Self-folded soft robotic structures with controllable joints
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ICRA17_1960_FI.pdf
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1.58 MB
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Author(s)
Miyashita, Shuhei
Yim, Sehyuk
Sung, Cynthia Rueyi
Lin, Rhea
Kim, Sangbae
Rus, Daniela L
Date Issued
May 2017
Journal
2017 IEEE International Conference on Robotics and Automation (ICRA)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Sung, Cynthia, Rhea Lin, Shuhei Miyashita, Sehyuk Yim, Sangbae Kim, and Daniela Rus. “Self-Folded Soft Robotic Structures with Controllable Joints.” 2017 IEEE International Conference on Robotics and Automation (ICRA), 29 May - 3 June, 2017, Singapore, Singapore, IEEE, 2017.
Version
Author's final manuscript
Abstract
This paper describes additive self-folding, an origami-inspired rapid fabrication approach for creating actuatable compliant structures. Recent work in 3-D printing and other rapid fabrication processes have mostly focused on rigid objects or objects that can achieve small deformations. In contrast, soft robots often require elastic materials and large amounts of movement. Additive self-folding is a process that involves cutting slices of a 3-D object in a long strip and then pleat folding them into a likeness of the original model. The zigzag pattern for folding enables large bending movements that can be actuated and controlled. Gaps between slices in the folded model can be designed to provide larger deformations or higher shape accuracy. We advance existing planar fabrication and self-folding techniques to automate the fabrication process, enabling highly compliant structures with complex 3-D geometries to be designed and fabricated within a few hours. We describe this process in this paper and provide algorithms for converting 3-D meshes into additive self-folding designs. The designs can be rapidly instrumented for global control using magnetic fields or tendon-driven for local bending. We also describe how the resulting structures can be modeled and their responses to tendon-driven control predicted. We test our design and fabrication methods on three models (a bunny, a tuna fish, and a starfish) and demonstrate the method's potential for actuation by actuating the tuna fish and starfish models using tendons and magnetic control.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
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DOI of Published Version
https://doi.org/10.1109/ICRA.2017.7989072
