Sorour, M, Elgeneidy, K, Srinivasan, A orcid.org/0000-0001-9280-7837 et al. (2 more authors) (2019) Grasping Unknown Objects Based on Gripper Workspace Spheres. In: Proceedings of 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 03-08 Nov 2019, Macau, China. IEEE ISBN 978-1-7281-4004-9
Abstract
In this paper, we present a novel grasp planning algorithm for unknown objects given a registered point cloud of the target from different views. The proposed methodology requires no prior knowledge of the object, nor offline learning. In our approach, the gripper kinematic model is used to generate a point cloud of each finger workspace, which is then filled with spheres. At run-time, first the object is segmented, its major axis is computed, in a plane perpendicular to which, the main grasping action is constrained. The object is then uniformly sampled and scanned for various gripper poses that assure at least one object point is located in the workspace of each finger. In addition, collision checks with the object or the table are performed using computationally inexpensive gripper shape approximation. Our methodology is both time efficient (consumes less than 1.5 seconds in average) and versatile. Successful experiments have been conducted on a simple jaw gripper (Franka Panda gripper) as well as a complex, high Degree of Freedom (DoF) hand (Allegro hand).
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Safety and Technology (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 22 Jun 2020 14:56 |
Last Modified: | 22 Jun 2020 14:56 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/iros40897.2019.8967989 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:162139 |