ci controllers for lego robots - comparison study m. gavalier, m. hudec, r. jakša and p. sinčák...
TRANSCRIPT
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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.
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Structure of Presentation
• Definiton of Task
• Setup of the Fuzzy and ANN Controller
• Lego Robot
• Comparison of Fuzzy and ANN (+RL)
• Examples of behavior
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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
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Observed parameters
• The error of parking
• The error of trajectory
222 )()()( yyxx fff
trajectoryoptimaloflength
trajectoryoflength
___
__
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Observed parameters
• Number of collisions with obstacle(s)
• Number of collisions with borders
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The model
'
)cos(' Txx
)sin(' Tyy
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Controller(s)
• INPUT : – angle of vehicle– x coordinate of vehicle
• OUTPUT: – steering angle
x
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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
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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
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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
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Experiments (no obstacles)
Fuzzy controller Neuro controller
Starting place
Target place
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Experiments (no obstacles)
Fuzzy controller Neuro controller
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Experiments (RL, no obstacles)200. trial
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Experiments (RL, no obstacles)
400. trial
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Experiments (RL, no obstacles)
600. trial
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Experiments (RL, no obstacles)
800. trial
(last)
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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
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Experiments (with obst.)
• Fuzzy: added 2 rules for obstacle detection
• NN: added an NN for control close to obstacle(s)
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Fuzzy controller
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Neural Controller
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NN RL Controller
Paths after 100 and 200 trials
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NN RL Controller
Paths after 300 and 400 trials
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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
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Our Robot
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Moving to the real (fuzzy)
Simulator Real trajectory of robot
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Moving to the real (neuro)
Simulator Real trajectory of robot
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Moving to the real
Desired path…
…and the reality …
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Conclusion and further work
• NN ? Fuzzy
• RL
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Lego Robot
RCX Brick
IR sensor
IR Port
HxWxL : 90x105x150 mm