1. Robosoccer Team MI20 presents … Mobile Intelligence Twente • Supervisors • Albert Schoute • Mannes Poel • Current team members • Paul de Groot • Roelof Hiddema

2. 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

3. Mission Impossible ?

4. Robocup Humanoid Small size Middle size Four-Legged Rescue Junior Simulation @Home FIRA HuroSot KheperaSot MiroSot UT team NaroSot QuadroSot RoboSot SimuroSot International leagues

5. Robocup Humanoid 2 vs. 2 (Osaka 2005)

6. FIRA Humanoid (Vienna 2003)

7. Robocup Middle League

8. Robocup Small League

9. FIRA Mirosot (11 vs. 11)

10. FIRA Mirosot competitie • Games between teams of 5, 7 or 11 robots • Camera’s above the field observe the playing • Computers control the robots wirelessly

11. MiroSot robots • Maximal dimensions:7.5 x 7.5 x 7.5 cm • Two-wheeled differential drive robots • Board-computer controls wheel velocities

12. Impression of EC 2005 @ UT

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

14. Generations of students 4th teamVienna 2006 1st teamLjubljana 2003

15. Generations of robots

16. Home base

17. Computer control

18. 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

19. Vision

20. Camera image

21. Color segmentation

22. Color separation

23. Region clustering

24. Camera calibration • Lens distortion

25. Image correction • Remap feature points only

26. Correction of projective mapping • Automatic field calibration by 4 known markers

27. Correct for parallax

28. Tracking

29. y (x,y) )θ x State estimation

30. Result on the screen

31. 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!

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

33. Strategy ? The team’s magic

34. System design ? The team’s pain

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

36. 1st team motion controller Solve the parking problem: move to “pose” (x, y, )

37. … while avoiding obstacles Local method: Vector Field Histogram Corresponding Histogram

38. Trying out in de simulator

39. Shoot and score!

40. Shoot and miss!

41. 2 2 1 1 2 1 1 2 Improvements Avoid tracking errors by collision analysis Real Prediction

42. pred1 pred2 γ last2 last1 Collision prediction

43. pred1 pred2 corr2 corr1 last2 last1 Collision state correction

44. n VA VB A P B Collision response model

45. VA ωA ωB A VB P B Collision response (cont.)

46. Improving strategy Choosing optimal offensive / defensive positions

47. Improved system structure • Complete software revision • Reduced thread concurrency • Simplified interprocess communication • Current O.S.Linux Fedora Core 4

48. Coming soon …

49. TU Vienna Parade

50. Questions?