• 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