Real Time Control of a Khepera Robot using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Nordin:1997:CC,
  author =       "Peter Nordin and Wolfgang Banzhaf",
  title =        "Real Time Control of a Khepera Robot using Genetic
                 Programming",
  journal =      "Cybernetics and Control",
  year =         "1997",
  volume =       "26",
  number =       "3",
  pages =        "533--561",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.52.6310",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.52.6310.pdf",
  size =         "33 pages",
  abstract =     "A computer language is a very general form of
                 representing and specifying an autonomous agent's
                 behavior. The task of planning feasible actions could
                 then simply be reduced to an instance of automatic
                 programming. We have evaluated the use of an
                 evolutionary technique for automatic programming called
                 Genetic Programming (GP) to directly control a
                 miniature robot. To our knowledge, this is the first
                 attempt to control a real robot with a GP based
                 learning method. Two schemes are presented. The
                 objective of the GP system in our first approach is to
                 evolve real-time obstacle avoiding behaviour. This
                 technique enables real-time learning with a real robot
                 using genetic programming. It has, however, the
                 drawback that the learning time is limited by the
                 response dynamics of the environment. To overcome this
                 problems we have devised a second method, learning from
                 past experiences which are stored in memory. This new
                 system allows a speed-up of the algorithm by a factor
                 of more than 2000. Obstacle avoiding behavior emerges
                 much faster, approximately 40 times as fast, allowing
                 learning of this task in 1.5 minutes. This learning
                 time is several orders of magnitudes faster then
                 comparable experiments with other control
                 architectures. Furthermore, the GP algorithm is very
                 compact and can be ported to the micro-controller of
                 the autonomous mobile miniature robot.",
}

Genetic Programming entries for Peter Nordin Wolfgang Banzhaf

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