Application of Fuzzy Logic for Collaborative Robot Control

Collaborative robots (cobots) play a crucial role in modern industry by ensuring safe and efficient human–robot interaction. However, traditional control methods struggle with uncertainty handling and adaptability to dynamic environments. This review explores the application of fuzzy logic as a promising approach for cobot control. The article discusses the fundamental principles of fuzzy logic, its advantages over classical methods, and successful case studies. It analyzes current research, including hybrid methods combining fuzzy logic with machine learning and evolutionary algorithms. The paper also highlights existing challenges and potential future research directions. The conclusions emphasize the potential of fuzzy logic to enhance cobot adaptability and reliability in real-world conditions.

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Institutions

Publication Details

Journal
Electronics
Published
2025-10-14
DOI
https://doi.org/10.3390/electronics14204029
Citations
1
Primary Topic
Fuzzy Logic and Control Systems
Type
article
Field-Weighted Citation Impact
2.48

Funders

Controls
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Application of Fuzzy Logic for Collaborative Robot Control

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Application of Fuzzy Logic for Collaborative Robot Control

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Abstract

Collaborative robots (cobots) play a crucial role in modern industry by ensuring safe and efficient human–robot interaction. However, traditional control methods struggle with uncertainty handling and adaptability to dynamic environments. This review explores the application of fuzzy logic as a promising approach for cobot control. The article discusses the fundamental principles of fuzzy logic, its advantages over classical methods, and successful case studies. It analyzes current research, including hybrid methods combining fuzzy logic with machine learning and evolutionary algorithms. The paper also highlights existing challenges and potential future research directions. The conclusions emphasize the potential of fuzzy logic to enhance cobot adaptability and reliability in real-world conditions.

ElectronicsVol. 14(20)
Tallinn University of Technology (EE)
European Commission
Openalex Percentile: Top 8%
Fuzzy Logic and Control Systems
2.48
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