FOR UAV CONTROL
Authors:   Interregional Academy of Personnel Management, Vyhovskyi Serhii 
Published: February 2025

The rapid development of unmanned aerial vehicles (UAVs) poses new challenges to engineers and developers of control systems. UAVs have found wide application in various fields, including agriculture, logistics, environmental monitoring, search and rescue operations, and military needs. The effectiveness of these systems largely depends on the combination of hardware and software solutions that ensure accurate positioning, autonomy, flight stability and safe performance of tasks. The article focuses on the key technical components, such as flight controllers, sensors and communication systems, as well as on the software platforms that allow the automation of the flight management process. In addition, innovative approaches to the integration of data from various sources and the use of machine learning algorithms to optimize the operation of UAVs are considered. The purpose is to highlight modern technical and software solutions that contribute to increasing the efficiency, reliability and autonomy of unmanned aerial vehicles, as well as to analyze their impact on the further development of the industry. The methodology presented in this article is based on a review of current technical and software solutions for controlling unmanned aerial vehicles (UAVs), focusing on hardware platforms, sensors, communication systems, and software. Comparative analysis of hardware platforms (FPGA, ARM, Atmel, Raspberry Pi) on key parameters: performance, flexibility, power consumption, complexity and cost. Software evaluations that include open platforms (ArduPilot, PX4, LibrePilot) and high-level control systems (Aerostack2, GAAS). Integration of sensor data using machine learning algorithms, such as the Kalman filter, to improve navigation accuracy and flight stability. Modeling of UAV energy consumption taking into account cargo weight, route length and quadratic growth due to aerodynamic drag. Analysis of multi-agent systems for drone group coordination, including trajectory modeling and motion synchronization. A graphical representation of the data that demonstrates a comparison of platforms, trajectories and energy consumption patterns. Scientific novelty. An approach to the integration of sensor data using machine learning algorithms, in particular the Kalman filter, is proposed to improve navigation accuracy and flight stability in difficult conditions. Results. Modern hardware and software platforms for controlling unmanned aerial vehicles (UAVs) are analyzed, taking into account their performance, energy consumption, flexibility and complexity. Multi-agent systems and their potential for synchronizing the actions of UAV groups in various tasks, including monitoring and search and rescue operations, are analyzed. Energy models of UAVs are detailed, taking into account the weight of the cargo, the route and the effect of aerodynamic resistance on the total energy consumption, which allows to optimize long missions. The prospects of integrating cloud technologies with hardware platforms aimed at processing large data sets in real time, which improves the autonomy and adaptability of systems, are evaluated. The advantages and disadvantages of modern high-level control systems, such as Aerostack2, GAAS, and their suitability for the development of innovative solutions in the field of UAV control are determined.