Building Maps of Workspace for Autonomous Mobile Robots Using Self-Organizing Neural Networks

Author(s):  
Katsuhiro Hori ◽  
Yukio Hashimoto ◽  
Shouyu Wang ◽  
Takeshi Tsuchiya
1970 ◽  
Vol 110 (4) ◽  
pp. 101-104 ◽  
Author(s):  
T. Proscevicius ◽  
A. Bukis ◽  
V. Raudonis ◽  
M. Eidukeviciute

Methods for intelligent mobile robots control which are based on principles of hierarchical control systems will be reviewed in this article. Hierarchical intelligent mobile robots are new direction for development of robotics, which have wide application perspectives. Despite increasing progress in technologies, the main problem of autonomous mobile robots development is that, they are ineffective in their control. In each of the hierarchical control levels (movement in space, problems solving and signal processing sets) will define by specific management of objectives, goals and rules. Communication and management between hierarchies are implemented by higher level of hierarchy using obtained information about the environment and lover level of hierarchy. Studies have shown that artificial neural networks, fuzzy logic are widely used for the development of the hierarchical systems. The main focus of the work is on communications in hierarchy levels, since the robot must be controlled in real time. Ill. 4, bibl. 13 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.110.4.298


2011 ◽  
Vol 2 (1) ◽  
pp. 45-50
Author(s):  
L. Ţepelea ◽  
I. Gavriluţ ◽  
A. Gacsádi ◽  
V. Tiponuţ

Abstract The paper presents an image-based algorithm for motion-planning of two mobile robots moving to the same target in an environment with obstacles. Due to parallel computing, the Cellular Neural Networks (CNN) techniques ensure the images processing in real-time and represent an advantageous solution for autonomous mobile robots guidance. The path planning algorithm can be improved increasing the speed of image processing, using advanced type of the CNN implementation and it can be extended for three or more robots.


1995 ◽  
Vol 40 (11) ◽  
pp. 1110-1110
Author(s):  
Stephen James Thomas

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