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

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dc.contributor.authorHou Y.C.en_US
dc.contributor.authorSahari K.S.M.en_US
dc.contributor.authorHow D.N.T.en_US
dc.contributor.authorWeng L.Y.en_US
dc.contributor.authorid37067465000en_US
dc.contributor.authorid57218170038en_US
dc.contributor.authorid57212923888en_US
dc.contributor.authorid26326032700en_US
dc.date.accessioned2023-05-29T07:23:39Z
dc.date.available2023-05-29T07:23:39Z
dc.date.issued2019
dc.descriptionAgricultural 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; Roboticsen_US
dc.description.abstractThis 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9058831
dc.identifier.doi10.1109/CRC.2019.00021
dc.identifier.epage64
dc.identifier.scopus2-s2.0-85084042877
dc.identifier.spage57
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85084042877&doi=10.1109%2fCRC.2019.00021&partnerID=40&md5=6c289a98e51910d4a5ad1a8a3f1e2c81
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24456
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleProceedings - 2019 4th International Conference on Control, Robotics and Cybernetics, CRC 2019
dc.titleCategory classification of deformable object using hybrid dynamic model for robotic graspingen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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