• DocumentCode
    399289
  • Title

    Modeling elastic objects with neural networks for vision-based force measurement

  • Author

    Greminger, Michael A. ; Nelson, Bradley J.

  • Author_Institution
    Dept. of Mechanical Eng., Minnesota Univ., Minneapolis, MN, USA
  • Volume
    2
  • fYear
    2003
  • fDate
    27-31 Oct. 2003
  • Firstpage
    1278
  • Abstract
    This paper presents a method to model the deformation of an elastic object with an artificial neural network. The neural network is trained directly from images of the elastic object deforming under known loads. Using this process, models can be created for objects such as biological tissues that cannot be modeled by existing techniques. The neural network elastic model is used in conjunction with a deformable template matching algorithm to perform vision-based force measurement (VBFM). We demonstrate this learning method on objects with both linear and nonlinear elastic properties.
  • Keywords
    biomechanics; computer vision; elastic deformation; force measurement; neural nets; artificial neural networks; deformable template matching algorithm; elastic object deformation; elastic objects modeling; learning method; nonlinear elastic properties; vision-based force measurement; Artificial neural networks; Biological materials; Biological system modeling; Biomedical measurements; Deformable models; Force feedback; Force measurement; Intelligent robots; Neural networks; Solid modeling;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Robots and Systems, 2003. (IROS 2003). Proceedings. 2003 IEEE/RSJ International Conference on
  • Print_ISBN
    0-7803-7860-1
  • Type

    conf

  • DOI
    10.1109/IROS.2003.1248821
  • Filename
    1248821