Enabling flexible and adaptable navigation of ground robots in dynamic environments with live learning

Enabling flexible and adaptable navigation of ground robots in dynamic environments with live learning

Автор: Al-khafaji I.M.A., Alisawi W.Ch., Ibraheem M.Kh., Djuraev Kh.A., Panov A.V.

Журнал: Вестник Южно-Уральского государственного университета. Серия: Компьютерные технологии, управление, радиоэлектроника @vestnik-susu-ctcr

Рубрика: Краткие сообщения

Статья в выпуске: 4 т.23, 2023 года.

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Federated learning is utilized for automated ground robot navigation, enabling decentralized training and continuous model adaptation. Strategies include hardware selection, ML model design, and hyperparameter fine-tuning. Real-world application involves optimizing communication protocols and evaluating performance with diverse network conditions. Federated learning shows promise for machine learning-based life learning systems in ground robot navigation. Research objective: to explore the use of federated learning in automated ground robot navigation and optimize the system for improved performance in dynamic environments.

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Federated learning, life learning, automated navigation, ground robot, machine learning, sensor fusion, dynamic environments

Короткий адрес: https://sciup.org/147242608

IDR: 147242608   |   DOI: 10.14529/ctcr230411

Список литературы Enabling flexible and adaptable navigation of ground robots in dynamic environments with live learning

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