Multi-robot path planning using co-evolutionary genetic programming

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

@Article{Kala20123817,
  author =       "Rahul Kala",
  title =        "Multi-robot path planning using co-evolutionary
                 genetic programming",
  journal =      "Expert Systems with Applications",
  volume =       "39",
  number =       "3",
  pages =        "3817--3831",
  year =         "2012",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2011.09.090",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0957417411014138",
  keywords =     "genetic algorithms, genetic programming, Path
                 planning, Motion planning, Mobile robotics, Grammatical
                 evolution, Co-operative evolution, Multi-robot
                 systems",
  abstract =     "Motion planning for multiple mobile robots must ensure
                 the optimality of the path of each and every robot, as
                 well as overall path optimality, which requires
                 cooperation amongst robots. The paper proposes a
                 solution to the problem, considering different source
                 and goal of each robot. Each robot uses a grammar based
                 genetic programming for figuring the optimal path in a
                 maze-like map, while a master evolutionary algorithm
                 caters to the needs of overall path optimality.
                 Co-operation amongst the individual robots'
                 evolutionary algorithms ensures generation of overall
                 optimal paths. The other feature of the algorithm
                 includes local optimisation using memory based lookup
                 where optimal paths between various crosses in map are
                 stored and regularly updated. Feature called wait for
                 robot is used in place of conventionally used priority
                 based techniques. Experiments are carried out with a
                 number of maps, scenarios, and different robotic
                 speeds. Experimental results confirm the usefulness of
                 the algorithm in a variety of scenarios.",
}

Genetic Programming entries for Rahul Kala

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