Abstract
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.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Miller W (1989) Real-time application of neural networks for sensor-based control of robots with vision. IEEE Trans Syst Man Cybern 19:825–831
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 2959–2964
Kohonen T (1988) Self-organizing maps and associative memory, 2nd edn (Springer series information sciences), vol. 8, Springer, pp 43–48
Wiener J, Burwick T, von Seelen W (2000) Self-organizing maps for visual feature representation based on natural binocular stimuli. Biol Cybern 82:97–110
Behera L, Kirubanandan N (1999) A hybrid neural control scheme for visuo-motor coordination. IEEE Control Syst 19:34–41
Buessler JL, Urban JP (1998) Visual guided movements: learning with modular neural maps in robotics. Neural Networks 11:1395–1415
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 470–475
Marinetz T, Ritter H, Schulten K (1990) Three-dimensional neural net for learning visuo-motor coordination of a robot arm. IEEE Trans Neural Networks 1:131–136
Walter JA, Schulten KJ (1993) Implementation of self-organizing neural networks for visuo-motor control of an industrial robot. IEEE Trans Neural Networks 4:86–95
Zeller M, Sharma R, Schulten K (1997) Motion planning of a pneumatic robot using a neural network. IEEE Control Syst 17: 89–98
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 1013–1017
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 517–522
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 495–500
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 49:230–239
Author information
Authors and Affiliations
Corresponding author
Additional information
This work was presented in part at the 14th International Symposium on Artificial Life and Robotics, Oita, Japan, February 5–7, 2009
About this article
Cite this article
Okada, N., Qiu, J., Nakamura, K. et al. Multiple self-organizing maps for a visuo-motor system that uses multiple cameras with different fields of view. Artif Life Robotics 14, 114 (2009). https://doi.org/10.1007/s10015-009-0639-4
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10015-009-0639-4