Artificial Life and Robotics

, 14:114 | Cite as

Multiple self-organizing maps for a visuo-motor system that uses multiple cameras with different fields of view

  • Nobuhiro Okada
  • Jinjun Qiu
  • Kenta Nakamura
  • Eiji Kondo
Original Article


This article proposes multiple self-organizing maps (SOMs) for control of a visuo-motor system that consists of a redundant manipulator and multiple cameras in an unstructured environment. The maps control the manipulator so that it reaches its end-effector at targets given in the camera images. The maps also make the manipulator take obstacle-free poses. Multiple cameras are introduced to avoid occlusions, and multiple SOMs are introduced to deal with multiple camera images. Some simulation results are shown.

Key words

Robot vision systems Self-organizing maps 


  1. 1.
    Miller W (1989) Real-time application of neural networks for sensor-based control of robots with vision. IEEE Trans Syst Man Cybern 19:825–831CrossRefGoogle Scholar
  2. 2.
    Carusone J, Eleurterio G (1998) The feature CMAC: a neuralnetwork-based vision system for robotic control. Proceedings of the IEEE International Conference on Robotics and Automation, vol 4, pp 2959–2964Google Scholar
  3. 3.
    Kohonen T (1988) Self-organizing maps and associative memory, 2nd edn (Springer series information sciences), vol. 8, Springer, pp 43–48MathSciNetGoogle Scholar
  4. 4.
    Wiener J, Burwick T, von Seelen W (2000) Self-organizing maps for visual feature representation based on natural binocular stimuli. Biol Cybern 82:97–110CrossRefGoogle Scholar
  5. 5.
    Behera L, Kirubanandan N (1999) A hybrid neural control scheme for visuo-motor coordination. IEEE Control Syst 19:34–41CrossRefGoogle Scholar
  6. 6.
    Buessler JL, Urban JP (1998) Visual guided movements: learning with modular neural maps in robotics. Neural Networks 11:1395–1415CrossRefGoogle Scholar
  7. 7.
    Buessler JL, Kara R, Wira P, et al (1999) Multiple self-organizing maps to facilitate the learning of visuo-motor correlations. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp 470–475Google Scholar
  8. 8.
    Marinetz T, Ritter H, Schulten K (1990) Three-dimensional neural net for learning visuo-motor coordination of a robot arm. IEEE Trans Neural Networks 1:131–136CrossRefGoogle Scholar
  9. 9.
    Walter JA, Schulten KJ (1993) Implementation of self-organizing neural networks for visuo-motor control of an industrial robot. IEEE Trans Neural Networks 4:86–95CrossRefGoogle Scholar
  10. 10.
    Zeller M, Sharma R, Schulten K (1997) Motion planning of a pneumatic robot using a neural network. IEEE Control Syst 17: 89–98CrossRefGoogle Scholar
  11. 11.
    Zha HB, Onitsuka T, Nagata T (1996) A visuo-motor coordination algorithm for controlling arm movements in environments with obstacles. Proceedings of the 4th International Conference on Control, Automation, Robotics and Vision, pp 1013–1017Google Scholar
  12. 12.
    Okada N, Shimizu Y, Maruki Y, et al (1999) A self-organizing visuo-motor map for a redundant manipulator in an environment with obstacles. Proceedings of the 9th International Conference on Advanced Robotics, pp 517–522Google Scholar
  13. 13.
    Han M, Okada N, Kondo E (2003) Collision avoidance for a visuo-motor system using a self-organizing map in a 3D space. 6th Japan- France Congress on Mechatronics, pp 495–500Google Scholar
  14. 14.
    Han M, Okada N, Kondo E (2006) Coordination of an uncalibrated 3-D visuo-motor system based on multiple self-organizing maps. JSME Int J Ser C 49:230–239CrossRefGoogle Scholar

Copyright information

© International Symposium on Artificial Life and Robotics (ISAROB). 2009

Authors and Affiliations

  • Nobuhiro Okada
    • 1
  • Jinjun Qiu
    • 1
  • Kenta Nakamura
    • 1
  • Eiji Kondo
    • 1
  1. 1.Department of Intelligent Machinery and SystemsKyushu UniversityFukuokaJapan

Personalised recommendations