Navigation learning system for mobile robot in heterogeneous environment: Inductive modeling approach; Conference on Computer Science and Information Technologies (CSIT); Vol. 1

Navigation learning system for mobile robot in heterogeneous environment: Inductive modeling approach; Conference on Computer Science and Information Technologies (CSIT); Vol. 1

Bibliografski detalji
Parent link:Conference on Computer Science and Information Technologies (CSIT): proceedings of the XIIth International Scientific and Technical Conference CSIT 2017, 05-08 September 2017, Lviv, Ukraine
Vol. 1.— 2017.— [P. 543-548]
Glavni autor: Andrakhanov A. A. Anatoliy Aleksandrovich
Autor kompanije: Национальный исследовательский Томский политехнический университет Инженерная школа информационных технологий и робототехники
Daljnji autori: Belyaev A. S. Aleksandr Sergeevich
Sažetak:Title screen
One of the key tasks of mobile robotics is navigation, which for Outdoor-type robots is exacerbated by the functioning in a priori of an unknown environment. In this paper, for the first time, the learning navigation system for mobile robot based on inductive modeling approach is presented. This approach is based on the principles of the group method of data handling (GMDH), which is one of the first techniques of Deep Learning. The paper presents the results of training models for estimating the robot's coordinates and angular orientation in heterogeneous environment. In addition to the direct readings of the on-board sensors, additional parameters were introduced to train the models, reflecting how the robot perceives the surface terramechanics. The models for estimation of the coordinates on the surface areas of various types and classifiers of the surface type were trained. The obtained results testify the efficiency of the developed Navigation Leaning System for Mobile Robot (NLS MR).
Jezik:engleski
Izdano: 2017
Teme:
труды учёных ТПУ
электронный ресурс
мобильные роботы
гетерогенные среды
испытания
навигация
машинное обучение
групповые методы
обработка данных
нейроны
Online pristup:https://doi.org/10.1109/STC-CSIT.2017.8098846
Format: Elektronički Poglavlje knjige
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=657652

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