gyroscopyГироскопия и навигацияGiroskopiya i Navigatsiya0869-70352075-0927AO «Концерн «ЦНИИ «Электроприбор»ZKNABIgyroscopy-10Research ArticleСтатьиАлгоритмы навигации самоходного колесного робота в изменяющейся внешней обстановке, гарантирующие предупреждение столкновений с подвижными и неподвижными препятствиямиAdvanced Control Algorithms for Dynamic Environment Navigation and Obstacle Avoidancehttps://orcid.org/0009-0006-6157-0877АлсайедН.AlsayedN.

Алсайед Нур. Аспирант, факультет систем управления и робототехники

С.-Петербург

Saint-Petersburg

https://orcid.org/0000-0001-6026-6706КрасновА. Ю.KrasnovA. Yu.

Краснов Александр Юрьевич. Кандидат технических наук, преподаватель, факультет систем управления и робототехники

С.-Петербург

Saint-Petersburg

Университет ИТМОРоссияITMO UniversityRussian Federation202416052025323105125Copyright © Алсайед Н., Краснов А.Ю., 20252025Алсайед Н., Краснов А.Ю.Alsayed N., Krasnov A.Y.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.gyroscopy.ru/jour/article/view/10

В статье описывается инновационный метод автономной навигации самоходного колесного робота Pioneer P3-DX в условиях наличия как статических, так и динамических препятствий. Для управления роботом используется алгоритм искусственных потенциальных полей (ИПП), позволяющий рассчитывать безопасную траекторию, а также нейросеть, помогающая классифицировать области вероятной опасности. Три ультразвуковых датчика обеспечивают измерение расстояния для оценки опасности, что наряду с данными об относительной скорости и направлении дает возможность идентифицировать области повышенного (зона 1) и меньшего риска (зона 2). При обнаружении опасности система нечеткой логики обеспечивает эффективное предотвращение столкновения, регулируя скорость колес. Результаты моделирования, проведенного в среде MATLAB и V-REP, демонстрируют эффективность предложенного алгоритма по сравнению с альтернативными подходами для навигации автономных мобильных роботов в сложных условиях с разными уровнями риска. Показано, что алгоритм обладает такими преимуществами, как адаптивность, отказоустойчивость и надежность.

This study proposes an autonomous navigation approach for the Pioneer P3-DX Autonomous Wheeled Robot (AWR) in environments containing both static and dynamic obstacles. The robot utilizes the Artificial Potential Field (APF) algorithm for path calculation, while a neural network aids in zone classification. Three ultrasonic sensors provide distance measurements for hazard assessment. These measurements, along with relative velocity and angle data, aid in identifying regions of elevated risk (Zone 1) and those of lesser risk (Zone 2). Upon hazard detection, fuzzy logic facilitates effective collision avoidance by adjusting wheel velocities. Simulation results conducted in MATLAB and V-REP demonstrate the approach’s efficacy in navigating diverse obstacles, showcasing its adaptability and resilience compared to alternative algorithms. This research introduces an innovative methodology for autonomous mobile robot navigation, emphasizing its reliability and efficiency in traversing intricate environments with varying risk levels.

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