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Category classification of deformable object using hybrid dynamic model for robotic grasping

Agricultural robots; Clothes; Convolutional neural networks; Deep neural networks; Deformation; Dynamic models; Object recognition; Simulation platform; Category Classification; Deformable object; Home service robot; Hybrid dynamic modeling; Off-line simulations; Robotic manipulation; Unfolding proc...

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Main Authors: Hou Y.C., Sahari K.S.M., How D.N.T., Weng L.Y.
Other Authors: 37067465000
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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spelling my.uniten.dspace-244562023-05-29T15:23:40Z Category classification of deformable object using hybrid dynamic model for robotic grasping Hou Y.C. Sahari K.S.M. How D.N.T. Weng L.Y. 37067465000 57218170038 57212923888 26326032700 Agricultural robots; Clothes; Convolutional neural networks; Deep neural networks; Deformation; Dynamic models; Object recognition; Simulation platform; Category Classification; Deformable object; Home service robot; Hybrid dynamic modeling; Off-line simulations; Robotic manipulation; Unfolding procedures; Visual identification; Robotics This work studies the problem of classification of a hung garment in the unfolding procedure by a home service robot. The sheer number of unpredictable configurations that the deformable object can end up in makes the visual identification of the object shape and size difficult. In this paper, we propose a hybrid dynamic model to recognize the pose of hung garment using a single manipulator. A dataset of hung garment is generated by capturing the depth images of real garments at the robotic platform (real images) and also the images of garment mesh model from offline simulation (synthetic images) respectively. Deep convolutional neural network is implemented to classify the category and estimate the pose of garment. Experiment results show that the proposed method performs well and is applicable to different garments in robotic manipulation. � 2019 IEEE. Final 2023-05-29T07:23:39Z 2023-05-29T07:23:39Z 2019 Conference Paper 10.1109/CRC.2019.00021 2-s2.0-85084042877 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084042877&doi=10.1109%2fCRC.2019.00021&partnerID=40&md5=6c289a98e51910d4a5ad1a8a3f1e2c81 https://irepository.uniten.edu.my/handle/123456789/24456 9058831 57 64 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Agricultural robots; Clothes; Convolutional neural networks; Deep neural networks; Deformation; Dynamic models; Object recognition; Simulation platform; Category Classification; Deformable object; Home service robot; Hybrid dynamic modeling; Off-line simulations; Robotic manipulation; Unfolding procedures; Visual identification; Robotics
author2 37067465000
author_facet 37067465000
Hou Y.C.
Sahari K.S.M.
How D.N.T.
Weng L.Y.
format Conference Paper
author Hou Y.C.
Sahari K.S.M.
How D.N.T.
Weng L.Y.
spellingShingle Hou Y.C.
Sahari K.S.M.
How D.N.T.
Weng L.Y.
Category classification of deformable object using hybrid dynamic model for robotic grasping
author_sort Hou Y.C.
title Category classification of deformable object using hybrid dynamic model for robotic grasping
title_short Category classification of deformable object using hybrid dynamic model for robotic grasping
title_full Category classification of deformable object using hybrid dynamic model for robotic grasping
title_fullStr Category classification of deformable object using hybrid dynamic model for robotic grasping
title_full_unstemmed Category classification of deformable object using hybrid dynamic model for robotic grasping
title_sort category classification of deformable object using hybrid dynamic model for robotic grasping
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806425978040221696
score 13.222552

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