mireabulletinRussian Technological JournalRussian Technological Journal2782-32102500-316XRTU MIREA10.32362/2500-316X-2026-14-4-36-52FJRJYKmireabulletin-1614Research ArticleРОБОТИЗИРОВАННЫЕ КОМПЛЕКСЫ И СИСТЕМЫ. ТЕХНОЛОГИИ ДИСТАНЦИОННОГО ЗОНДИРОВАНИЯ НЕРАЗРУШАЮЩЕГО КОНТРОЛЯMULTIPLE ROBOTS (ROBOTIC CENTERS) AND SYSTEMS. REMOTE SENSING AND NON-DESTRUCTIVE TESTINGДиагностика неисправностей в интеллектуальных системах управления автономными роботами на основе онтологического подходаFault diagnostics in intelligent control systems of autonomous robots: An ontological approachhttps://orcid.org/0009-0003-6671-5674БыковцевЮ. А.BykovtsevYu. A.

Быковцев Юрий Алексеевич, к.т.н., доцент, кафедра проблем управления, Институт искусственного интеллекта

Scopus Author ID 57302607300, ResearcherID KRQ-5339-2024

119454, Москва, пр-т Вернадского, д. 78

Yuri A. Bykovtsev, Cand. Sci. (Eng.), Assistant Professor, Department of Management Problems, Institute of Artificial Intelligence

Scopus Author ID 57302607300, ResearcherID KRQ-5339-2024

78, Vernadskogo pr., Moscow, 119454

bykovcev@mirea.ruhttps://orcid.org/0000-0001-6708-9124ЛохинВ. М.LokhinV. M.

Лохин Валерий Михайлович, д.т.н., профессор, кафедра проблем управления, Институт искусственного интеллекта

Scopus Author ID 6602931640

119454, Москва, пр-т Вернадского, д. 78

Valery M. Lokhin, Dr. Sci. (Eng.), Professor, Department of Management Problems, Institute of Artificial Intelligence

Scopus Author ID 6602931640

78, Vernadskogo pr., Moscow, 119454

kpu-mirea@yandex.ruhttps://orcid.org/0000-0002-6297-8894МанькоС. В.MankoS. V.

Манько Сергей Викторович, д.т.н., профессор, кафедра проблем управления, Институт искусственного интеллекта

Scopus Author ID 55761014700

119454, Москва, пр-т Вернадского, д. 78

Sergey V. Manko, Dr. Sci. (Eng.), Professor, Department of Management Problems, Institute of Artificial Intelligence

Scopus Author ID 55761014700

78, Vernadskogo pr., Moscow, 119454

manko@mirea.ruhttps://orcid.org/0000-0003-3353-9945РомановМ. П.RomanovM. P.

Романов Михаил Петрович, д.т.н., профессор, научный руководитель Института искусственного интеллекта

Scopus Author ID 14046079000

119454, Москва, пр-т Вернадского, д. 78

Mikhail P. Romanov, Dr. Sci. (Eng.), Professor, Academic Supervisor of the Institute of Artificial Intelligence

Scopus Author ID 14046079000

78, Vernadskogo pr., Moscow, 119454

m_romanov@mirea.ruМИРЭА Российский технологический университетРоссияMIREA Russian Technological UniversityRussian Federation2026080820261443652Copyright © Быковцев Ю.А., Лохин В.М., Манько С.В., Романов М.П., 20262026Быковцев Ю.А., Лохин В.М., Манько С.В., Романов М.П.Bykovtsev Y.A., Lokhin V.M., Manko S.V., Romanov M.P.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.rtj-mirea.ru/jour/article/view/1614Цели

Цели. Целью работы является создание средств и методов диагностики автономных роботов для оперативного выявления неисправностей на основе анализа причинно-следственных связей между наблюдаемыми проявлениями и источниками возникновения отказов.

Методы

Методы. В ходе выполнения работы использовались методы аналитического обзора современной научно-технической литературы по тематике проводимых исследований; методы построения онтологий при разработке модели диагностики автономных роботов; методы булевой алгебры при формировании запросов к онтологической модели диагностики; модификации методов условной и безусловной диагностики в приложении к поиску причинно-следственных связей между отказами и неисправностями на основе использования онтологической модели; методы машинного эксперимента при оценке продолжительности обработки запросов к онтологической модели диагностики.

Результаты

Результаты. Предложена онтологическая модель и методы условной и безусловной диагностики, позволяющие обеспечить установление причинно-следственных связей между наблюдаемыми отказами и вызывающими их неисправностями.

Выводы

Выводы. Показано, что на основе онтологического подхода с учетом наличия развитой инструментальной поддержки, включая языки описания онтологий, средства их моделирования, а также технологии логического вывода и рассуждений на онтологиях, создается новая парадигма разработки перспективного класса автономных робототехнических комплексов, обладающих повышенной степенью отказоустойчивости и, соответственно, адаптивностью. Поскольку создание автономного робота предполагает наличие бортовой интеллектуальной системы управления, построенной по иерархическому принципу и содержащей исполнительный, тактический и стратегический уровни, повышение адаптивности может быть обеспечено за счет эффективного использования «богатых» баз алгоритмов интеллектуальной системы управления на всех уровнях иерархии управления, которые подключаются в зависимости от результатов работы онтологической модели.

Objectives

Objectives. The work set out to develop diagnostic tools and methods for autonomous robots for the rapid detection of faults based on the analysis of cause-and-effect relationships between observed manifestations and the sources of failures.

Methods

Methods. The paper presents an analytical review of the current scientific and technical literature on the topic of the research, which includes ontology construction methods for developing a diagnostic model for autonomous robots, Boolean algebra methods for generating queries to the ontological diagnostic model, modifications to conditional and unconditional diagnostic methods for finding cause-and-effect relationships between failures and malfunctions based on the use of an ontological model, and machine experimentation methods for estimating the processing time of queries to the ontological diagnostic model.

Results

Results. The presented ontological model and methods for conditional and unconditional diagnostics is used to establish cause-and-effect relationships between observed failures and the malfunctions that cause them. 

Conclusions

Conclusions. Based on an ontological approach and taking into account the availability of advanced tool support, including ontology description languages, ontology modeling tools, and ontology-based inference and reasoning technologies, a new paradigm is identified for the development of a promising class of autonomous robotic systems with increased fault tolerance and, consequently, adaptability. The development of an autonomous robot presupposes the presence of an onboard intelligent control system built on a hierarchical principle that comprises executive, tactical, and strategic levels. The results of the ontological model provide an effective basis for activating and utilizing the intelligent control system’s rich algorithm databases at all levels of the control hierarchy to support increased adaptability.

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The authors declare that there are no conflicts of interest present.