Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/138936
Title: A methodology to model and simulate customized realistic anthropomorphic robotic hands
Authors: Tian, Li
Magnenat-Thalmann, Nadia
Thalmann, Daniel
Zheng, Jianmin
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Tian, L., Magnenat-Thalmann, N., Thalmann, D., & Zheng, J. (2018). A methodology to model and simulate customized realistic anthropomorphic robotic hands. Proceedings of Computer Graphics International 2018, 153-162. doi:10.1145/3208159.3208182
Abstract: When building robotic hands, researchers are always face with two main issues of how to make robotic hands look human-like and how to make robotic hands function like real hands. Most existing solutions solve these issues by manually modelling the robotic hand [10-18]. However, the design processes are long, and it is difficult to duplicate the geometry shape of a human hand. To solve these two issues, this paper presents a simple and effective method that combines 3D printing and digitization techniques to create a 3D printable cable-driven robotic hand from scanning a physical hand. The method involves segmenting the 3D scanned hand model, adding joints, and converting it into a 3D printable model. Comparing to other robotic solutions, our solution retains more than 90% geometry information of a human hand1, which is attained from 3D scanning. Our modelling progress takes around 15 minutes that include 10 minutes of 3D scanning and five minutes for changing the scanned model to an articulated model by running our algorithm. Compared to other articulated modelling solutions [19, 20], our solution is compatible with an actuation system which provides our robotic hand with the ability to mimic different gestures. We have also developed a way of representing hand skeletons based on the hand anthropometric. As a proof of concept, we demonstrate our robotic hand's performance in the grasping experiments.
URI: https://hdl.handle.net/10356/138936
DOI: 10.1145/3208159.3208182
Rights: © 2018 Association for Computing Machinery. All rights reserved. This paper was published in CGI 2018: Proceedings of Computer Graphics International 2018 and is made available with permission of Association for Computing Machinery.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:IMI Conference Papers

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