Enabling flexible and adaptable navigation of ground robots in dynamic environments with live learning
Section: Краткие сообщения
Article in issue: 4 т.23, 2023.
Free access
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.
Similar articles in the section Artificial intelligence
Short address: https://sciup.org/147242608
IDS: 147242608 | UDC: 004.89 | DOI: 10.14529/ctcr230411
Обеспечение гибкой и адаптируемой навигации наземных роботов в динамических средах с помощью интерактивного обучения
Федеративное обучение используется для автоматизированной навигации наземных роботов, обеспечивая децентрализованное обучение и непрерывную адаптацию модели. Стратегии включают выбор оборудования, разработку модели машинного обучения и тонкую настройку гиперпараметров. Реальное приложение включает в себя оптимизацию протоколов связи и оценку производительности в различных сетевых условиях. Федеративное обучение показывает перспективы для систем обучения жизни на основе машинного обучения в навигации наземных роботов. Цель исследования: изучить использование федеративного обучения в автоматизированной навигации наземных роботов и оптимизировать систему для повышения производительности в динамических средах.
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