REDESIGNING THE KNOWLEDGE OF ROBOTIC SYSTEM ENGINEERS THROUGH THE MODEL-PARAMETRIC SPACE OF INFORMATION TECHNOLOGY

The relevance of the topic is driven by the rapid development of digital technologies and the growing complexity of designing robotic systems, which requires new approaches to the representation, structuring, and verification of knowledge. The need to integrate heterogeneous models and parameters into a single information environment determines the importance of the study for industrial and scientific applications. The purpose of the study is to investigate methods of representing the knowledge of robotic system designers based on the model-parameter space and to describe the possibilities of their practical use. The object of research is the process of formalizing knowledge and integrating models in the design of complex technical systems. The methodological basis of the study is a systematic approach, semantic modeling methods, interval analysis, and theoretical set operations on knowledge neighborhoods. As a result of the work, the concept of building a model-parameter space (<M,P>) was formed, a classification of basic concepts and categories in the field of design was developed, and formal measures of compatibility, integrity, and completeness of knowledge were proposed. Algorithms for constructing knowledge neighborhoods and integrating models into holistic methodologies with automated consistency checking have been developed. The structure of the tool and software complex to support the processes of model analysis and synthesis in robotic systems is presented. The practical significance of the results lies in the possibility of using the proposed approaches in the development of digital engineering platforms, reducing the risk of design errors, increasing the adaptability of design solutions, and reducing the development time of complex technical systems.