  The automatic design of controllers for mobile robots usually requires two
stages. In the first stage,sensorial data are preprocessed or transformed into
high level and meaningful values of variables whichare usually defined from
expert knowledge. In the second stage, a machine learning technique is applied
toobtain a controller that maps these high level variables to the control
commands that are actually sent tothe robot. This paper describes an algorithm
that is able to embed the preprocessing stage into the learningstage in order
to get controllers directly starting from sensorial raw data with no expert
knowledgeinvolved. Due to the high dimensionality of the sensorial data, this
approach uses Quantified Fuzzy Rules(QFRs), that are able to transform
low-level input variables into high-level input variables, reducingthe
dimensionality through summarization. The proposed learning algorithm, called
Iterative QuantifiedFuzzy Rule Learning (IQFRL), is based on genetic
programming. IQFRL is able to learn rules with differentstructures, and can
manage linguistic variables with multiple granularities. The algorithm has been
testedwith the implementation of the wall-following behavior both in several
realistic simulated environmentswith different complexity and on a Pioneer 3-AT
robot in two real environments. Results have beencompared with several
well-known learning algorithms combined with different data
preprocessingtechniques, showing that IQFRL exhibits a better and statistically
significant performance. Moreover,three real world applications for which IQFRL
plays a central role are also presented: path and objecttracking with static
and moving obstacles avoidance.

Robotics;Artificial Intelligence;Machine Learning