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
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;
Conference_Titel :
Intelligent Robots and Systems, 2003. (IROS 2003). Proceedings. 2003 IEEE/RSJ International Conference on
Print_ISBN :
0-7803-7860-1
DOI :
10.1109/IROS.2003.1248821