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Design, Modeling, Control, and Application of Everting Vine Robots

Authors :
Laura H. Blumenschein
Margaret M. Coad
David A. Haggerty
Allison M. Okamura
Elliot W. Hawkes
Source :
Frontiers in Robotics and AI, Vol 7 (2020)
Publication Year :
2020
Publisher :
Frontiers Media S.A., 2020.

Abstract

In nature, tip-localized growth allows navigation in tightly confined environments and creation of structures. Recently, this form of movement has been artificially realized through pressure-driven eversion of flexible, thin-walled tubes. Here we review recent work on robots that “grow” via pressure-driven eversion, referred to as “everting vine robots,” due to a movement pattern that is similar to that of natural vines. We break this work into four categories. First, we examine the design of everting vine robots, highlighting tradeoffs in material selection, actuation methods, and placement of sensors and tools. These tradeoffs have led to application-specific implementations. Second, we describe the state of and need for modeling everting vine robots. Quasi-static models of growth and retraction and kinematic and force-balance models of steering and environment interaction have been developed that use simplifying assumptions and limit the involved degrees of freedom. Third, we report on everting vine robot control and planning techniques that have been developed to move the robot tip to a target, using a variety of modalities to provide reference inputs to the robot. Fourth, we highlight the benefits and challenges of using this paradigm of movement for various applications. Everting vine robot applications to date include deploying and reconfiguring structures, navigating confined spaces, and applying forces on the environment. We conclude by identifying gaps in the state of the art and discussing opportunities for future research to advance everting vine robots and their usefulness in the field.

Details

Language :
English
ISSN :
22969144
Volume :
7
Database :
Directory of Open Access Journals
Journal :
Frontiers in Robotics and AI
Publication Type :
Academic Journal
Accession number :
edsdoj.834ecda2e4a4a7591484c1146f2ecee
Document Type :
article
Full Text :
https://doi.org/10.3389/frobt.2020.548266