INTELLIGENT METHODS OF AUTONOMOUS NAVIGATION AND COLLECTIVE CONTROL OF DISTRIBUTED SYSTEMS IN MESH NETWORKS

Abstract

The relevance of the study is due to the growing role of distributed cyber-physical systems, unmanned platforms and autonomous agents. Their functioning requires reliable navigation, adaptive data exchange and coordinated decision-making in dynamic environments. The use of Mesh networks, which provide decentralized interaction between nodes and increase the system's resilience to failures of individual elements, is of particular importance. The purpose of the study is to analyze and generalize intelligent methods of autonomous navigation and collective management of distributed systems in Mesh networks, as well as to determine the prospects for their practical application. To achieve this goal, the methods of system analysis, comparative evaluation of artificial intelligence algorithms, multi-agent management and decentralized data routing were used. As a result of the study, the features of the application of machine learning, reinforcement learning and collective decision-making methods to ensure autonomous navigation of mobile agents in a distributed information environment were identified. It has been established that the combination of intelligent algorithms with self-organized architecture of Mesh networks contributes to increasing the adaptability, scalability and reliability of the functioning of distributed systems. The feasibility of using multi-agent approaches to coordinate the actions of autonomous nodes in conditions of limited resources and variable network topology is substantiated. The scientific novelty of the work lies in the comprehensive generalization of modern approaches to the integration of intelligent navigation methods and collective control in Mesh networks. The practical significance of the results is associated with the possibility of their use in the development of unmanned systems, robotic complexes, sensor monitoring networks and other distributed information and control systems. Further research should be directed to improving the mechanisms of adaptive interaction of agents and increasing the efficiency of decision-making in complex network environments.