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CSFRIEBURG 1998
Team Analysis for Middle Size
League in RoboCup
Jaisudha Purushothaman
Why Robocup?
 Alan Mackworth (1993)
proposed soccer as a test
bed for AI and Robotics
research.
 Landmark Project and
Standard Problem
 American Space Project
Apollo
Chess
RoboCup
Environment
Static
Dynamic
Information
Accessibility
Complete
Incomplete
Sensor
Readings
Symbolic
Non-symbolic
Control
Central
Distributed
ULTIMATE GOAL: By 2050, develop a team of fully
autonomous humanoid robots that can win against the
human world champion team in soccer
Research Areas Covered
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Real Time Sensor Fusion
Reactive Behavior
Strategy Acquisition
Learning
Real Time Planning
Multi-agent Systems
Context recognition
Vision
Strategic Decision Making
Motor Control
Intelligent Robot Control
Soccer Rules!
 Simulation League
 Small Size League
 Middle Size League
--Size and color of 3X3 ping pong tables
--Up to 5 robots per team
--Futsal-4 ball
--Size of base of robot upto 50 cm
--No global vision
--Colored Goals
--Walls surrrounding field
The Team
2nd place at RoboCup German Open
2002, Paderborn, April 2002
World champion at RoboCup 2001,
Seattle, August 2001
Champion at RoboCup German Open
2001, Paderborn, June 2001
World champion at RoboCup 2000,
Melbourne, September 2000
German champion at VISION
RoboCup'99, Stuttgart, October 1999
3rd place at RoboCup'99, Stockholm,
August 1999
German champion at VISION
RoboCup'98, Stuttgart, October 1998
World champion at RoboCup'98,
Paris, July 1998
Team Players
Pioneer 1 Robot developed by Kurt
Kolenidge & manufactured by
ActivMedia
Video Camera to Cognachrome
Vision system manufactured by
Newton Lab
PLS200 Laser Range Finders
manufactured by SICK AG – 1800
field of view and .50 angular
resolution
Toshiba Notebook Libretto 70CT
running Linux
Wavelan Radio Ethernet
Ball Handling Mechanism
Player Architecture
Team Architecture
Self Localization
Perception Module
Global World Model
Basic Skills
 Goalkeeper
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Head mounted to right
Always watches the ball
Moves to point where it expects to intercept the ball
 Field Player
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Approach-Postion
Go-to-Position
Observe-Ball
Search-Ball
Move-Ball
Shoot-Ball
Action Selection (Decision Tree)
Roles and Areas of Competence
Path Planning Module
World Model
Graph Building
A* Search
Path Test
Introspection
Path
Path Planning Example
Other Techniques & Issues of Interest
 UttoriUnited and RMIT omni directional driving
mechanisms
 Principles of Minimal Control (Ullanta)
 Vision Guided Behavior Acquisition (Osaka)
 Dynamic Role Changing (CMUnited)
 Explicit World Model alternative :
Action Based Sensor Space Categorization for Robot
Learning.
 Osaka and its Improvements over the years
 CSFreiburg’s Improvements till 2001
Robocup Applications
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Robocup Rescue
The Future University – Hakodate
Education and Edutainment
Rocco – Live Commentator
The Licentiate Thesis of Johan
Kummeneje, Stockholm University
My Perfect Team
CSFreiburg with:
 Training to Shoot by Offline Learning
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A Defense Strategy ( Ball Intercept already inbuilt)
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Dynamic Role Change between Forwarder and
Defender
Issues :
 What if there is No Communication allowed?
 What if the same teams play against each other?
1998 Final Goal (T-Team)
2001 Final Goal (Trackies)
Papers
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Robocup: Robot World Cup by Kitano, Asada, Noda and Matsubara
What we learned from Robocup-97 and Robocup-98 by
Asada,Suzuki,Veloso and Kitano
The CS Freiburg Team Playing Robotic Soccer in an Explicit
World Model by J-S. Gutmann, W. Hatzack, I. Herrmann, B.
Nebel,F.Rittinger, A. Topor, and T. Weigel
Fast, Accurate, and Robust Self-Localization in Polygonal
Environments by J.-S. Gutmann, T. Weigel, B. Nebel.
The CS Freiburg Robotic Soccer Team: Reliable SelfLocalization, Multirobot Sensor Integration, and Basic Soccer
Skills by J.-S. Gutmann, W. Hatzack,I. Herrmann,B. Nebel,F. Rittinger,
A. Topor,T. Weigel, and B. Welsch.
CS Freiburg 2001 by T. Weigel, A. Kleiner, F. Diesch, M. Dietl, J.-S.
Gutmann, B. Nebel, P. Stiegeler and B. Szerbakowski
CS Freiburg Doing the Right Thing in a Group by Weigel et al.
More Papers
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Osaka University “Trackies 2002” by Takahashi, Hikiti, Katoh,
Chaladhorn, Edazawa and Asada
An overview of Robocup Physical Challenge: Phase I by Asada
Robocup Rescue: A grand Challenge for Multi-Agent Systems by
Kitano
Vision Guided Behavior Acquisition of a Mobile Robot by MultiLayered Reinforcement Learning by Takahashi and Asada
Priniciples of Minimal Control of a Comprehensive Team
Behavior by Brian Werger
Osaka University Trackies 2003 by Takahashi et al.
Strategy Classification in Multi-Agent Environment Applying
Reinforcement Learning to Soccer Agents by Asada et al
Vision based Robot Learning Towards Robocup: Osaka
University Trackies by Takahashi et al
Reasonable Performance in Less Learning Time by Real Robot
Based on Incremental State Space Segmentation by Takahashi,
Asada and Hosoda 1996
Papers, Links and Books
Cooperative Team Play Based on Communication by K. Yokota, K.
Ozaki, N.Watanabe,A. Matsumoto, D. Koyama, T. Ishikawa,
K. Kawabata, H. Kaetsu and H. Asama.
Journal:
Artificial Intelligence ,July 1999, Vol.110 no.2 ,Special Issue :
RoboCup The First Step by Kitano guest editor
Book:
Robotics by Marvin Minsky
Websites:
www.cs-freiburg.de
www.robocup.org
www.csl.sony.co.jp/person/kitano/RoboCup/RoboCup-old.html
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So Finally Question Time Now !
Semifinal 2001 goal (Stuttgart)
99 Preliminaries (RMIT)
00 Quarter Final (CMU)
Tricking the Goalie (CE)
The Visibility Graph Method
GOAL
START
The Visibility Graph Method (cont’d)
GOAL
START
The Visibility Graph Method (cont’d)
GOAL
START
The Visibility Graph Method (cont’d)
GOAL
START
The Visibility Graph Method (cont’d)
We can find the shortest path
using Dijkstra’s Algorithm
START
GOAL
Path