scholarly journals PREDETERMINED MOVEMENT OF MOBILE ROBOT USING NEURAL NETWORKS

2014 ◽  
pp. 64-68
Author(s):  
Oleh Adamiv ◽  
Vasyl Koval ◽  
Iryna Turchenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mobile Robot ◽  
Formation Process ◽  
Robot Control ◽  
Experimental Results ◽  
The Road ◽  
Mobile Robot Control ◽  
Different Parts ◽  
Training Sets

This paper describes the experimental results of neural networks application for mobile robot control on predetermined trajectory of the road. There is considered the formation process of training sets for neural network, their structure and simulating features. Researches have showed robust mobile robot movement on different parts of the road.

Real-Time Neural Network Based Camera Localization and its Extension to Mobile Robot Control

1997 ◽  
Vol 08 (03) ◽  
pp. 279-293 ◽  
Author(s):  
Doo-Hyun Choi ◽  
Se-Young Oh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mobile Robot ◽  
Real Time ◽  
Robot Control ◽  
Imaging System ◽  
System Modeling ◽  
Hybrid Approach ◽  
Camera Localization ◽  
Mobile Robot Control

The feasibility of using neural networks for camera localization and mobile robot control is investigated here. This approach has the advantages of eliminating the laborious and error-prone process of imaging system modeling and calibration procedures. Basically, two different approaches of using neural networks are introduced of which one is a hybrid approach combining neural networks and the pinhole-based analytic solution while the other is purely neural network based. These techniques have been tested and compared through both simulation and real-time experiments and are shown to yield more precise localization than analytic approaches. Furthermore, this neural localization method is also shown to be directly applicable to the navigation control of an experimental mobile robot along the hallway purely guided by a dark wall strip. It also facilitates multi-sensor fusion through the use of multiple sensors of different types for control due to the network's capability of learning without models.

scholarly journals Addressing the Movement of a Freescale Robotic Car Using Neural Network

2016 ◽  
Vol 24 (39) ◽  
pp. 23-31
Author(s):  
Dušan Horváth ◽  
Peter Cuninka
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mobile Robot ◽  
Robot Control ◽  
Hebbian Learning ◽  
Infrared Sensors ◽  
Guide Line ◽  
Mobile Robot Control ◽  
Direction Of Movement ◽  
Truth Tables

Abstract This article deals with the management of a Freescale small robotic car along the predefined guide line. Controlling of the direction of movement of the robot is performed by neural networks, and scales (memory) of neurons are calculated by Hebbian learning from the truth tables as learning with a teacher. Reflexive infrared sensors serves as inputs. The results are experiments, which are used to compare two methods of mobile robot control - tracking lines.

Mobile robot control by neural networks

1990 ◽  
Vol 21 (7) ◽  
pp. 96-106
Author(s):  
Shigemi Nagata Member ◽  
Nobuo Watanabe ◽  
Kazuo Asakawa Nonmembers
Keyword(s):  
Neural Networks ◽  
Mobile Robot ◽  
Robot Control ◽  
Mobile Robot Control

Mobile robot control using sliding mode and neural network

2003 ◽  
Vol 36 (17) ◽  
pp. 491-496
Author(s):  
Vinícius Menezes de Oliveira ◽  
Walter Fetter Lages ◽  
Edson Roberto de Pieri
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Robot Control ◽  
Sliding Mode ◽  
Mobile Robot Control
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