1. CI Controllers for Lego Robots - Comparison Study M. Gavalier, M. Hudec, R. Jakša and P. Sinčák {gavalier,hudecm,jaksa,sincak}@neuron-ai.tuke.sk Dep. Of Cybernetics and AI,TU Košice E-ISCI 2000 Special thanks to Mr. S. Kaleta for his help in design and contruction the position detection system.

2. Structure of Presentation Definiton of Task Setup of the Fuzzy and ANN Controller Lego Robot Comparison of Fuzzy and ANN (+RL) Examples of behavior

3. Definition of task Motivation Our goal is to bring the car from point A to the point B Making a comparison of NN and Fuzzy controllers on the task of “intelligent parking procedure” 2 types of environments

4. Observed parameters The error of parking The error of trajectory

5. Observed parameters Number of collisions with obstacle(s) Number of collisions with borders

6. The model

7. Controller(s) INPUT : – angle of vehicle –x coordinate of vehicle OUTPUT: – steering angle

8. Fuzzy Controller (no obstacles) 35 fuzzy rules IF x=LE AND =RB THEN =PS LE – left RB – right below PS – positive small Defuzzyfication – centroid Mamdami fuzzy controller

9. Membership functions LE – Left LC – Left Center CE – Center RC – Right Center RI – Right RB Right below RU – Right Upper VE - Vertical NB – negative big NM- Negative medium ZE –zero

10. Neural Controller (no obstacles) FF NN Std. Backpropagation 2 input, {5,7,10,20} hidden, 1 output neuron Training data set was produced by Fuzzy C. 3000 path samples were used

11. Experiments (no obstacles) Fuzzy controllerNeuro controller Starting place Target place

12. Experiments (no obstacles) Fuzzy controllerNeuro controller

13. Experiments (RL, no obstacles) 200. trial

14. Experiments (RL, no obstacles) 400. trial

15. Experiments (RL, no obstacles) 600. trial

16. Experiments (RL, no obstacles) 800. trial (last)

17. Results (no obstacles) No. of collisions Error of parking Error of trajectory Fuzzy Controller 8701.2275 Neuro Controller 8501.2133 RL NN controller 28335.261.6324 Ratio of trajectory Error Fuzzy:NN is 1.0117

18. Experiments (with obst.) Fuzzy: added 2 rules for obstacle detection NN: added an NN for control close to obstacle(s)

19. Fuzzy controller

20. Neural Controller

21. NN RL Controller Paths after 100 and 200 trials

22. NN RL Controller Paths after 300 and 400 trials

23. Comparison of controllers (environment with obstacles) 10000 run/paths No. of collision with obstacle (/1path) No. of collisions with border Error of parking Error of trajectory Fuzzy11.86367601.74 Fuzzy20.67215601.63 A4.5368630.00011.86 NN20.28471601.64 NN online0.1157616.41.41 RL0.12261862.861.52

24. Our Robot

25. Moving to the real (fuzzy) SimulatorReal trajectory of robot

26. Moving to the real (neuro) SimulatorReal trajectory of robot

27. Moving to the real Desired path… …and the reality …

28. Conclusion and further work NN ? Fuzzy RL

29. Lego Robot RCX Brick IR sensor IR Port HxWxL : 90x105x150 mm