Learning fuzzy controllers in mobile robotics with embedded preprocessing

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{RodriguezFdez:2015:ASC,

• 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 alberto.bugarin.diz",

}

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

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