Mr. Sung-Hyun Baek Profile

Sung-Hyun Baek

at Agency for Defense Development

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Proceedings Article | 7 June 1996 Paper

Neural net target-tracking system using structured laser patterns

Jae-Wan Cho, Yong-Bum Lee, Nam-Ho Lee, Soon-Yong Park, Jongmin Lee, Gapchu Choi, Sunghyun Baek, Dong-Sun Park

Proceedings Volume 2739, (1996) https://doi.org/10.1117/12.241934

KEYWORDS: Neural networks, Sensors, Semiconductor lasers, Cameras, Laser systems engineering, Artificial neural networks, Robotic systems, Motion estimation, Edge detection, Computing systems

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In this paper, we describe a robot endeffector tracking system using sensory information from recently-announced structured pattern laser diodes, which can generate images with several different types of structured pattern. The neural network approach is employed to recognize the robot endeffector covering the situation of three types of motion: translation, scaling and rotation. Features for the neural network to detect the position of the endeffector are extracted from the preprocessed images. Artificial neural networks are used to store models and to match with unknown input features recognizing the position of the robot endeffector. Since a minimal number of samples are used for different directions of the robot endeffector in the system, an artificial neural network with the generalization capability can be utilized for unknown input features. A feedforward neural network with the generalization capability can be utilized for unknown input features. A feedforward neural network trained with the back propagation learning is used to detect the position of the robot endeffector. Another feedforward neural network module is used to estimate the motion from a sequence of images and to control movements of the robot endeffector. COmbining the tow neural networks for recognizing the robot endeffector and estimating the motion with the preprocessing stage, the whole system keeps tracking of the robot endeffector effectively.

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