OVERVIEW OF MODERN TECHNICAL AND SOFTWARE SOLUTIONS FOR UAV CONTROL

Abstract

The rapid development of unmanned aerial vehicles (UAVs) poses new challenges for 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 a combination of hardware and software solutions that ensure accurate positioning, autonomy, flight stability and safe task performance. The article focuses on key technical components, such as flight controllers, sensors and communication systems, as well as on software platforms that allow for the automation of the flight control process. In addition, innovative approaches to integrating data from various sources and using machine learning algorithms to optimize UAV operation are considered. The aim of the article 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. Evaluation of software, which includes open platforms (ArduPilot, PX4, LibrePilot) and highlevel 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 coordinating drone groups, including trajectory modeling and motion synchronization. A graphical representation of data that demonstrates a comparison of platforms, trajectories, and energy consumption models. Scientific novelty. An approach to integrating sensor data using machine learning algorithms, in particular the Kalman filter, is proposed to improve navigation accuracy and flight stability in difficult conditions. Conclusions. 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. UAV energy models are detailed, taking into account the weight of the cargo, the route, and the impact of aerodynamic drag on the total energy consumption, which allows optimizing long missions. The prospects for integrating cloud technologies with hardware platforms aimed at processing large data sets in real time, which improves the autonomy and adaptability of systems, are assessed. The advantages and disadvantages of modern high-level control systems, such as Aerostack2, GAAS, and their suitability for developing innovative solutions in the field of UAV control are identified.