A fuzzy-Gaussian neural network and its application to mobile robot control

1996 ◽  
Vol 4 (2) ◽  
pp. 193-199 ◽  
Author(s):  
K. Watanabe ◽  
Jun Tang ◽  
M. Nakamura ◽  
S. Koga ◽  
T. Fukuda
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Robot Control ◽  
Mobile Robot Control

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.

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

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.

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