Automated generation of robust error recovery logic in assembly systems using genetic programming

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@Article{Baydar200155,
  author =       "Cem M. Baydar and Kazuhiro Saitou",
  title =        "Automated generation of robust error recovery logic in
                 assembly systems using genetic programming",
  journal =      "Journal of Manufacturing Systems",
  volume =       "20",
  number =       "1",
  pages =        "55--68",
  year =         "2001",
  ISSN =         "0278-6125",
  DOI =          "doi:10.1016/S0278-6125(01)80020-0",
  URL =          "http://www.sciencedirect.com/science/article/B6VJD-441R1H8-6/2/cdebaddb30a67a67dc7cb6dd41fabf9f",
  keywords =     "genetic algorithms, genetic programming, robotics,
                 Automated Assembly Systems, Error Recovery, Multi-Level
                 Optimization",
  abstract =     "Automated assembly lines are subject to unexpected
                 failures, which can cause costly shutdowns. Generally,
                 the recovery process is done 'on-line' by human experts
                 or automated error recovery logic controllers embedded
                 in the system. However, these controller codes are
                 programmed based on anticipated error scenarios and,
                 due to the geometrical features of the assembly lines,
                 there may be error cases that belong to the same
                 anticipated type but are present in different
                 positions, each requiring a different way to recover.
                 Therefore, robustness must be assured in the sense of
                 having a common recovery algorithm for similar cases
                 during the recovery sequence.

                 The proposed approach is based on three-dimensional
                 geometric modeling of the assembly line coupled with
                 the genetic programming and multi-level optimization
                 techniques to generate robust error recovery logic in
                 an 'off-line' manner. The approach uses genetic
                 programming's flexibility to generate recovery plans in
                 the robot language itself. An assembly line is modeled
                 and from the given error cases an optimum way of error
                 recovery is investigated using multi-level optimization
                 in a 'generate and test' fashion. The obtained results
                 showed that with the improved convergence gained by
                 using multi-level optimisation, the infrastructure is
                 capable of finding robust error recovery algorithms. It
                 is expected that this approach will require less time
                 for the generation of robust error recovery logic.",
  notes =        "IRB6000 KAREL2, ROUTINE GPcode26, Move to POS, Move
                 Relative...",
}

Genetic Programming entries for Cem M Baydar Kazuhiro Saitou

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