The relevance of this research stems from the need to create effective control systems for autonomous robots capable of operating in uncertain and dynamically changing environments. An environment representation system must address the challenges of localization, mapping, object detection and classification, dynamic prediction, and semantic interpretation. For an autonomous robot to navigate and perform goal-directed actions in its environment, it must understand its environment—without this, it will be unable to effectively plan movements, avoid obstacles, and reach destinations. Existing environment representation methods have limitations when adapting to unfamiliar, unstructured environments. The aim of this study is to develop the concept and architecture of an environment representation system based on a multi-agent neurocognitive architecture for controlling autonomous robots within a heterogeneous human-machine team. The scientific novelty of this study lies in the development of a multi-agent neurocognitive architecture for representing the state of the environment. The proposed approach enables the creation of flexible world models capable of self-organization and scalability with increasing knowledge. The research methodology is based on the use of multi-agent technologies and neurocognitive models. A multi-agent neurocognitive architecture has been developed, including mechanisms for collecting data from sensors, representing objects and subjects in the environment, forming a mechanism for constructing cause-and-effect relationships, and sharing knowledge between members of a heterogeneous human-machine team. The developed architecture enables the scalability of decision-making systems and facilitates knowledge sharing between team members. A promising direction for further research is improving the system's safety mechanisms. The results of this study can be used in the development of next-generation autonomous robotic systems.
