Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/95531
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Mechanical Engineering | en_US |
dc.creator | Qi, J | en_US |
dc.creator | Ma, G | en_US |
dc.creator | Zhou, P | en_US |
dc.creator | Zhang, H | en_US |
dc.creator | Lyu, Y | en_US |
dc.creator | Navarro-Alarcon, D | en_US |
dc.date.accessioned | 2022-09-21T01:40:47Z | - |
dc.date.available | 2022-09-21T01:40:47Z | - |
dc.identifier.issn | 0169-1864 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/95531 | - |
dc.language.iso | en | en_US |
dc.publisher | Taylor & Francis | en_US |
dc.rights | © 2021 Informa UK Limited, trading as Taylor & Francis Group and The Robotics Society of Japan | en_US |
dc.rights | This is an Accepted Manuscript of an article published by Taylor & Francis in Advanced Robotics on 23 Nov 2021 (Published online), available at http://www.tandfonline.com/10.1080/01691864.2021.2004222. | en_US |
dc.subject | Autoencoder | en_US |
dc.subject | Deformable objects | en_US |
dc.subject | Robotics | en_US |
dc.subject | Self-organizing network | en_US |
dc.subject | Visual servoing | en_US |
dc.title | Towards latent space based manipulation of elastic rods using autoencoder models and robust centerline extractions | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 101 | en_US |
dc.identifier.epage | 115 | en_US |
dc.identifier.volume | 36 | en_US |
dc.identifier.issue | 3 | en_US |
dc.identifier.doi | 10.1080/01691864.2021.2004222 | en_US |
dcterms.abstract | The automatic shape control of deformable objects is a challenging (and currently hot) manipulation problem due to their high-dimensional geometric features and complex physical properties. In this study, a new methodology to manipulate elastic rods automatically into 2D desired shapes is presented. An efficient vision-based controller that uses a deep autoencoder network is designed to compute a compact representation of the object's infinite-dimensional shape. An online algorithm that approximates the sensorimotor mapping between the robots configuration and the object's shape features is used to deal with the latters (typically unknown) mechanical properties. The proposed approach computes the rods centerline from raw visual data in real-time by introducing an adaptive algorithm on the basis of a self-organizing network. Its effectiveness is thoroughly validated with simulations and experiments. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Advanced robotics, 2022, v. 36, no. 3, p. 101-115 | en_US |
dcterms.isPartOf | Advanced robotics | en_US |
dcterms.issued | 2022 | - |
dc.identifier.scopus | 2-s2.0-85119866983 | - |
dc.identifier.eissn | 1568-5535 | en_US |
dc.description.validate | 202209 bcfc | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | ME-0152 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | German Academic Exchange Service; Key-Area Research and Development Program of Guangdong Province 2020 | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 58671587 | - |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Zhou_Towards_Latent_Space.pdf | Pre-Published version | 6.82 MB | Adobe PDF | View/Open |
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