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Analysis of operator identification methods in the working area of a collaborative manipulator robot
(2025) Molozhanov, L.; Gurin, D.
The paper presents the results of the analysis of modern methods of operator identification in the working area of a collaborative robot-manipulator, which is important in the context of safe and effective interaction between a person and a robotic system. Sensor approaches, computer vision methods, biometric technologies, algorithms based on deep neural networks, as well as integration solutions using data fusion and fuzzy logic are considered. It is shown that sensor methods provide basic security, but are limited in accuracy, while computer vision and deep learning allow achieving high detail in identification, although they require significant computing resources. Biometric approaches create conditions for personalization of interaction, but may lose effectiveness in a production environment with a high level of noise and the need to use personal protective equipment. Data integration methods increase the stability of the system and its ability to work in conditions of uncertainty, ensuring multi-level adaptability. The analysis confirms that optimal solutions should be based on a combination of several methods, which is consistent with the concept of Industry 5.0 and contributes to the development of new generation cognitive robotic systems.