KREST Institute Summer Research
June 27, 2008
Project Leader:
Dr. Pedro Diaz-Gomez
Research Group
Benoit Tufeu
 Cora James
 Judy Kula
 Terri Godman

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
Is this a robot?
Problem:

To design a Braitenberg style robot
simulation that would accurately touch a
light and then seek the next.
A Braitenberg Robot
Trial and Error
Four Robots
Terri
Benoit
Judy
Cora
Four Environments
Environment
1
Environment
Environment
42
Environment 3
Four by Four sets of data
Statistics on the Four Robots
Dr. Diaz’s Analysis of Variables
Parameters of the selected robot:
Benoit’s Robot B
+10
-2
+
(variable)
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.

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.

New 14 Light Test Environment
Varying the Bias in Robot B –
Training Environment 4
Varying the Bias in Robot B –
Test Environment 5
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.

Distribution of Training Environment 4
Distribution of Test Environment 5
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.

Results of ANOVA test for training
environment 4
Results of ANOVA test for Test
Environment 5
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.

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
Mean
7.185
3.640
11.67
10.08
Standard
deviation
1.77
2.45
.958
2.17
P value
0.000
0.000
0.000
0.000
1st Quartile
8.00
2.00
12.0
8.00
2nd Quartile
8.00
3.00
12.0
11.00
3rd Quartile
8.00
4.75
12.0
12.0
Maximum
8.00
8.00
12.0
13.0
Minimum
2.00
0.00
12.0
6.00
K-S test results for Training
Environment 4
Higher bias
Lower bias
K-S test results for Test
Environment 5
Higher bias
Lower bias
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.
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

Application
Building BYO-bots with Dr. Miller
The Handy Board: the Brain and
Power of the Robot
Our First Working Robot
Our First Robot in Action
A New Prototype
Thank You,
Gracias,
Merci,
Dr. Diaz