MODELING OF EMOTIONAL-ADAPTIVE INTERACTION OF COLLABORATIVE ROBOTS IN THE INDUSTRY 5.0 ENVIRONMENT BASED ON FRACTAL-NEURAL LOGIC

Vladyslav Yevsieiev1; Svitlana Maksymova1; Artem Lisovskyi2; Amer Abu-Jassar3

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Published

2025-09-02

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Vol. 5 No. 9 (2025): Multidisciplinary Journal of Science and Technology

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Articles

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MODELING OF EMOTIONAL-ADAPTIVE INTERACTION OF COLLABORATIVE ROBOTS IN THE INDUSTRY 5.0 ENVIRONMENT BASED ON FRACTAL-NEURAL LOGIC. (2025). Multidisciplinary Journal of Science and Technology, 5(9), 21-32. http://www.mjstjournal.com/index.php/mjst/article/view/4841

How to Cite

MODELING OF EMOTIONAL-ADAPTIVE INTERACTION OF COLLABORATIVE ROBOTS IN THE INDUSTRY 5.0 ENVIRONMENT BASED ON FRACTAL-NEURAL LOGIC. (2025). Multidisciplinary Journal of Science and Technology, 5(9), 21-32. http://www.mjstjournal.com/index.php/mjst/article/view/4841

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MODELING OF EMOTIONAL-ADAPTIVE INTERACTION OF COLLABORATIVE ROBOTS IN THE INDUSTRY 5.0 ENVIRONMENT BASED ON FRACTAL-NEURAL LOGIC

Vladyslav Yevsieiev1

1Department of Computer-Integrated Technologies, Automation and Robotics, Kharkiv National University of Radio Electronics, Ukraine

Svitlana Maksymova1

1Department of Computer-Integrated Technologies, Automation and Robotics, Kharkiv National University of Radio Electronics, Ukraine

Artem Lisovskyi2

2Department Artificial Intelligence Department, Kharkiv National University of Radio Electronics, Ukraine

Amer Abu-Jassar3

3Department of Computer Science, College of Information Technology, Amman Arab University, Amman, Jordan

Keywords: Fractal Logic, Emotional Adaptation, Collaborative Robotics, Neural Networks, Industry 5.0, Cognitive Interaction.

Abstract

The article presents an approach to modeling the emotional-adaptive interaction of collaborative robots based on fractal-neural logic within Industry 5.0 concept. The proposed model combines the fractal nature of noise with deep neural networks to reproduce the dynamics of emotional states in a changing environment. The simulation demonstrates the system's ability to adaptively learn and take into account the context of interaction between agents. The results indicate the effectiveness of the approach for building flexible, emotionally sensitive robotic systems focused on cognitive-social integration. The model can be used in service, production, and social robotics to create new forms of cooperative behavior.

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