Title :
A self-learning robot vision system
Author :
Kobayashi, Hisato ; Uchida, Kenko ; Matsuzaki, Yutaka
Author_Institution :
Dept. of Electr. Eng., Hosei Univ., Tokyo, Japan
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;
Conference_Titel :
Neural Networks, 1991. 1991 IEEE International Joint Conference on
Print_ISBN :
0-7803-0227-3
DOI :
10.1109/IJCNN.1991.170681