Affiliations: Innovation Centre -- Vancouver, National Research
Council of Canada, 3250 East Mall, Vancouver, BC, Canada V6T1W5 | Mechanical Engineering, University of British
Columbia, Vancouver, BC, Canada V6T1Z1 | Engineering Science, Simon Fraser University, Burnaby,
BC, Canada V5A1S6
Abstract: One of the essential problems of feature-based visual servoing is
calculating the inverse Jacobian, which relates changes in features to changes
in robot position. Neural networks can approximate the inverse feature
Jacobian. Neural networks also allow other forms of vision input to be easily
used to position the robot. The vision system is primarily responsible for
reducing the dimensionality of the input to reduce the size and therefore
computational load on the system. In this paper we develop a system which uses
neural networks to both encode the image and generate control signals. In our
system, the image dimensionality can be reduced in four ways: feature
extraction, averaging compression, vector quantization, and principal component
expansion. We demonstrate that it is possible to use neural networks for both
image analysis and control of a vision guided robot, with little loss of
accuracy when compared to feature based extraction.
