ANALYSIS OF EXISTING METHODS FOR DETERMINING THE TRAJECTORIES OF ATMOSPHERIC AIRCRAFT FOR THE POSSIBILITY OF THEIR IMPROVEMENT

Abstract

The article provides a structured analysis of current approaches to flight trajectory planning for atmospheric unmanned aerial vehicles (UAVs), emphasizing the conceptual interplay between «trajectory», «route», and «navigation» within autonomous control systems. The research generalizes interdisciplinary interpretations of these terms, drawing from aviation, control theory, cybernetics, and artificial intelligence, to clarify their impact on navigation logic under uncertainty. A comparative review of planning methods is presented, including classical kinematic models, Kalman filtering, heuristic algorithms (e.g., potential fields, swarm optimization), and AI-driven techniques such as neural networks and reinforcement learning. These approaches are evaluated based on accuracy, adaptability, computational cost, and energy efficiency. Findings show that classical methods offer precision and predictability in structured settings, but struggle to adapt to dynamic environments. AI methods demonstrate higher adaptability and learning potential but require significant computational resources and training datasets. The article outlines key limitations typical for complex missions – such as unpredictable weather, urban obstacles, and real-time decision-making across multiple agents – and proposes directions for improvement. Suggested solutions include the integration of online learning, the use of generative AI models (e.g., GPT, diffusion models), and the implementation of multi-objective optimization techniques tailored to operational constraints. The study introduces a hybrid trajectory planning architecture combining deterministic models with intelligent adaptive layers that support autonomous control in changing environments. The proposed framework enhances UAV mission flexibility, reliability, and autonomy, contributing to the development of intelligent decision-support systems for next-generation aerial robotics.