• Sorted by Date • Classified by Publication Type • Classified by Topic • Sorted by First Author Last Name •
Jacob Menashe, Katie Genter, Samuel Barrett, and Peter Stone. UT Austin Villa 2013: Advances in Vision, Kinematics, and Strategy. In The Eighth Workshop on Humanoid Soccer Robots at Humanoids 2013, October 2013.
[PDF]350.2kB [postscript]24.1MB
In RoboCup, although the fields are standardized and color coded, the area outside the fields often contains many objects of various colors. Sometimes objects off the field may look very similar to balls, robots, or other objects normally found on the soccer field. Robots must detect all of these objects, 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 false 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 transition 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.
@InProceedings{HUMANOIDS13-menashe,
author = {Jacob Menashe and Katie Genter and Samuel Barrett and Peter Stone},
title = {{UT} {A}ustin {V}illa 2013: Advances in Vision, Kinematics, and Strategy},
booktitle = {The Eighth Workshop on Humanoid Soccer Robots at Humanoids 2013},
location = {Atlanta, GA},
month = {October},
year = {2013},
abstract = {
In RoboCup, although the fields are standardized
and color coded, the area outside the fields often contains many
objects of various colors. Sometimes objects off the field may
look very similar to balls, robots, or other objects normally
found on the soccer field. Robots must detect all of these objects,
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 false
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 transition 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.
},
}
Generated by bib2html.pl (written by Patrick Riley ) on Wed Apr 17, 2024 18:47:34