The use of machine learning tools for modeling the countermeasure environment of robotic means and mobile objects of monitoring

DOI: 10.31673/2412-9070.2023.052934

Abstract

In this article, an analysis of research related to the problems and areas of application of machine learning was carried out and it becomes clear that reinforcement learning is an important tool for the development of highly effective and adaptive control systems. This approach opens perspectives for the development of intelligent systems that can effectively adapt to changes in the environment and provide a reliable and optimal level of control under conditions of uncertainty and variability. In the course of the research, a complex environment modelling technique was developed for the task of searching and chasing moving objects by robotic means as part of reinforcement learning. The methodology includes defining goals and objectives, modelling the environment, defining states, actions, and rewards, formulating the state transition function, defining the reward function, modelling completion criteria, using convolutional neural networks to analyse the environment, and integrating the model into reinforcement learning algorithms. A feature of the developed methodology is its ability to adapt to various environmental conditions and dynamic changes occurring in it. The use of graphical representation of the environment allows you to reproduce in detail the real conditions in which the robotic means operate, including obstacles, areas of visibility and different types of surfaces. The use of convolutional neural networks for environmental analysis is an innovative approach that allows you to automate the process of determining the impact of the environment on the parameters of robotic vehicles and moving objects. CNN training on the collected data about the environment provides accurate prediction of influence coefficients in different areas, which allows optimizing strategies of robot behaviour depending on changes in conditions. In general, the developed environment modelling technique allows not only to accurately reproduce the real conditions in which robotic means operate, but also provides flexibility and adaptability when environmental conditions change. This opens up vast opportunities for training and improving reinforcement learning algorithms, increasing the effectiveness of robots in performing their tasks in various real-world scenarios.

Keywords: artificial intelligence; mathematical modeling; reinforcement learning; artificial neural networks; robotics; sensor networks; monitoring systems; environment model; automation.