1. KREST Institute Summer Research June 27, 2008

2. Project Leader: Dr. Pedro Diaz-Gomez

3. Research Group  Benoit Tufeu  Cora James  Judy Kula  Terri Godman

4. First: What is a Robot?  It is an autonomous system.  It must exist in the physical world.  It must be able to sense its environment.  It can act based on the sensor.  It must achieve a goal. Pg. 2 The Robotics Primer, Maja J. Matarić

5. Is this a robot?

6. Problem:  To design a Braitenberg style robot simulation that would accurately touch a light and then seek the next.

7. A Braitenberg Robot

8. Trial and Error

9. Four Robots Terri Benoit Judy Cora

10. Four Environments Environment 1 Environment 2 Environment 3 Environment 4

11. Four by Four sets of data

12. Statistics on the Four Robots

13. Dr. Diaz’s Analysis of Variables

14. Parameters of the selected robot: Benoit’s Robot B +10 -2 + (variable)

15. Hypothesis  A robot simulation that includes a central sensor with a small positive bias will be more accurate in acquiring and hitting lights then one with a central sensor with a high positive bias.  Note: Bias is a value that controls the speed of the engine based on the intensity of the light.

16. Experiments  Run Robot B through training Environment 4 with 10 trials, varying the bias on the central sensor from 0.0 to 1.0 (in 0.1 intervals).  Run Robot B through a new 14 light test environment with 10 trials, varying the bias in the same manner.

17. New 14 Light Test Environment

18. Varying the Bias in Robot B – Training Environment 4

19. Varying the Bias in Robot B – Test Environment 5

20. Initial Evaluations  After reviewing the data, we found that there was little difference in the accuracy at low biases of 0 to 1.  Further tests were run at biases of 5, 10, 15, and 20 in order to have a wider range of data.

21. Distribution of Training Environment 4

22. Distribution of Test Environment 5

23. Results of ANOVA test  All data from biases 0 to 20 on the central sensor was compared to the number of lights hit.  This showed statistically that bias has an effect on accuracy.

24. Results of ANOVA test for training environment 4

25. Results of ANOVA test for Test Environment 5

26. Analysis of ANOVA tests  The very small P-values statistically show that the bias value of the central sensor has a significant effect on the accuracy of robot performance.  To support our hypothesis that a low bias is more accurate, a KS (Kolmogorov-Smirnov) test was run to compare biases under one to biases greater than one.

27. General Statistics on the Additional Experiments. Training Environment 4 Bias 0.0 to 0.9 Training Environment 4 Bias 1.0 to 20.0 Test Environment 5 Bias 0.0 to 0.9 Test Environment 5 Bias 1.0 to 20.0 Mean7.1853.64011.6710.08 Standard deviation 1.772.45.9582.17 P value0.000 1 st Quartile8.002.0012.08.00 2 nd Quartile8.003.0012.011.00 3 rd Quartile8.004.7512.0 Maximum8.00 12.013.0 Minimum2.000.0012.06.00

28. K-S test results for Training Environment 4 Higher bias Lower bias

29. K-S test results for Test Environment 5 Higher bias Lower bias

30. Conclusions  Based on the results of the KS tests for both environments, a bias below 1 on the central sensor is more accurate then a bias above 1. This is consistent with our hypothesis.

31. Future Research Could Include  a larger trial population in order to be statistically more significant.  more test environments.  more variation of biases.  investigations on biases of the other sensors.  changes in the positions of sensors.  programming that allows for evaluation of environmental conditions

32. Application

33. Building BYO-bots with Dr. Miller

34. The Handy Board: the Brain and Power of the Robot

35. Our First Working Robot

36. Our First Robot in Action

37. A New Prototype

38. Thank You, Gracias, Merci, Dr. Diaz