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Proceedings Paper

Neural-network approach to determine operator hand orientation for teleoperated control of a robot manipulator
Author(s): Cedric Cocaud; Jonathan Kofman; Amor Jnifene
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Paper Abstract

In robot-manipulator teleoperation, vision-based tracking of the human operator motion offers a non-contacting approach that permits unhindered operator motion. To control the robot manipulator, the three-dimensional (3D) position and orientation of the arm of the operator is required. This paper presents a neural-network (NN) based method of determining the orientation of the human hand using non-invasive markerless vision-based tracking. The tracking method uses images of the hand from two fixed cameras to determine three angles of hand orientation. The neural network processing to determine the hand orientation consists of five procedures. First, a preprocessing system performs basic transformations on the input images to prepare them to be interpreted by the neural network. Secondly, an unsupervised neural network extracts relevant local features necessary to recognize the input patterns. Thirdly, a self-organizing neural network combines the local features of the previous network to identify the global pattern. Next, a modified radial-basis function (RBF) neural network calculates the probabilities that a given input pattern corresponds to each basic pattern, for which the RBF NN was trained. Finally, the orientation of the hand is interpolated between these basic patterns by calculating the weighted average of the most probable configurations identified by the RBF NN.

Paper Details

Date Published: 25 October 2004
PDF: 12 pages
Proc. SPIE 5602, Optomechatronic Sensors, Actuators, and Control, (25 October 2004); doi: 10.1117/12.580349
Show Author Affiliations
Cedric Cocaud, Univ. of Ottawa (Canada)
Jonathan Kofman, Univ. of Ottawa (Canada)
Amor Jnifene, Univ. of Ottawa (Canada)

Published in SPIE Proceedings Vol. 5602:
Optomechatronic Sensors, Actuators, and Control
Kee S. Moon, Editor(s)

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