A Genetic Programming Framework for Error Recovery in Robotic Assembly Systems

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

  author =       "Kazuhiro Saitou and Cem M. Baydar",
  title =        "A Genetic Programming Framework for Error Recovery in
                 Robotic Assembly Systems",
  pages =        "346--351",
  booktitle =    "Late Breaking Papers at the 2000 Genetic and
                 Evolutionary Computation Conference",
  year =         "2000",
  editor =       "Darrell Whitley",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  broken =       "http://www-personal.engin.umich.edu/~cbaydar/GECCO2000.pdf",
  URL =          "http://citeseer.ist.psu.edu/cache/papers/cs/26330/http:zSzzSzwww-personal.engin.umich.eduzSz~cbaydarzSzGECCO2000.pdf/baydar00genetic.pdf",
  URL =          "http://citeseer.ist.psu.edu/535952.html",
  abstract =     "the advantages and performance of genetic programming
                 in use of error recovery planning in robotic assembly
                 systems is discussed. Existing systems use polynomial
                 time planning techniques or heuristics to produce error
                 recovery plans. However, these systems require
                 translation of the generated plans to working
                 controller codes. An alternative approach could be the
                 use of Genetic Programming to produce recovery plans in
                 robot language itself. A framework is developed and
                 coupled with a 3D robotic simulation software for the
                 generation of error recovery logic in assembly systems.
                 The developed architecture uses a genetic programming
                 system based on both deterministic and probabilistic
                 crossover and variable mutation schemes. Performance of
                 the system is evaluated with the simulations made on a
                 three dimensionally modeled automated assembly line.
                 The obtained results showed that the deterministic
                 crossover operator improves the evolution of the plans
                 and the system is efficient of generating robust
                 recovery plans for different error states.",
  notes =        "Part of \cite{whitley:2000:GECCOlb}",

Genetic Programming entries for Kazuhiro Saitou Cem M Baydar