DocumentCode
2642629
Title
A self-learning robot vision system
Author
Kobayashi, Hisato ; Uchida, Kenko ; Matsuzaki, Yutaka
Author_Institution
Dept. of Electr. Eng., Hosei Univ., Tokyo, Japan
fYear
1991
fDate
18-21 Nov 1991
Firstpage
2007
Abstract
The authors propose a self-learning strategy for robot vision systems which are used to identify the position of the target part handled by a robot. They tried to use a neural network as a decision-making system which determines how to move the robot to reach the exact target on the base of the image acquired by the robot eye. The authors taught this function automatically to the neural network. The total system works as follows: (1) a target object is set at a known position, and the position is taught to the system, (2) the robot moves randomly around the target and the neural network learns the relation between the relative positions and images, and (3) after enough learning, the robot can identify the target located at an arbitrary position
Keywords
computer vision; learning systems; neural nets; robots; self-adjusting systems; computer vision; decision-making system; neural network; self-learning robot vision system; Decision making; Education; Educational robots; Image processing; Manufacturing systems; Neural networks; Orbital robotics; Robot motion; Robot vision systems; Robotics and automation;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1991. 1991 IEEE International Joint Conference on
Print_ISBN
0-7803-0227-3
Type
conf
DOI
10.1109/IJCNN.1991.170681
Filename
170681
Link To Document