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 , 0.25, 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, , , , , ,376 0,276630, , , , ,6178 0,1250,250,5123 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)

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)

Results(2) 0.125,0.25, ,0.25,0.5, ,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 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)