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Robotics 2017, 6(1), 5; doi:10.3390/robotics6010005

Visual Tracking of Deformation and Classification of Non-Rigid Objects with Robot Hand Probing

1
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, K1N 6N5, Canada
2
Department of Computer Science and Engineering, Université du Québec en Outaouais, Gatineau, QC, J8X 3X7, Canada
*
Author to whom correspondence should be addressed.
Academic Editors: Kah Bin Lim and Chui Chee Kong
Received: 30 November 2016 / Revised: 7 March 2017 / Accepted: 14 March 2017 / Published: 17 March 2017
(This article belongs to the Special Issue Robotics and 3D Vision)
View Full-Text   |   Download PDF [7162 KB, uploaded 17 March 2017]   |  

Abstract

Performing tasks with a robot hand often requires a complete knowledge of the manipulated object, including its properties (shape, rigidity, surface texture) and its location in the environment, in order to ensure safe and efficient manipulation. While well-established procedures exist for the manipulation of rigid objects, as well as several approaches for the manipulation of linear or planar deformable objects such as ropes or fabric, research addressing the characterization of deformable objects occupying a volume remains relatively limited. The paper proposes an approach for tracking the deformation of non-rigid objects under robot hand manipulation using RGB-D data. The purpose is to automatically classify deformable objects as rigid, elastic, plastic, or elasto-plastic, based on the material they are made of, and to support recognition of the category of such objects through a robotic probing process in order to enhance manipulation capabilities. The proposed approach combines advantageously classical color and depth image processing techniques and proposes a novel combination of the fast level set method with a log-polar mapping of the visual data to robustly detect and track the contour of a deformable object in a RGB-D data stream. Dynamic time warping is employed to characterize the object properties independently from the varying length of the tracked contour as the object deforms. The proposed solution achieves a classification rate over all categories of material of up to 98.3%. When integrated in the control loop of a robot hand, it can contribute to ensure stable grasp, and safe manipulation capability that will preserve the physical integrity of the object. View Full-Text
Keywords: deformable objects; robotic hand manipulation; contour tracking; RGB-D imaging; level sets; log-polar transform; classification deformable objects; robotic hand manipulation; contour tracking; RGB-D imaging; level sets; log-polar transform; classification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Hui, F.; Payeur, P.; Cretu, A.-M. Visual Tracking of Deformation and Classification of Non-Rigid Objects with Robot Hand Probing. Robotics 2017, 6, 5.

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