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Proceedings Paper

Use of sensors to deal with uncertainty in realistic robotic environments
Author(s): Enrique Cervera; Angel P. del Pobil; Edward Marta; Miguel Angel Serna
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Paper Abstract

This work is an application of Kohonen's self-organizing feature maps, to deal with uncertainty in realistic robotic environments. The neural network is fed with the signals of a force sensor attached to the wrist of the robot arm. The learning process consists of two phases: a training phase and a labeling phase. After training, the clusters of the map are associated to contact states. There are potential uncertainties in the positions of the elements, which cause error situations, like incorrect grasping of a piece or bad insertions. The error situations can be associated with clusters in the network. As a result of the application of this learning scheme, we can state that Kohonen's self-organizing feature maps are well suited for dealing with uncertainty in realistic robotic environments, particularly robot pick-and-place operations. They are easy to apply and powerful, and are a step towards the solution of more complex intelligent tasks.

Paper Details

Date Published: 6 April 1995
PDF: 8 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205188
Show Author Affiliations
Enrique Cervera, Jaume I Univ. (Spain)
Angel P. del Pobil, Jaume I Univ. (Spain)
Edward Marta, CEIT and Univ. of Navarra (Spain)
Miguel Angel Serna, CEIT and Univ. of Navarra (Spain)

Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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