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 2000Special thanks to Mr. S. Kaleta for his help in design and contruction the position detection system.

Structure of Presentation

• Definiton of Task

• Setup of the Fuzzy and ANN Controller

• Lego Robot

• Comparison of Fuzzy and ANN (+RL)

• Examples of behavior

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

Observed parameters

• The error of parking

• The error of trajectory

222 )()()( yyxx fff

trajectoryoptimaloflength

trajectoryoflength

___

__

Observed parameters

• Number of collisions with obstacle(s)

• Number of collisions with borders

The model

'

)cos(' Txx

)sin(' Tyy

 

Controller(s)

• INPUT : – angle of vehicle– x coordinate of vehicle

• OUTPUT: – steering angle

x

Fuzzy Controller (no obstacles)

• 35 fuzzy rules

• IF x=LE AND =RB THEN =PSLE – left RB – right below PS – positive small

• Defuzzyfication – centroid

• Mamdami fuzzy controller

Membership functionsLE – 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

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

Experiments (no obstacles)

Fuzzy controller Neuro controller

Starting place

Target place

Experiments (no obstacles)

Fuzzy controller Neuro controller

Experiments (RL, no obstacles)200. trial

Experiments (RL, no obstacles)

400. trial

Experiments (RL, no obstacles)

600. trial

Experiments (RL, no obstacles)

800. trial

(last)

Results (no obstacles)No. of collisions

Error of parking

Error of trajectory

Fuzzy

Controller

87 0 1.2275

Neuro Controller

85 0 1.2133

RL NN controller

283 35.26 1.6324

Ratio of trajectory Error Fuzzy:NN is 1.0117

Experiments (with obst.)

• Fuzzy: added 2 rules for obstacle detection

• NN: added an NN for control close to obstacle(s)

Fuzzy controller

Neural Controller

NN RL Controller

Paths after 100 and 200 trials

NN RL Controller

Paths after 300 and 400 trials

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

Fuzzy1 1.8636 76 0 1.74

Fuzzy2 0.6721 56 0 1.63

A 4.5368 63 0.0001 1.86

NN2 0.2847 16 0 1.64

NN online 0.1157 6 16.4 1.41

RL 0.1226 186 2.86 1.52

Our Robot

Moving to the real (fuzzy)

Simulator Real trajectory of robot

Moving to the real (neuro)

Simulator Real trajectory of robot

Moving to the real

Desired path…

…and the reality …

Conclusion and further work

• NN ? Fuzzy

• RL

Lego Robot

RCX Brick

IR sensor

IR Port

HxWxL : 90x105x150 mm