moitvivt Моделирование, оптимизация и информационные технологии Modeling, Optimization and Information Technology 2310-6018 Издательство 10.26102/2310-6018/2025.49.2.045 1892 Обзор и анализ технологий систем технического зрения, основанных на оптических датчиках для автоматического движения по грунтовым дорогам Review and analysis of optical sensor-based technical vision systems technologies for autonomous navigation on dirt roads 0000-0003-0701-7529 Бычков Александр Михайлович Bychkov Alexander bambam1999@yandex.ru aff-1 0009-0003-4160-5225 Буланов Алексей Алексеевич Bulanov Alexey bulanov@mirea.ru aff-2 МИРЭА - Российский технологический университет MIREA - Russian Technological University МИРЭА - Российский технологический университет MIREA - Russian Technological University 01 01 2026 1 1 10.26102/2310-6018/2025.49.2.045 Copyright © Авторы, 2026 2026 This work is licensed under a Creative Commons Attribution 4.0 International License

Обзор посвящен технологиям технического зрения для автономного движения мобильного робота по грунтовым дорогам и анализу степени их технологической готовности. Выборка работ проводилась согласно методике «PRISMA» при помощи агрегатора научных статей «Google Scholar». На основе анализа работ из полученной выборки были выделены ключевые технологии, включающие в себя: наборы данных, технологии построения карты местности и технологии обнаружения дороги и препятствий. Они, в свою очередь, были поделены на подтехнологии, для каждой из которых был оценен уровень технологической готовности по представленной в работе шкале по новой предложенной интерпретации для шкалы TRL с учетом работы с грунтовыми дорогами, в частности, и со средами, сложно поддающимися исследованию в лабораторных условиях в общем случае. В результате исследования составлена статистика с выделением наиболее актуальных работ в области автономного движения по грунтовым дорогам. Также выведено, что основная тенденция развития навигации – получение и обработка комплексных данных; при анализе проходимости сосредотачиваются на извлечении и обработке геометрических признаков; существует острая необходимость в качественных наборах данных.

This review is devoted to computer vision technologies for the autonomous navigation of a mobile robot on dirt roads, and to analyzing their degree of technological readiness. The selection of studies was conducted according to the PRISMA methodology using the academic article aggregator Google Scholar. Based on the analysis of the works from the selected sample, key technologies were identified, including datasets, terrain mapping techniques, and methods for road and obstacle detection. These were further divided into sub-technologies, each of which was evaluated for its level of technological readiness using the scale presented in the study – a newly proposed interpretation of the TRL scale – taking into account the particular challenges of working on dirt roads and in environments that are generally difficult to replicate under laboratory conditions. As a result of the study, statistics were compiled that highlight the most significant works in the field of autonomous navigation on dirt roads. It was also concluded that the main trend in navigation development involves the acquisition and processing of comprehensive data, that traversability analysis focuses on the extraction and processing of geometric features, and that there is an urgent need for high-quality datasets.

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