Learning fuzzy controllers in mobile robotics with embedded preprocessing

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "I. Rodriguez-Fdez and M. Mucientes and A. Bugarin",
  title =        "Learning fuzzy controllers in mobile robotics with
                 embedded preprocessing",
  journal =      "Applied Soft Computing",
  volume =       "26",
  pages =        "123--142",
  year =         "2015",
  keywords =     "genetic algorithms, genetic programming, Mobile
                 robotics, Quantified Fuzzy Rules, Iterative Rule
                 Learning, Genetic fuzzy system",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2014.09.021",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1568494614004748",
  abstract =     "The automatic design of controllers for mobile robots
                 usually requires two stages. In the first stage, sensor
                 data are preprocessed or transformed into high level
                 and meaningful values of variables which are usually
                 defined from expert knowledge. In the second stage, a
                 machine learning technique is applied to obtain a
                 controller that maps these high level variables to the
                 control commands that are actually sent to the robot.
                 This paper describes an algorithm that is able to embed
                 the preprocessing stage into the learning stage in
                 order to get controllers directly starting from raw
                 data with no expert knowledge involved. Due to the high
                 dimensionality of the sensor data, this approach uses
                 Quantified Fuzzy Rules (QFRs), that are able to
                 transform low-level input variables into high-level
                 input variables, reducing the dimensionality through
                 summarisation. The proposed learning algorithm, called
                 Iterative Quantified Fuzzy Rule Learning (IQFRL), is
                 based on genetic programming. IQFRL is able to learn
                 rules with different structures, and can manage
                 linguistic variables with multiple granularities. The
                 algorithm has been tested with the implementation of
                 the wall-following behaviour both in several realistic
                 simulated environments with different complexity and on
                 a Pioneer 3-AT robot in two real environments. Results
                 have been compared with several well-known learning
                 algorithms combined with different data preprocessing
                 techniques, 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 object tracking with
                 static and moving obstacles avoidance.",
  notes =        "ismael.rodriguez manuel.mucientes

Genetic Programming entries for Ismael Rodriguez Fernandez Manuel Mucientes Molina Alberto J Bugarin Diz