Off-line error prediction, diagnosis and recovery using virtual assembly systems

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Cem M. Baydar and Kazuhiro Saitou",
  title =        "Off-line error prediction, diagnosis and recovery
                 using virtual assembly systems",
  booktitle =    "Proceedings of the IEEE International Conference on
                 Robotics and Automation, ICRA 2001",
  year =         "2001",
  volume =       "1",
  pages =        "818--823",
  address =      "Seoul, Korea",
  month =        "21-26 " # may,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, 3D model,
                 Bayesian reasoning, Monte Carlo simulation, assembly
                 line, automated assembly systems, error scenarios,
                 peg-in-hole assembly, unexpected failures, virtual
                 assembly systems, Bayes methods, Monte Carlo methods,
                 assembling, fault diagnosis, industrial robots,
                 inference mechanisms, robot programming",
  ISSN =         "1050-4729",
  ISBN =         "0-7803-6576-3",
  DOI =          "doi:10.1109/ROBOT.2001.932651",
  size =         "6 pages",
  abstract =     "Automated assembly systems often stop their operation
                 due to the unexpected failures occurred during their
                 assembly process. Since these large-scale systems are
                 composed of many parameters, it is difficult to
                 anticipate all possible types of errors with their
                 likelihood of occurrence. Several systems were
                 developed in the literature, focusing on online
                 diagnosing and recovering the assembly process in an
                 intelligent manner based on the predicted error
                 scenarios. However, these systems do not cover all of
                 the possible errors and they are deficient in dealing
                 with the unexpected error situations. The proposed
                 approach uses Monte Carlo simulation of the assembly
                 process with the 3D model of the assembly line to
                 predict the possible errors in an offline manner. After
                 that, these predicted errors can be diagnosed and
                 recovered using Bayesian reasoning and genetic
                 programming. A case study composed of a peg-in-hole
                 assembly was performed and the results are discussed.
                 It is expected that with this new approach, errors can
                 be diagnosed and recovered accurately and costly
                 downtime of robotic assembly systems will be reduced.",
  notes =        "GP creates code in RAPID language. Also known as

Genetic Programming entries for Cem M Baydar Kazuhiro Saitou