The Development Of A Genetic Programming Method For Kinematic Robot Calibration

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Jens-Uwe Dolinsky",
  title =        "The Development Of A Genetic Programming Method For
                 Kinematic Robot Calibration",
  school =       "Liverpool John Moores University",
  year =         "2001",
  address =      "UK",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, coevolution,
                 stochastic inference, robotrak, Symbolic, System
                 identification, Evolutionary Computer software
  URL =          "",
  URL =          "",
  URL =          "",
  size =         "183 pages",
  abstract =     "Kinematic robot calibration is the key requirement for
                 the successful application of offline programming to
                 industrial robotics. To compensate for inaccurate robot
                 tool positioning, offline generated poses need to be
                 corrected using a calibrated kinematic model, leading
                 the robot to the desired poses. Conventional robot
                 calibration techniques are heavily reliant upon
                 numerical optimisation methods for model parameter
                 estimation. However, the non-linearities of the
                 kinematic equations, inappropriate model
                 parameterisations with possible parameter
                 discontinuities or redundancies, typically result in
                 badly conditioned parameter identification. Research in
                 kinematic robot calibration has therefore mainly
                 focused on finding robot models and appropriate
                 accommodated numerical methods to increase the accuracy
                 of these models. This thesis presents an alternative
                 approach to conventional kinematic robot calibration
                 and develops a new inverse static kinematic calibration
                 method based on the recent genetic programming
                 paradigm. In this method the process of robot
                 calibration is fully automated by applying symbolic
                 model regression to model synthesis (structure and
                 parameters) without involving iterative numerical
                 methods for parameter identification, thus avoiding
                 their drawbacks such as local convergence, numerical
                 instability and parameter discontinuities. The approach
                 developed in this work is focused on the evolutionary
                 design and implementation of computer programs that
                 model all error effects in particular non-geometric
                 effects such as gear transmission errors, which
                 considerably affect the overall positional accuracy of
                 a robot. Genetic programming is employed to account for
                 these effects and to induce joint correction models
                 used to compensate for positional errors. The potential
                 of this portable method is demonstrated in calibration
                 experiments carried out on an industrial robot.",
  notes =        "

Genetic Programming entries for Jens-Uwe Dolinsky