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57 ing the machine learning algorithms. At a modern rate of increasing the number of robots, it is a relevant task to develop algorithms for data analysis and autonomous decision-making about a robot’s behavior. Examples of such robotic systems include cars, unmanned drones, robot couriers, etc. Implementing a closed system within one technical unit makes it possible to minimize the number of perturbations; the implementation of an autonomous control system over a robot is not difficult. When employing a significant number of autonomous mobile robots, the process of managing each individually becomes resource-intensive and difficult to implement [1]. In such systems, it is also optimal to use machine learning algorithms. Machine learning algorithms are typically tested using a computer by analyzing a significant amount of data; the analysis result is information presented in the text form. At present, this approach is not optimal when real robots are involved, especially in multi-agent systems, because it does not allow the visualization of solution results and conducting a field experiment in dynamics. Existing models of laboratoBUILDING A MODEL OF NETWORK INTERACTION BETWEEN THE COMPONENTS OF A MULTIAGENT SYSTEM OF MOBILE ROBOTS

Building a model of network interaction between the components of a multiagent system of mobile robots