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

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 manip...

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Main Authors: Fei Hui, Pierre Payeur, Ana-Maria Cretu
Format: Article
Language:English
Published: MDPI AG 2017-03-01
Series:Robotics
Subjects:
Online Access:http://www.mdpi.com/2218-6581/6/1/5
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spelling doaj-5face16364234498817e6a5f1c07edb92020-11-25T00:30:00ZengMDPI AGRobotics2218-65812017-03-0161510.3390/robotics6010005robotics6010005Visual Tracking of Deformation and Classification of Non-Rigid Objects with Robot Hand ProbingFei Hui0Pierre Payeur1Ana-Maria Cretu2School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, K1N 6N5, CanadaSchool of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, K1N 6N5, CanadaSchool of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, K1N 6N5, CanadaPerforming 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.http://www.mdpi.com/2218-6581/6/1/5deformable objectsrobotic hand manipulationcontour trackingRGB-D imaginglevel setslog-polar transformclassification
collection DOAJ
language English
format Article
sources DOAJ
author Fei Hui
Pierre Payeur
Ana-Maria Cretu
spellingShingle Fei Hui
Pierre Payeur
Ana-Maria Cretu
Visual Tracking of Deformation and Classification of Non-Rigid Objects with Robot Hand Probing
Robotics
deformable objects
robotic hand manipulation
contour tracking
RGB-D imaging
level sets
log-polar transform
classification
author_facet Fei Hui
Pierre Payeur
Ana-Maria Cretu
author_sort Fei Hui
title Visual Tracking of Deformation and Classification of Non-Rigid Objects with Robot Hand Probing
title_short Visual Tracking of Deformation and Classification of Non-Rigid Objects with Robot Hand Probing
title_full Visual Tracking of Deformation and Classification of Non-Rigid Objects with Robot Hand Probing
title_fullStr Visual Tracking of Deformation and Classification of Non-Rigid Objects with Robot Hand Probing
title_full_unstemmed Visual Tracking of Deformation and Classification of Non-Rigid Objects with Robot Hand Probing
title_sort visual tracking of deformation and classification of non-rigid objects with robot hand probing
publisher MDPI AG
series Robotics
issn 2218-6581
publishDate 2017-03-01
description 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.
topic deformable objects
robotic hand manipulation
contour tracking
RGB-D imaging
level sets
log-polar transform
classification
url http://www.mdpi.com/2218-6581/6/1/5
work_keys_str_mv AT feihui visualtrackingofdeformationandclassificationofnonrigidobjectswithrobothandprobing
AT pierrepayeur visualtrackingofdeformationandclassificationofnonrigidobjectswithrobothandprobing
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