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Comparison Study of CI Controllers for Lego Robots

This research explores the performance of Fuzzy and ANN controllers on an intelligent parking task with Lego robots. It compares trajectory errors, collisions with obstacles and borders, using different types of controllers. The study includes experiments with and without obstacles, showcasing the efficiency of each controller type. Ultimately, the conclusion and further work suggest potential improvements for neural network controllers.

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Comparison Study of CI Controllers for Lego Robots

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  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) Target place Starting place Fuzzy controller Neuro controller

  12. Experiments (no obstacles) Fuzzy controller Neuro 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) 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)

  24. Our Robot

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

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

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

  28. Conclusion and further work • NN ? Fuzzy • RL

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

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