title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð°Ð´Ð°Ð¿ÑÐ¸Ð²Ð½Ð¾Ð¹ ÑÐ¸ÑÑÐµÐ¼Ñ ÑÑÑÐºÐ¾Ð²ÐºÐ¸ ÑÐ¾Ð±Ð¾ÑÐ¾ÑÐµÑ Ð½Ð¸ÑÐµÑÐºÐ¸Ñ ÐºÐ¾Ð¼Ð¿Ð»ÐµÐºÑÐ¾Ð² ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ ÑÐ¸Ð¿Ð¾Ð² Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Development of an algorithm of adaptive system of docking of robotic complexes of different types using machine learning methods
Creators: name: ÐÐ¾Ð·Ð´Ð½ÑÐºÐ¾Ð², ÐÐ»Ð°Ð´Ð¸Ð¼Ð¸Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐÐ»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ
Ð Ð¾Ð±Ð¾ÑÐ¾ÑÐµÑ Ð½Ð¸ÑÐµÑÐºÐ¸Ðµ ÑÐ¸ÑÑÐµÐ¼Ñ
Ð¼ÐµÑÐ¾Ð´
ÑÐ¸ÑÑÐµÐ¼Ð° ÑÑÑÐºÐ¾Ð²ÐºÐ¸
Ð°Ð´Ð°Ð¿ÑÐ¸Ð²Ð½ÑÐ¹ Ð±ÑÑÑÐ¸Ð½Ð³
Ð±ÐµÑÐ¿Ð¸Ð»Ð¾ÑÐ½ÑÐµ Ð°Ð¿Ð¿Ð°ÑÐ°ÑÑ
Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÑÐ°Ð»ÑÐ½ÑÐµ ÑÐ¸ÑÑÐµÐ¼Ñ ÑÐ¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ
Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
method
docking system
adaptive boosting
unmanned vehicles
intelligent control systems
learning models

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description: This work is devoted to the development of an algorithm of adaptive system of docking of robotic complexes of different types using machine learning methods. The purpose of this study is to increase the autonomy of functioning of robotic complexes of different types by developing and applying the algorithm of adaptive docking. The main tasks solved in this work are: monitoring and evaluation of the environment; detection of objects and obstacles; maneuvering; approaching an object. To solve these problems, the paper proposes the use of trainable models, which is a promising approach for the creation of adaptive docking systems capable of adapting to different environmental conditions and scenarios, learning from experience and data. In this paper, we have developed an adaptive docking algorithm that forms a flexible trainable model for the control system of unmanned robotic complexes oper-ating in an unformalized environment. The developed adaptive docking algorithm will contribute to increasing the efficiency of operations performed by robotic complexes of various types through the use of machine learning methods, as well as reducing the time and costs associated with their operation in the conditions of changes in the meteorological situation and the appearance of obstacles, due to the development of the algorithm in a virtual environment.