Modeling Elastic Objects with Neural Networks for Vision-Based Force Measurement

Michael A Greminger, Bradley J. Nelson

Research output: Contribution to conferencePaperpeer-review

23 Scopus citations

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.

Original languageEnglish (US)
Pages1278-1283
Number of pages6
StatePublished - Dec 26 2003
Event2003 IEEE/RSJ International Conference on Intelligent Robots and Systems - Las Vegas, NV, United States
Duration: Oct 27 2003Oct 31 2003

Other

Other2003 IEEE/RSJ International Conference on Intelligent Robots and Systems
CountryUnited States
CityLas Vegas, NV
Period10/27/0310/31/03

Fingerprint Dive into the research topics of 'Modeling Elastic Objects with Neural Networks for Vision-Based Force Measurement'. Together they form a unique fingerprint.

Cite this