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Multimodal and Sensorial Interfaces for Mobile Robots course task. Nicola Piotto a.y. 2007/2008. Specifics about the task. Robertino has been positioned at different distances from an obstacle (i.e. 0.125, 0.25, 0.5, 1, 2, 3 [meters]). - PowerPoint PPT Presentation
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Multimodal and Sensorial Interfaces for Mobile Robots
course task
Nicola Piotto
a.y. 2007/2008
Specifics about the task
• Robertino has been positioned at different distances from an obstacle (i.e. 0.125, 0.25, 0.5, 1, 2, 3 [meters]).
• For each step several measurements from the frontal IR sensor has been collected.
• The final goal is to define a function to map the noisy sensor data to the real object distance.
Initial data observations
• More the object distance increases the noise increases as well.
84,11891 156,7241 300,5202 775,1184 1816,334 2091,376
0,27663 0,345478 0,729991 11,81193 131,7697 312,6178
0,125 0,25 0,5 1 2 3
0
500
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1 17 33 49 65 81 97 113
129
145
161
177
193
209
225
241
257
273
289
305
321
337
353
369
385
401
417
433
449
465
481
497
513
529
545
561
577
S erie1
S erie2
S erie3
S erie4
S erie5
S erie6
distance
mean
variance
Employed solution
• The solution to the problem can be reached using a linear regression over the acquired sensor data.
• In this way it is possible to analitycally define a linear function throughout least squared error minimization (data fitting).
• The derived function maps sensor data to object distances.
Considerations
• It has been tried to retrieve an higher degree interpolating function (e.g. polinomial, quadratic) using a Support Vector Regression (SVR) procedure: however, due to the noise in the observed data it has not been possible to successfully end the task (the final result was unreliable).
Considerations(2)
0 500 1000 1500 2000 2500 3000 3500 4000-1
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1
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6
Some specification
• The linear regression has been implemented in Matlab environment.
• [b,c]=regress(x,y)• Y=c+b*X• Y is the estimated object distance.• X is the sensor measurement• x is the training data• y is the related distance
Results
• The matrix z includes in 2 columns all the training sensor data(1) and the distance they refer to(2) (bracketed results refre to different set of data considered).
• b=0.013;(0.014);(0.016);
• MSE=0.0849;(0.033);(8.9269*10^-5)
-500 0 500 1000 1500 2000 2500 3000 3500 4000-1
0
1
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Results(2)0.125,0.25,0.5
0.125,0.25,0.5,1
0.125,0.25,0.5,1,2,3
Considerations
• Including also the noisy data from the bigger object distance leads to a calibration function not particularly precise (high MSE) .
• Instead, considering only the less noisy data from the smallest object distance (up to 0.5-1 m) leads to a more reliable calibration function.
Considerations(2)
• Given the impossibility in processing the information from the more noisy distances, it may be suggested to employ the IR sensor to estimate object distances up to 1 meter.
• For bigger distances is not achieved an sufficient precision so it may be better employ different kind of sensor.
Considerations(3)
0 50 100 150 200 250 3000
0.1
0.2
0.3
0.4
0.5
0.6
280 285 290 295 300 305
0.485
0.49
0.495
0.5
0.505
0.51
0.515
0.52
130 135 140 145 150 155 160 165
0.235
0.24
0.245
0.25
0.255
0.26
30 40 50 60 70 80 90 100
0.1
0.11
0.12
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