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 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 UT

Twente’s robosoccer team Started in 2002: Missing Impossible Mission Impossible Mobile Intelligence

Generations of students 1 st team Ljubljana th team Vienna 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 ) θ) θ x y (x,y)

Result on the screen

Motion Control Robots have local PID velocity controllers Motion commands  wheel speeds (v r, v l ) cq. lin. & ang. velocities (v,  ) Kinematic robot model Higher speeds: account for dynamics!

Motion Planning Driving fast to play the ball while avoiding obstacles …

Strategy ? The team’s magic

System design ? The team’s pain

(Re)designing for the winning team Initial MI20 multi-agent system architecture:

1 st 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 RealPrediction Avoid tracking errors by collision analysis

Collision prediction last 1 pred 1 pred 2 γ last 2

Collision state correction corr 1 corr 2 last 2 pred 2 last 1 pred 1

Collision response model B VBVB P n A VAVA

Collision response (cont.) B VBVB ωBωB A VAVA ωAωA 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?