Force and Topography Reconstruction Using GP and MOR for the TACTIP Soft Sensor System

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "G {de Boer} and H Wang and M Ghajari and 
                 A Alazmani and R Hewson and P Culmer",
  title =        "Force and Topography Reconstruction Using {GP} and
                 {MOR} for the {TACTIP} Soft Sensor System",
  booktitle =    "Proceedings of the 17th Annual Conference Towards
                 Autonomous Robotic Systems, TAROS 2016",
  year =         "2016",
  editor =       "Lyuba Alboul and Dana Damian and Jonathan M. Aitken",
  volume =       "9716",
  series =       "Lecture Notes in Computer Science",
  pages =        "65--74",
  address =      "Sheffield, UK",
  month =        jun # " 26--" # jul # " 1",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Model Order
  bibsource =    "OAI-PMH server at",
  contributor =  "L Alboul and D Damian and J. M. Aitken",
  oai =          "",
  isbn13 =       "978-3-319-40379-3",
  URL =          "",
  DOI =          "doi:10.1007/978-3-319-40379-3_7",
  size =         "10 pages",
  abstract =     "Sensors take measurements and provide feedback to the
                 user via a calibrated system, in soft sensing the
                 development of such systems is complicated by the
                 presence of nonlinearities, e.g. contact, material
                 properties and complex geometries. When designing
                 soft-sensors it is desirable for them to be inexpensive
                 and capable of providing high resolution output. Often
                 these constraints limit the complexity of the sensing
                 components and their low resolution data capture, this
                 means that the usefulness of the sensor relies heavily
                 upon the system design. This work delivers a force and
                 topography sensing framework for a soft sensor. A
                 system was designed to allow the data corresponding to
                 the deformation of the sensor to be related to outputs
                 of force and topography. This system used Genetic
                 Programming (GP) and Model Order Reduction (MOR)
                 methods to generate the required relationships. Using a
                 range of 3D printed samples it was demonstrated that
                 the system is capable of reconstructing the outputs
                 within an error of one order of magnitude.",

Genetic Programming entries for Greg de Boer Haixin Wang Mazdak Ghajari Ali Alazmani Robert Hewson Pete Culmer