Design and Implementation of a Fuzzy Path Optimization System for Omnidirectional Autonomous Mobile Robot Control in Real-Time

2018 ◽  
pp. 241-252 ◽  
Author(s):  
Felizardo Cuevas ◽  
Oscar Castillo
Keyword(s):  
Mobile Robot ◽  
Real Time ◽  
Robot Control ◽  
Path Optimization ◽  
Autonomous Mobile Robot ◽  
Optimization System ◽  
Design And Implementation ◽  
Mobile Robot Control

scholarly journals Adaptive ground segmentation method for real-time mobile robot control

2017 ◽  
Vol 14 (6) ◽  
pp. 172988141774813 ◽  
Author(s):  
Hoang Vu ◽  
Hieu Trong Nguyen ◽  
Phuong Minh Chu ◽  
Weiqiang Zhang ◽  
Seoungjae Cho ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Real Time ◽  
Robot Control ◽  
Segmentation Method ◽  
Ground Segmentation ◽  
Mobile Robot Control

1A1-D21 Real time mobile robot control with a multiresolution map representation

2006 ◽  
Vol 2006 (0) ◽  
pp. _1A1-D21_1-_1A1-D21_4
Author(s):  
Katsuya Iwata ◽  
Shinkichi Inagaki ◽  
Yusuke Nara ◽  
Tatsuya Suzuki
Keyword(s):  
Mobile Robot ◽  
Real Time ◽  
Robot Control ◽  
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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