• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.5C
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• pp.618-626
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• 2004

In this paper, we describe a robot endeffector recognition system based on a Modular Neural Networks (MNN). The proposed recognition system can be used for vision system which track a given object using a sequence of images from a camera unit. The main objective to achieve with the designed MNN is to precisely recognize the given robot endeffector and to minimize the processing time. Since the robot endeffector can be viewed in many different shapes in 3- D space, a MNN structure, which contains a set of feedforwared neural networks, can be more attractive in recognizing the given object. Each single neural network learns the endeffector with a cluster of training patterns. The training MNN patterns for a neural network share the similar characteristics so that they can be easily trained. The trained UM is les s sensitive to noise and it shows the better performance in recognizing the endeffector. The recognition rate of MNN is enhanced by 14％ over the single neural network. A vision system with the MNN can precisely recognize the endeffector and place it at the center of a display for a remote operator.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.496-499
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• 1999

This paper describes a medular neural network(MNN) for a vision system which tracks a given object using a sequence of images from a camera unit. The MNN is used to precisely recognize the given robot endeffector and to minize the processing time. Since the robot endeffector can be viewed in many different shapes in 3-D space, a MNN structure, which contains a set of feedforwared neural networks, co be more attractive in recognizing the given object. Each single neural network learns the endeffector with a cluster of training patterns. The training patterns for a neural network share the similar charateristics so that they can be easily trained. The trained MNN is less sensitive to noise and it shows the better performance in recognizing the endeffector. The recognition rate of MNN is enhanced by 14% over the single neural network. A vision system with the MNN can precisely recognize the endeffector and place it at the center of a display for a remote operator.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.523-526
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• 1996

In this paper, robot endeffector tracking using sensory information from structured laser pattern diode, is described. In order to track robot endeffector robustly irrespective of translation, scaling and rotation of robot working tool, structured laser pattern is used as track feature. Structured laser patterns of crosshair, concentric circles, dot matrix, and parallel lines are illuminated to robot endeffector. Illuminated laser patterns are held invariently and coherently irrespective of various motions of robot endeffector. Extracting and tracking these invariant structured laser patterns as track feature, the whole system keeps tracking of the robot endeffector robustly and effectively provided that structured laser pattern is always assumed to aim at robot endeffector.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1279-1280
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• 1997

The proportion of the aged people is rapidly increasing toward the 21st century. This change will cause serious problems in the area of daily activities, preservation of health, medical care and physical rehabilitatiion for the aged. for the solution of these problems this paper suggest a new type of robot for the aged man, called a silver robot. This paper presents a concept of silver robot and it's feasibility study.