This paper describes a method of combining two approaches to modelling flexible objects. Modal Analysis using Finite Element Methods (FEMs) generates a set of vibrational modes for a single shape. Point Distribution Models (PDMs) generate a statistical model of shape and shape variation from a set of example shapes. A new approach is described which generates vibrational modes when few example shapes are available and changes smoothly to using more statistical modes of variation when a large data set is presented. Results are given for both synthetic and real examples. Experiments using the models for image search show that the combined version performs better than either the PDM or FEM models alone.
We have developed a trainable method of shape representation which can automatically capture the invariant properties of a class of shapes and provide a compact parametric description of variability. We have applied the method to a family of flexible ribbons (worms) and to heart shapes in echocardiograms. We show that in both cases a natural parameterisation of shape results.
This paper introduces a new challenge problem: designing robotic systems to recover after disassembly from high-energy events and a first implemented solution of a simplified problem. It uses vision-based localization for self-reassembly. The control architecture for the various states of the robot, from fully-assembled to the modes for sequential docking, are explained and inter-module communication details for the robotic system are described. Robots and Systems, IROS 2007, October 2007. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
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Reprinted from Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent
Author(s)Mark Yim, Babak Shirmohammadi, Jimmy Sastra, Michael Park, Michael Dugan, and Camillo J. Taylor This journal article is available at ScholarlyCommons: http://repository.upenn.edu/meam_papers/147Abstract-This paper introduces a new challenge problem: designing robotic systems to recover after disassembly from high-energy events and a first implemented solution of a simplified problem. It uses vision-based localization for selfreassembly. The control architecture for the various states of the robot, from fully-assembled to the modes for sequential docking, are explained and inter-module communication details for the robotic system are described.
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