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A hybrid neural network based vision-guided robotic system

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3 Author(s)
K. Stanley ; Innovation Centre, Nat. Res. Council of Canada, Vancouver, BC, Canada ; J. Wu ; W. A. Gruver

There are two primary methods for mapping an input image to robot motion: computed kinematics and visual servoing. Computed kinematics uses a kinematic transform between the image plane and the world frame. Computed kinematics algorithms require only a single iteration, but are sensitive to calibration errors. Visual servoing uses a control law to regulate the image to a desired state. Visual servoing is more robust, but requires more computation to reach a solution. To balance these opposing factors, we proposed a hybrid system that uses an initial computed kinematics move followed by a visual servoing correction, thereby providing a compromise between speed and accuracy. A linear approximation model and a neural network were used to approximate the kinematic transform between the image and world frames. A PD control system is used to regulate the image to its final state

Published in:

IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th  (Volume:1 )

Date of Conference:

25-28 July 2001