In RoboCup, although the fields are standardized and color coded, the area outside the fields often contains ma ny objects of various colors. Sometimes objects off the field ma y look very similar to balls, robots, or other objects normally found on the soccer field. Robots must detect all of these obje cts, and then differentiate between the true positives and false positives. This paper presents a new method using Gaussian fitness scores to differentiate between true positives and f alse positives for balls, robots, and penalty crosses. We also present some other improvements in our code base following our 2012 championship, such as our usage of a virtual base for forward kinematics calculations, our ability to flexibly transitio n player roles given dynamic numbers of teammates, and our ability to quickly integrate new kicks of varying speeds into our strategy. With these improvements, our UT Austin Villa team finished third in the Standard Platform League at RoboCup 2013.
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