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Branching Vine Robots for Unmapped Environments

While exploring complex unmapped spaces is a persistent challenge for robots, plants are able to reliably accomplish this task. In this work we develop branching robots that deploy through an eversion process that mimics key features of plant growth (i.e., apical extension, branching). We show that...

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Autores principales: Glick, Paul E., Adibnazari, Iman, Drotman, Dylan, Ruffatto III, Donald, Tolley, Michael T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8987124/
https://www.ncbi.nlm.nih.gov/pubmed/35402519
http://dx.doi.org/10.3389/frobt.2022.838913
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author Glick, Paul E.
Adibnazari, Iman
Drotman, Dylan
Ruffatto III, Donald
Tolley, Michael T.
author_facet Glick, Paul E.
Adibnazari, Iman
Drotman, Dylan
Ruffatto III, Donald
Tolley, Michael T.
author_sort Glick, Paul E.
collection PubMed
description While exploring complex unmapped spaces is a persistent challenge for robots, plants are able to reliably accomplish this task. In this work we develop branching robots that deploy through an eversion process that mimics key features of plant growth (i.e., apical extension, branching). We show that by optimizing the design of these robots, we can successfully traverse complex terrain even in unseen instances of an environment. By simulating robot growth through a set of known training maps and evaluating performance with a reward heuristic specific to the intended application (i.e., exploration, anchoring), we optimized robot designs with a particle swarm algorithm. We show these optimization efforts transfer from training on known maps to performance on unseen maps in the same type of environment, and that the resulting designs are specialized to the environment used in training. Furthermore, we fabricated several optimized branching everting robot designs and demonstrated key aspects of their performance in hardware. Our branching designs replicated three properties found in nature: anchoring, coverage, and reachability. The branching designs were able to reach 25% more of a given space than non-branching robots, improved anchoring forces by 12.55×, and were able to hold greater than 100× their own mass (i.e., a device weighing 5 g held 575 g). We also demonstrated anchoring with a robot that held a load of over 66.7 N at an internal pressure of 50 kPa. These results show the promise of using branching vine robots for traversing complex and unmapped terrain.
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spelling pubmed-89871242022-04-08 Branching Vine Robots for Unmapped Environments Glick, Paul E. Adibnazari, Iman Drotman, Dylan Ruffatto III, Donald Tolley, Michael T. Front Robot AI Robotics and AI While exploring complex unmapped spaces is a persistent challenge for robots, plants are able to reliably accomplish this task. In this work we develop branching robots that deploy through an eversion process that mimics key features of plant growth (i.e., apical extension, branching). We show that by optimizing the design of these robots, we can successfully traverse complex terrain even in unseen instances of an environment. By simulating robot growth through a set of known training maps and evaluating performance with a reward heuristic specific to the intended application (i.e., exploration, anchoring), we optimized robot designs with a particle swarm algorithm. We show these optimization efforts transfer from training on known maps to performance on unseen maps in the same type of environment, and that the resulting designs are specialized to the environment used in training. Furthermore, we fabricated several optimized branching everting robot designs and demonstrated key aspects of their performance in hardware. Our branching designs replicated three properties found in nature: anchoring, coverage, and reachability. The branching designs were able to reach 25% more of a given space than non-branching robots, improved anchoring forces by 12.55×, and were able to hold greater than 100× their own mass (i.e., a device weighing 5 g held 575 g). We also demonstrated anchoring with a robot that held a load of over 66.7 N at an internal pressure of 50 kPa. These results show the promise of using branching vine robots for traversing complex and unmapped terrain. Frontiers Media S.A. 2022-03-24 /pmc/articles/PMC8987124/ /pubmed/35402519 http://dx.doi.org/10.3389/frobt.2022.838913 Text en Copyright © 2022 Glick, Adibnazari, Drotman, Ruffatto III and Tolley. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Robotics and AI
Glick, Paul E.
Adibnazari, Iman
Drotman, Dylan
Ruffatto III, Donald
Tolley, Michael T.
Branching Vine Robots for Unmapped Environments
title Branching Vine Robots for Unmapped Environments
title_full Branching Vine Robots for Unmapped Environments
title_fullStr Branching Vine Robots for Unmapped Environments
title_full_unstemmed Branching Vine Robots for Unmapped Environments
title_short Branching Vine Robots for Unmapped Environments
title_sort branching vine robots for unmapped environments
topic Robotics and AI
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8987124/
https://www.ncbi.nlm.nih.gov/pubmed/35402519
http://dx.doi.org/10.3389/frobt.2022.838913
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