robosoccer team mi20 presents …
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Robosoccer Team MI20 presents …. Mobile Intelligence Twente. Supervisors Albert Schoute Mannes Poel Current team members Paul de Groot Roelof Hiddema. Robot soccer as a scientific “playing field”. Interdisciplinary Hardware & Software Sensing & Control Image processing - PowerPoint PPT PresentationTRANSCRIPT
Robosoccer Team MI20 presents …
Supervisors• Albert Schoute• Mannes Poel
Current team members• Paul de Groot• Roelof Hiddema
Mobile Intelligence Twente
Robot soccer as a scientific “playing field”
Interdisciplinary• Hardware & Software• Sensing & Control• Image processing • Motion planning• Multi-agent collaboration• Communication• Artificial intelligence
International• Championships
(FIRA, Robocup)• Congresses
Mission Impossible ?
International leagues
Robocup• Humanoid• Small size• Middle size• Four-Legged• Rescue• Junior• Simulation• @Home
FIRA• HuroSot• KheperaSot• MiroSot UT team• NaroSot• QuadroSot• RoboSot• SimuroSot
Robocup Humanoid 2 vs. 2 (Osaka 2005)
FIRA Humanoid (Vienna 2003)
Robocup Middle League
Robocup Small League
FIRA Mirosot (11 vs. 11)
• Games between teams of 5, 7 or 11 robots• Camera’s above the field observe the playing• Computers control the robots wirelessly
FIRA Mirosot competitie
MiroSot robots
• Maximal dimensions:7.5 x 7.5 x 7.5 cm
• Two-wheeled differential drive robots
• Board-computer controls wheel velocities
Impression of EC 2005 @ UT
Twente’s robosoccer team
• Started in 2002:
Missing Impossible
Mission Impossible
Mobile Intelligence
Generations of students
1st teamLjubljana 2003
4th teamVienna 2006
Generations of robots
Home base
Computer control
Localization
• Robots have color patches on top
• Design is free, except for obligatory team color
• Design choice:identical or different patterns per robot?
• Identical makes recognition simpler, but robots must be tracked
Vision
Camera image
Color segmentation
Color separation
Region clustering
Camera calibration
• Lens distortion
Image correction
• Remap feature points only
Correction of projective mapping
• Automatic field calibration by 4 known markers
Correct for parallax
Tracking
State estimation
y
x
v
v
y
x
y
x
y
x
State
) θx
y
(x,y)
Result on the screen
Motion Control
• Robots have local PID velocity controllers• Motion commands wheel speeds (vr, vl) cq. lin.
& ang. velocities (v, )• Kinematic robot model
• Higher speeds: account for dynamics!
10
0sin
0cos
y
x
Motion Planning
Driving fast to play the ballwhile avoiding obstacles …
Strategy ?
The team’s magic
System design ?
PlayerAgentPlayerAgentPlayerAgentPlayerAgent
prediction &derivation
Camera
Coachagent
UserInterface
PlayerAgent
Robot
TimageTsettings
Tvision_settings & Tcalibration
Tgame_status & Tscore_board
Tscore_board & Tgame_status
Tworld_data
Tworld_data
Tworld_data
Tcoach_data
Tsensor
Tsensor & Todometrics
Twheel
Motors
State
Tvoltage
Tsensor & Todometrics
Todometrics
User
RFcommunication
Twheel
Tsensor & Todometrics
Vision Measurement
Tsnap_shot
Simulator DLL
Tsnap_shot
Twheel
Tsensor & Todometrics &
Timage
Tgame_status & Tscore_board
Tplayer_data
Tscore_board & Tgame_status
The team’s pain
(Re)designing for the winning team
Initial MI20 multi-agent system architecture:
1st team motion controller
Solve the parking problem:
move to “pose”(x, y, )
… while avoiding obstacles
Vector Field Histogram Corresponding Histogram
Local method:
Trying out in de simulator
Shoot and score!
Shoot and miss!
Improvements
Real Prediction
1
2 2
1
1
2
2
1
Avoid tracking errors by collision analysis
Collision prediction
last1
pred1
pred2
γ
last2
Collision state correction
corr1
corr2last2
pred2
last1
pred1
Collision response model
B
VB
P
n
A
VA
Collision response (cont.)
B
VBωBA
VAωA
P
Improving strategy
Choosing optimal offensive / defensive positions
Improved system structure
• Complete software revision
• Reduced thread concurrency
• Simplified interprocess communication
• Current O.S.Linux Fedora Core 4
Coming soon …
TU Vienna Parade
Questions?