Robust Color Classification Using Fuzzy Reasoning and Genetic Algorithms in RoboCup Soccer Leagues

Authors:
Alireza Kashanipour;Amir Reza Kashanipour;Nargess Shamshiri Milani;Peyman Akhlaghi;Kaveh Khezri Boukani
Affiliations:
Mechatronic Research Labratory Dept. of Electrical and Computer Engineering, Azad University, Qazvin, Iran;Dept. of Electrical Engineering, Sahand University of Technolegy, Tabriz, Iran;Dept. of Electrical Engineering, Sahand University of Technolegy, Tabriz, Iran;Dept. of Electrical Engineering, Sahand University of Technolegy, Tabriz, Iran;Dept. of Electrical Engineering, Sahand University of Technolegy, Tabriz, Iran
Venue:
RoboCup 2007: Robot Soccer World Cup XI
Year:
2008

Citing 4
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Abstract

Color segmentation is typically the first step of vision processing for a robot operating in a color coded environment, like RoboCup soccer, and many object recognition modules rely on that, in this paper we present a method for color segmentation that is based on fuzzy logic. Fuzzy sets are defined on the H, S and L components of the HSL color space and provide a fuzzy logic model that aims to follow the human intuition of color classification. The membership functions used for the fuzzy inference are optimized by genetic algorithms. The method requires the setting of only a few parameters and has been proved to be very robust to noise and light variations, allowing for setting parameters only once. The approach has been implemented on MRL middle size robots, and successfully experimented in the numbers of the friendly matches of the Middle size in the 2006's games.