Analysis of Machine Learning Methods in Navigation and Trajectory Planning for Autonomous Control of Unmanned Systems

This article investigates the use of machine learning methods in navigation and trajectory planning for the autonomous control of unmanned systems. The main approaches, such as deep learning and reinforcement learning, are considered, offering innovative solutions to challenges arising in dynamic and complex environments. An overview of machine learning methods is conducted, highlighting their advantages over traditional algorithms due to flexibility, adaptability, and the ability to operate under uncertainty. The application of machine learning in trajectory planning is analyzed, including the use of autoencoders, generative models, and graph neural networks for predicting and optimizing routes. Existing problems and challenges are discussed, particularly ensuring safety and reliability, the need for large volumes of high-quality data, issues of model interpretability, and regulatory aspects. Prospects for development are identified, including the development of more efficient algorithms, enhancing model transparency, and establishing standards for the responsible deployment of autonomous systems. In conclusion, it is emphasized that machine learning is a transformative force in the field of autonomous navigation and trajectory planning. Overcoming current challenges and continuing innovation will unlock the full potential of unmanned systems, bringing significant benefits to society and the economy through widespread application across various sectors.