Homberg, BS, Katzschmann, RK, Dogar, MR orcid.org/0000-0002-6896-5461 et al. (1 more author) (2019) Robust proprioceptive grasping with a soft robot hand. Autonomous Robots, 43 (3). pp. 681-696. ISSN 0929-5593
Abstract
This work presents a soft hand capable of robustly grasping and identifying objects based on internal state measurements along with a combined system which autonomously performs grasps. A highly compliant soft hand allows for intrinsic robustness to grasping uncertainties; the addition of internal sensing allows the configuration of the hand and object to be detected. The finger module includes resistive force sensors on the fingertips for contact detection and resistive bend sensors for measuring the curvature profile of the finger. The curvature sensors can be used to estimate the contact geometry and thus to distinguish between a set of grasped objects. With one data point from each finger, the object grasped by the hand can be identified. A clustering algorithm to find the correspondence for each grasped object is presented for both enveloping grasps and pinch grasps. A closed loop system uses a camera to detect approximate object locations. Compliance in the soft hand handles that uncertainty in addition to geometric uncertainty in the shape of the object.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) The Author(s) 2018, This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
Keywords: | Soft robotics; Soft gripper; Proprioceptive soft robotic hand; Proprioceptive sensing; Online object identification; Learning new objects; Autonomously grasping |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 18 Apr 2018 15:46 |
Last Modified: | 25 Jun 2023 21:18 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/s10514-018-9754-1 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:129719 |
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