Co-evolution framework of swarm self-assembly robots

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@Article{Li:2015:Neurocomputing,
  author =       "Haiyuan Li and Hongxing Wei and Jiangyang Xiao and 
                 Tianmiao Wang",
  title =        "Co-evolution framework of swarm self-assembly robots",
  journal =      "Neurocomputing",
  volume =       "148",
  pages =        "112--121",
  year =         "2015",
  ISSN =         "0925-2312",
  DOI =          "doi:10.1016/j.neucom.2012.10.047",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0925231214009394",
  abstract =     "In this paper, we present a co-evolution framework of
                 configuration and control for swarm self-assembly
                 robots, Sambots, in changing environments. The
                 framework can generate different patterns composed of a
                 set of Sambot robots to adapt to the uncertainties in
                 complex environments. Sambot robots are able to
                 autonomously aggregate and disaggregate into a
                 multi-robot organism. To obtain the optimal pattern for
                 the organism, the configuration and control of
                 locomoting co-evolve by means of genetic programming.
                 To finish self-adaptive tasks, we imply a unified
                 locomotion control model based on Central Pattern
                 Generators (CPGs). In addition, taking modular assembly
                 modes into consideration, a mixed genotype is used,
                 which encodes the configuration and control.
                 Specialised genetic operators are designed to maintain
                 the evolution in the simulation environment. By using
                 an orderly method of evaluation, we can select some
                 resulting patterns of better performance. Simulation
                 experiments demonstrate that the proposed system is
                 effective and robust in simultaneously constructing the
                 adaptive structure and locomotion pattern. The
                 algorithmic research and application analysis bring
                 about deeper insight into swarm intelligence and
                 evolutionary robotics.",
  keywords =     "genetic algorithms, genetic programming, Co-evolution,
                 Swarm robot",
}

Genetic Programming entries for Haiyuan Li Hongxing Wei Jiangyang Xiao Tianmiao Wang

Citations