This work presents the experiment we are carrying out with a
B21 mobile robot. The main objective of the experiment is to use
the images taken from the CCD camera mounted on the top of the
robot in order to detect and cross opening doors. We divide the
traversing doors behavior in two steps:
door identification and aproximation, and door
crossing.
For the first step, we consider that doors are
parallel vertical lines separated by a minimum distance. Thereby,
we use a Sobel filter to detect vertical lines in the image. Once
the robot detects a posible matching, she makes the necessary
movements to get closer to the door until a preventive distance,
where the door is still visible and there is no collision danger,
is reached. This aproximation also helps the robot to reject some
false doors. From this position the robot learns the actions it has
to perform in order to cross the door (2nd step).
To learn the actions to do, we use as input
data the sensor readings and the position of the door lines in the
image. We compare the learning results obtained with a Bayesian
Network (BN) with several machine learning paradigms in order to
select the method that best adapts to the problem. In this
experiment, the magnitudes of rotational and traslational velocity
during the door crossing step is fixed. Only the signs of those
variables vary from action to action. The results obtained by the
Bayesian Network in the first experimental phase are the bests
among all paradigms used, so we have decided to use BNs in the
robot. In order to do the inference, we use the HUGIN software. By
this way, we obtain in each moment the action the robot should do
given the values of the sonars it has.
This is a real application that shows the use
of Bayesian Networks as Supervised Classifier paradigm. The BN
learning have been done using three score+search methods, using the
metrics K2, BIC and Entropy. We have also used a fixed BN structure
in order to compare the automatic learning proccess with the use of
an expert knowledge to construct the BN.
