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

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1 17 33 49 65 81 97 113

129

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385

401

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433

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497

513

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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)

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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)

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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)

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280 285 290 295 300 305

0.485

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0.505

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130 135 140 145 150 155 160 165

0.235

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0.11

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