UM Women's Soccer Team, 1998

Front Row: Nancy Mather, Shannon Poole, Amber Berendowsky, Stephanie McArdle, Kristin Buckley, Mari Hoff, Carrie Brady, Laurie Peterson, Jen Stahl.

Middle Row: Terese Smallwood - manager, Jon Shoenwetter - student athletic trainer, Becky Kozlick, Abby Tompkins, Lauren Clister, Carissa Stewart, Jessica Jones, Vanessa Lewis, Kelly Lukasik, Emily Schmitt, Rex Thompson - certified athletic trainer.

Back Row: Debbie Belkin - head coach, Scott Forrester - assistant coach, Kerry Hood, Jessica Parmalee, Bethany Greenblatt, Jessica Limauro, Kacy Beitel, Marie Spaccarotella,

UM Women's Soccer Team, 1999

Back Row: Assistant Coach Scott Forrester, Becky Kozlick, Abby Crumpton, Andrea Kayal, Jen Stahl, Amber Berendowsky, Mari Hoff, Laurie Peterson, Vicky Whitley, Amy Sullivant. Athletic Trainer Rex Thompson.

Middle Row: Head Coach Debbie Belkin, Aviva Jacobs, Carly Williamson, Lindsay Friedland, Lauren Clister, Carissa Stewart, Bre Bennett, Abby Tompkins, Marie Spaccarotella, Nancy Mather, Student Athletic Trainer Clarissa Charlier.

Front Row: Assistant Coach Carrie Maier, Tammy Mitchell, Michele Pisiri, Jessica Parmalee, Stephanie McArdle, Shannon Poole, Emily Schmitt, Kacy Beitel, Alissa Shaw, Manager Tatiana Anthony.

UM Soccer Team, ca. 1913-1914

Top Row (from left to right): Hui K. Li, John E. Robertson, Landis D. McDowell, William Robertson; Middle Row (from left to right): Constantine D. Tripolitis, Abram B. Coryell, Felix J. Watts, Jacob deLiefde, Clarence I. Shutes; Bottom Row (from left to right): Samuel Cohen, Clarence R. Stallings, Wen H. Pan, William S. James

A global vision system for a robot soccer team

This paper describes the real time global vision system for the robot soccer team the RoboRoos. It has a highly optimised pipeline that includes thresholding, segmenting, colour normalising, object recognition and perspective and lens correction. It has a fast ‘paint’ colour calibration system that can calibrate in any face of the YUV or HSI cube. It also autonomously selects both an appropriate camera gain and colour gains robot regions across the field to achieve colour uniformity. Camera geometry calibration is performed automatically from selection of keypoints on the field. The system acheives a position accuracy of better than 15mm over a 4m × 5.5m field, and orientation accuracy to within 1°. It processes 614 × 480 pixels at 60Hz on a 2.0GHz Pentium 4 microprocessor.

Classifying an opponent's behaviour in robot soccer

This paper illustrates the prediction of opponent behaviour in a competitive, highly dynamic, multi-agent and partially observableenvironment, namely RoboCup small size league robot soccer. The performance is illustrated in the context of the highly successful robot soccer team, the RoboRoos. The project is broken into three tasks; classification of behaviours, modelling and prediction of behaviours and integration of the predictions into the existing planning system. A probabilistic approach is taken to dealing with the uncertainty in the observations and with representing the uncertainty in the prediction of the behaviours. Results are shown for a classification system using a Naïve Bayesian Network that determines the opponent’s current behaviour. These results are compared to an expert designed fuzzy behaviour classification system. The paper illustrates how the modelling system will use the information from behaviour classification to produce probability distributions that model the manner with which the opponents perform their behaviours. These probability distributions are show to match well with the existing multi-agent planning system (MAPS) that forms the core of the RoboRoos system.