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CI Controllers for Lego Robots - Comparison Study
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Transcript of CI Controllers for Lego Robots - Comparison Study
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