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http://hdl.handle.net/10397/95531
Title: | Towards latent space based manipulation of elastic rods using autoencoder models and robust centerline extractions | Authors: | Qi, J Ma, G Zhou, P Zhang, H Lyu, Y Navarro-Alarcon, D |
Issue Date: | 2022 | Source: | Advanced robotics, 2022, v. 36, no. 3, p. 101-115 | 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. | Keywords: | Autoencoder Deformable objects Robotics Self-organizing network Visual servoing |
Publisher: | Taylor & Francis | Journal: | Advanced robotics | ISSN: | 0169-1864 | EISSN: | 1568-5535 | DOI: | 10.1080/01691864.2021.2004222 | Rights: | © 2021 Informa UK Limited, trading as Taylor & Francis Group and The Robotics Society of Japan 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. |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
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Zhou_Towards_Latent_Space.pdf | Pre-Published version | 6.82 MB | Adobe PDF | View/Open |
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