COLLABORATIVE ROBOTS INTELLIGENT CONTROL USING FUZZY LOGIC WITHIN INDUSTRY

The article presents a study of intelligent control of collaborative robots using fuzzy logic within Industry 5.0, which is aimed at ensuring safe and effective interaction between robots and a dynamic environment. The proposed mathematical models and numerical simulations demonstrated the ability of the system to successfully avoid collisions and reach target points while maintaining smoothness and optimality of trajectories. The results obtained confirm the advantages of fuzzy logic for describing uncertainty and making decisions in real time, which is critically important for the development of flexible manufacturing systems. The presented approach opens up prospects for further integration with digital twins and machine learning in order to increase the level of autonomy and adaptability of robotic systems.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-10-07
DOI
https://doi.org/10.5281/zenodo.17289030
Primary Topic
Bacterial Genetics and Biotechnology
Type
article
Field-Weighted Citation Impact
0.00
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article

COLLABORATIVE ROBOTS INTELLIGENT CONTROL USING FUZZY LOGIC WITHIN INDUSTRY

Zenodo (CERN European Organization for Nuclear Research)
Bacterial Genetics and Biotechnology
article

COLLABORATIVE ROBOTS INTELLIGENT CONTROL USING FUZZY LOGIC WITHIN INDUSTRY

article en

Abstract

The article presents a study of intelligent control of collaborative robots using fuzzy logic within Industry 5.0, which is aimed at ensuring safe and effective interaction between robots and a dynamic environment. The proposed mathematical models and numerical simulations demonstrated the ability of the system to successfully avoid collisions and reach target points while maintaining smoothness and optimality of trajectories. The results obtained confirm the advantages of fuzzy logic for describing uncertainty and making decisions in real time, which is critically important for the development of flexible manufacturing systems. The presented approach opens up prospects for further integration with digital twins and machine learning in order to increase the level of autonomy and adaptability of robotic systems.

Zenodo (CERN European Organization for Nuclear Research)
Amman Arab University (JO), V. N. Karazin Kharkiv National University (UA)
Industry, innovation and infrastructure
Openalex Percentile: Top 35%
Bacterial Genetics and Biotechnology
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COLLABORATIVE ROBOTS INTELLIGENT CONTROL USING FUZZY LOGIC WITHIN INDUSTRY · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS