Evolutionary Approaches to Robot Path Planning

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@PhdThesis{Kent:thesis,
  author =       "Simon Kent",
  title =        "Evolutionary Approaches to Robot Path Planning",
  school =       "Department of Information Systems and Computing,
                 Brunel University",
  year =         "1999",
  address =      "Uxbridge, Middlesex, UB8 3PH, United Kingdom.",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://bura.brunel.ac.uk/bitstream/2438/1276/1/thesis.pdf",
  URL =          "http://bura.brunel.ac.uk/handle/2438/1276",
  size =         "218 pages",
  abstract =     "The ultimate goal in robotics is to create machines
                 which are more independent and rely less on humans to
                 guide them in their operation. There are many
                 sub-systems which may be present in such a robot, one
                 of which is path planning the ability to determine a
                 sequence of positions or configurations between an
                 initial and goal position within a particular obstacle
                 cluttered workspace.

                 Many classical path planning techniques have been
                 developed, but these tend to have drawbacks such as
                 their computational requirements; the suitability of
                 the plans they produce for a particular application; or
                 how well they are able to generalise to unseen
                 problems.

                 In recent years, evolutionary based problem solving
                 techniques have seen a rise in popularity, possibly
                 coinciding with the improvement in the computational
                 power afforded researches by successful developments in
                 hardware. These techniques adopt some of the features
                 of natural evolution and mimic them in a computer. The
                 increase in the number of publications in the areas of
                 Genetic Algorithms (GA) and Genetic Programming (GP)
                 demonstrate the success achieved when applying these
                 techniques to ever more problem areas.

                 This dissertation presents research conducted to
                 determine whether there is a place for Evolutionary
                 Approaches, and specifically GA and GP, in the
                 development of future path planning techniques.",
  notes =        "Advisor Dracopoulos, D C",
}

Genetic Programming entries for Simon Kent

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