Playing Robotic Soccer

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Playing Robotic Soccer

• Ofer Elrom
• Dvir Afek

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Why soccer game ?

• Advancing through Competition .
• Wide range of Technologies.
• Many AI challenges.
• A well known game.
• Distribution, Cooperation, Sensing.

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CS Freiburg Team - Germany
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Scope

• Robot hardware.
• Architecture.
• Self Localization Algorithm
• - Scan Matching
• The Global Picture.
• Decision making.
• RoboCup 2000 Video.
• Future

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Robot Hardware

• Pioneer 1 robot.
• Video camera vision system.
• Notebook Pentiumâ running Linux.
• WaveLan Radio Ethernet.
• Laser Range Finders.
• Kicking Mechanism.

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Architecture 1

• Autonomous robotic soccer players.
• Local world model.
• Self Localization.
• Player Recognition.
• Ball Recognition.

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What is Self Localization ?

• The process of updating the Pose of a robot in an
  environment based on sensors readings.
• 3 Major Categories
• Behaviour Based Approaches (blind)
• Land Marks (vision).
• Dense Sensor Matching (laser, sonar).

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Location Realizing

• Problem Given a robot in a known polygonal
  environment, how does the robot realize its
  location?

Who am I? Where am I?
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• Solution - basic algorithmfind your position
  using odometry (x,y,ang)while (1) on every
  forward move d and rotation t x x d
  cos(ang) y y d sin(ang) ang ang
  t find your position using laser scanning
  (x,y,ang) use Kalman Filter to calculate
  new position - (x,y,ang) as a combination of
  (x,y,ang) and (x,y,ang)

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(x,y,ang)
y
x x d cos(ang)y y d
sin(ang)ang ang t
ang

d
y
ang
t
x
x
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• Seems quite simple.But...
• How do you find your position using laser
  scanning?
• solution Line Matching algorithm.

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Line Matching algorithm

• The Robot has an a-priori model (sketch) of the
  environment.
• 1. Scan a group S of lines from your
  environment.
• 2. Find all possible matches between scanned
  lines and lines on the a-priori model. Matches
  dictate location point.
• 3. Choose the most plausible location point.

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A-Priori model of environment
matching..
first match
second match
Real environment
scanning...
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• How do we find all possible matches between a
  group of scanned lines S and a group of model
  lines M?
• we use back tracking recursiveS group of
  scanned linesM group of model linesP a
  group of pairs, in which we matched p lines
  from S with p lines from M.
• What we are looking for is a perfect match
  (discrete math) for group S, or in other words a
  group p for which, P S.

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• The recursive code (Dont panic)
• LineMatch(M, S, P)
• Input model lines M, scan lines S, pairs P
• Output set of possible location points H
• if P S
• H P
• else
• H //Empty group
• s SelectScanLine (S,P)
• for all m in M do
• if VerifyMatch(M, S, P U (m,s) )
• H H U LineMatch(M, S, P U (m,s) )
• return H // Now panic!!!

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Example
m2
p0
m1
m3
,s2
p(s2, m1)
m4
out of the field!!!
p (s2,m1), (s1,m2)
s3
error - killbranch
s1
s2
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Example
m2
p0
m3
m1
,s2
p(s2, m3)
m4
Robot cant be in two places at the same time!!!
p(s2, m3), (s1, m1)
s3
Error, kill branch
s1
s2
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Example
m2
,s2
p0
m3
m1
,s1
p(s1, m3)
m4
p(s1, m3), (s2, m2)
s3
s1
p(s1, m3), (s2, m2), (m4,s3)
s2
Succeeded in finding a location point !!! ( P
s )
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Players Recognition
After finding its location, the robot can scan
other players, whose location is derived from the
robots location. Each Robot sends the
information to the server which can then realize
which of the players is foe and which is friend.
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Ball Recognition

• Color Based Vision System.

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Architecture 1

• Autonomous robotic soccer players.
• Local world model.
• Self Localization.
• Player Recognition.
• Ball Recognition.

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Architecture 2

• Global world model.
• Communication.
• Information to server.
• Clear out! to other players.
• Multi Agent Cooperation (global).
• Action Decision (individual).
• Path Planning.

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Creating World Module
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Global World Model

• Foe / Friend.
• Ball Position.

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Players Actions

• Decision Tree - Parsing on a decision tree
  according to the global world model will lead to
  one of the following actions
• GoTo Position / Ball.
• Observe Ball.
• Search For Ball.
• Move Ball / Kick Ball.
• Clear Out.
• Goal Keeper.

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Ex. A defense player, taking action as the ball
roles in
defense
offense
goalie
Ball approaches
Ball roles away
Observe Ball
In 2nd sector
In my sector
(static)
GoTo Home Position
Block ball
(path planning)
(path planning)
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Path Planning

• Motion Planning with Moving Obstacles..
• Unpredictable Opponents.
• Computationally Demanding.

Approximation
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Players Role
PUT THE BALL IN THE GOAL
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Players Role
Lets watch some game Highlights..
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Whats Next ?

• Learning Techniques.
• Player Communication.
• Dynamic Role Changes.
• Humanoid Robot (2050).
• ..
• ..