1. On robots that play soccer René van de Molengraft 11 december 2009

2. RoboCup Mission / faculteit werktuigbouwkunde PAGE 111-12-2009 “By the year 2050, develop a team of fully autonomous humanoid robots that can win against the human world soccer champion team”

3. RoboCup RoboCup Junior / faculteit werktuigbouwkunde PAGE 211-12-2009

4. RoboCup RoboCup@Home / faculteit werktuigbouwkunde PAGE 311-12-2009

5. RoboCup Standard platform league / faculteit werktuigbouwkunde PAGE 411-12-2009

6. RoboCup / faculteit werktuigbouwkunde PAGE 511-12-2009 Humanoid league

7. RoboCup Middle Size League / faculteit werktuigbouwkunde PAGE 611-12-2009

8. Hall Object / faculteit werktuigbouwkunde PAGE 711-12-2009

9. Tech United Eindhoven Facts / faculteit werktuigbouwkunde PAGE 811-12-2009 Started spring 2005 20 active members 2008+2009: second place

10. TU/e – Control Systems Technology / faculteit werktuigbouwkunde PAGE 911-12-2009 Robotics research researchexperimenteren onderwijs RoboCup Robotics in Care and Cure RoboEarth

11. Care – Domestic Robotics / faculteit werktuigbouwkunde PAGE 1011-12-2009

12. Cure – Medical Robotics / faculteit werktuigbouwkunde PAGE 1111-12-2009

13. TU/e – Control Systems Technology / faculteit werktuigbouwkunde PAGE 1211-12-2009 What makes a robot a robot? Autonomous Intelligence Sensors and actuators Complexity  Dynamic, unstructured environment

14. TU/e – Control Systems Technology / faculteit werktuigbouwkunde PAGE 1311-12-2009 Challenges 3D-sensing World modeling Learning

15. TU/e – Control Systems Technology / faculteit werktuigbouwkunde PAGE 1411-12-2009 RoboEarth WWW for and by robots Share any reusable knowledge

16. State of the art - Humanoid / faculteit werktuigbouwkunde PAGE 1511-12-2009 Honda’s Asimo

17. State of the art - Android / faculteit werktuigbouwkunde PAGE 1611-12-2009 REPLIEE, Osaka University, Japan

18. Why humanoid/android robots? / faculteit werktuigbouwkunde PAGE 1711-12-2009 Advantages Familiar Fits environment

19. State of the art - Tele-operated / faculteit werktuigbouwkunde PAGE 1811-12-2009 iBOT, Independence Technology

20. How to build a soccer robot? / faculteit werktuigbouwkunde PAGE 1911-12-2009 Autonomous soccer Field 12x18 meter 5 vs 5 robots Two halves of 15 min. Human referee Design constraints FIFA rules Additions Robocup: size, weight, color Middle Size League

21. How to build a soccer robot? / faculteit werktuigbouwkunde PAGE 2011-12-2009 Fully autonomous Vision and localisation Fast and safe Ball handling (‘do not clamp’) Ball kick Team play Challenges

22. How to build a soccer robot? / faculteit werktuigbouwkunde PAGE 2111-12-2009 Manage complexity Keep it simple Hierarchy Make choices explicit Use models Create robustness Redundancy Rules of thumb

23. How to build a soccer robot? / faculteit werktuigbouwkunde PAGE 2211-12-2009 team robot Bottom up design

24. How to build a soccer robot? / faculteit werktuigbouwkunde PAGE 2311-12-2009 Localisation Soccer skills Intercept, dribble, kick Soccer roles Attacker, defender, goalkeeper Soccer tactics Pass, assist, play system Requirements

25. How to build a soccer robot? / faculteit werktuigbouwkunde PAGE 2411-12-2009 Low level (hardware) functions Drive Kick Ball handling World sensing High level (software) functions Localisation Skills Roles and strategy Functions

26. How to build a soccer robot? / faculteit werktuigbouwkunde PAGE 2511-12-2009 Localisation Which sensors? Vision Odometry Accelerometers Laser Hardware or software? CPU power Sensor fusion Design space exploration

27. How to build a soccer robot? / faculteit werktuigbouwkunde PAGE 2611-12-2009 Interdisiplinary Mechanics Electronics Informatics Specialists needed! Model-based design (simulation and prototyping Early integration Multidisciplinary design

28. How to build a soccer robot? / faculteit werktuigbouwkunde PAGE 2711-12-2009 Evolution and revolution Devbot0 2005 Model 2006 Model 2007 Model 2008 Model 2009

29. How to build a soccer robot? / faculteit werktuigbouwkunde PAGE 2811-12-2009 Motion platform: evolution Base frameClosed box

30. How to build a soccer robot? / faculteit werktuigbouwkunde PAGE 2911-12-2009 Kicker: revolution Pneumatic Solenoid

31. How to build a soccer robot? / faculteit werktuigbouwkunde PAGE 3011-12-2009 Kick variation: (r)evolution

32. How to build a soccer robot? / faculteit werktuigbouwkunde PAGE 3111-12-2009 Hardware Omni-vision camera Electronic compass Odometry with motor encoders Software White-line detection Compass calibration Sensor fusion Localisation

33. How to build a soccer robot? / faculteit werktuigbouwkunde PAGE 3211-12-2009 White line detection

34. How to build a soccer robot? / faculteit werktuigbouwkunde PAGE 3311-12-2009 Avoid obstacles Find ‘open space’ Determine subtargets Obstacle tracker Prediction Motion planning

35. How to build a soccer robot? / faculteit werktuigbouwkunde PAGE 3411-12-2009 Robot architecture omni- drive kicker ball- handling omni- vision compass Motion control kicker control ball control image acquis. compass acquis. localisation task executer skills skill planner HW SW

36. World modeling / faculteit werktuigbouwkunde PAGE 3511-12-2009 Object tracking 5 against 5 >30 kg, 4 m/s Omni-vision: 30 x 640 x 480 x 3 bytes/s Assume features (x, y, r) from omni- vision Noise (shadow, motion blur, finite resolution) and multiple features/object

37. Object tracking / faculteit werktuigbouwkunde PAGE 3611-12-2009 Motion planning

38. Object tracking / faculteit werktuigbouwkunde PAGE 3711-12-2009 At time t1: m1 measurements, n1 objects m1*n1 possible associations At next time t2: m1*n1*m2*n2 Combinatorial explosion 1 2 5 3 4 6

39. Object tracking / faculteit werktuigbouwkunde PAGE 3811-12-2009 2-D position measurements from vision Constant velocity model Stationary Kalman filter State estimation

40. Object tracking / faculteit werktuigbouwkunde PAGE 3911-12-2009 Zero-acceleration Object model

41. Object tracking / faculteit werktuigbouwkunde PAGE 4011-12-2009 Choose uncertainty weigths Solve Riccati equation -> K Variance equation is disregarded Kalman filter

42. Object tracking / faculteit werktuigbouwkunde PAGE 4111-12-2009 Inspired by Schubert and Sidenbladh, Sequential clustering with particle filters – estimating the number of clusters from data, 2005 Prune exploding tree by particle filtering Static objects only Sequential clustering using Kalman filters

43. Object tracking / faculteit werktuigbouwkunde PAGE 4211-12-2009 Prune exploding tree by discrete filtering Track dynamic objects Incorporate a-priori knowledge Sequential clustering using Kalman filters

44. Object tracking / faculteit werktuigbouwkunde PAGE 4311-12-2009 Measurements are processed sequentially Measurement can be associated with clutter new object existing object Sequential clustering using Kalman filters

45. Object tracking / faculteit werktuigbouwkunde PAGE 4411-12-2009 1, 2, 5, 52, 203, 877, 4140, 21147, 115975, 678570 [] [0] [1] [0,0][0,1] [1,0] [1,1][1,2] measurement 1 measurement 2 Tree with all possible associations

46. Object tracking / faculteit werktuigbouwkunde PAGE 4511-12-2009 E.g. H= [0, 1, 1, 2, 1, 2, 3, 0, 1, 1, 1, 4, 2] So, 4 objects -> 4 Kalman filters Each hypothesis is assigned a probability Propagate hypotheses via Bayesian filtering Hypothesis H

47. Object tracking / faculteit werktuigbouwkunde PAGE 4611-12-2009 Prediction

48. Object tracking / faculteit werktuigbouwkunde PAGE 4711-12-2009 is the position of the object in assigned to measurement k Correction

49. Object tracking / faculteit werktuigbouwkunde PAGE 4811-12-2009 Set of hypotheses form a discrete distribution Select n hypotheses with highest probability Throw away hypotheses with P<0.01*Pmax Tree is pruned to at most n hypotheses n = 10 still works for 10 to 20 objects! Pruning by discrete filter

50. Object tracking / faculteit werktuigbouwkunde PAGE 4911-12-2009 Region of interest Clip beyond boundary in state space Measurements Objects in hypotheses Maximum number of objects in hypthesis A priori knowledge

51. Object tracking / faculteit werktuigbouwkunde PAGE 5011-12-2009 Maximum a Posteriori (MAP) estimate states Best estimate

52. Example / faculteit werktuigbouwkunde PAGE 5111-12-2009

53. Implementation / faculteit werktuigbouwkunde PAGE 5211-12-2009 Simulink S-function in C CPU effort linear in #measurements linear in #hypotheses quadratic in #objects < 5 ms (10, 20, 10) Add predictor for anticipation

54. Validation / faculteit werktuigbouwkunde PAGE 5311-12-2009 Tech United – Cambada, july, 2009