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Designing FIRA Medium-Sized Soccer Robot Vision System Using Particle Swarm Optimization

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Abstract:

Enabling FIRA medium-sized soccer robots to recognize target objects according to color information requires that competing teams manually set the range of colors according to ambient lighting conditions prior to games. This color information is used to differentiate features of target objects, such as the ball, the goals, and the field. Constructing a color-feature model such as this is extremely time-consuming and the resulting model is unable to adapt dynamically to changes in lighting conditions. This study applied a look-up table method to execute RGB-HSV color space conversion to accelerate video processing. A particle swarm optimization (PSO) scheme was developed to detect the color-feature parameters of the target objects in the HSV color space. This enables the automatic completion of color-feature modeling and the construction of the knowledge model required by the robot for object recognition. Experiment results demonstrate that the proposed method is capable of enhancing the robustness of the robot vision system in determining changes in lighting conditions. In addition, the manpower and time required to set robot parameters prior to games were reduced significantly.

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Info:

Periodical:

Applied Mechanics and Materials (Volumes 764-765)

Pages:

675-679

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.764-765.675

Citation:

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Online since:

May 2015

Authors:

Ching Yi Chen*, Chi Chiang Ko

Keywords:

Color-Feature Model, PSO, Soccer Robots

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© 2015 Trans Tech Publications Ltd. All Rights Reserved

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* - Corresponding Author

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