DOI: 10.17587/prin.14.442-451

Mathematical Modeling Method for Detecting the Fuzzy Occurrence of Dangerous Events

G. E. Rego, Postgraduate Student, Scientist, regoGr@yandex.ru, Institute of Mathematics and Information Technology, Petrozavodsk State University, Petrozavodsk, 185910, Russian Federation

Corresponding author: Grigoriy E. Rego, Postgraduate Student, Scientist, Institute of Mathematics and Information Technology, Petrozavodsk State University, Petrozavodsk, 185910, Russian Federation E-mail: regoGr@yandex.ru

The article describes a mathematical modeling method for detecting the fuzzy occurrence of dangerous events. A distinctive feature of this study is the assessment of the occurrence of dangerous events based on the use of fuzzy sets. Each dangerous event is associated with a subset of the set of sensors. The data is converted into some characteristics of the occurrence of an event, which are then fed to the input of the membership function of a set of dangerous events. In addition, an example of the application of this mathematical model is given, demonstrating its advantages and possibilities for its expansion. The appendix contains a table of possible dangerous events associ­ated with the functioning of the forest robot. The presented model can also be used to detect the fuzzy occurrence of dangerous events associated with human movement. The developed mathematical modeling method for detecting the occurrence of dangerous events has many applications. In particular, it contributes to the creation of a digital assistant to expand the capabilities of the senses of a remote human operator, and can also be used to control a mobile robot.

Keywords: mobile robot, mathematical modeling method, fuzzy set, digital assistant, forest robot

pp. 442–451

For citation:
Rego G. E. Mathematical Modeling Method for Detecting the Fuzzy Occurrence of Dangerous Events, Programmnaya Ingeneria, 2023, vol. 14, no. 9, pp. 442—451. DOI: 10.17587/prin.14.442-451.

The research is implemented with financial support by Russian Science Foundation, project no. 22-11-20040 (https://rscf.ru/en/project/22-11-20040/) jointly with Republic of Karelia and funding from Venture Investment Fund of Republic of Karelia (VIF RK).

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