An approach to self-training of the mobile robot

Author

Golovko, Vladimir ; Ignatiuk, Oleg ; Sadykhov, Rauf

Author_Institution

Brest Tech. Univ., Byelorussia

fYear

2001

fDate

2001

Firstpage

11

Lastpage

15

Abstract

The unsupervised learning of an autonomous mobile robot is a real research topic. It permits an artificial system to interact successfully with its environment and to avoid obstacles. This paper presents an intelligent control architecture which integrates self-training methods and is able to operate in complex, unknown environments in order to achieve its target. Our approach is based on reactive obstacle avoidance. The intelligent model integrates different neural networks and permits on-line learning. The results of experiments are discussed

Keywords

collision avoidance; mobile robots; neurocontrollers; unsupervised learning; autonomous mobile robot; intelligent control architecture; mobile robot; neural networks; obstacle avoidance; on-line learning; reactive obstacle avoidance; self-training; self-training methods; unsupervised learning; Artificial intelligence; Artificial neural networks; Cybernetics; Intelligent control; Intelligent networks; Intelligent robots; Mobile robots; Neural networks; Supervised learning; Unsupervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, International Workshop on, 2001.

Conference_Location

Crimea

Print_ISBN

0-7803-7164-X

Type

conf

DOI

10.1109/IDAACS.2001.941969

Filename

941969