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Twisting for soft intelligent autonomous robot in unstructured environments
Soft robots that can harvest energy from environmental resources for autonomous locomotion is highly desired; however, few are capable of adaptive navigation without human interventions. Here, we report twisting soft robots with embodied physical intelligence for adaptive, intelligent autonomous loc...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9295757/ https://www.ncbi.nlm.nih.gov/pubmed/35605115 http://dx.doi.org/10.1073/pnas.2200265119 |
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author | Zhao, Yao Chi, Yinding Hong, Yaoye Li, Yanbin Yang, Shu Yin, Jie |
author_facet | Zhao, Yao Chi, Yinding Hong, Yaoye Li, Yanbin Yang, Shu Yin, Jie |
author_sort | Zhao, Yao |
collection | PubMed |
description | Soft robots that can harvest energy from environmental resources for autonomous locomotion is highly desired; however, few are capable of adaptive navigation without human interventions. Here, we report twisting soft robots with embodied physical intelligence for adaptive, intelligent autonomous locomotion in various unstructured environments, without on-board or external controls and human interventions. The soft robots are constructed of twisted thermal-responsive liquid crystal elastomer ribbons with a straight centerline. They can harvest thermal energy from environments to roll on outdoor hard surfaces and challenging granular substrates without slip, including ascending loose sandy slopes, crossing sand ripples, escaping from burying sand, and crossing rocks with additional camouflaging features. The twisting body provides anchoring functionality by burrowing into loose sand. When encountering obstacles, they can either self-turn or self-snap for obstacle negotiation and avoidance. Theoretical models and finite element simulation reveal that such physical intelligence is achieved by spontaneously snapping-through its soft body upon active and adaptive soft body-obstacle interactions. Utilizing this strategy, they can intelligently escape from confined spaces and maze-like obstacle courses without any human intervention. This work presents a de novo design of embodied physical intelligence by harnessing the twisting geometry and snap-through instability for adaptive soft robot-environment interactions. |
format | Online Article Text |
id | pubmed-9295757 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-92957572022-11-23 Twisting for soft intelligent autonomous robot in unstructured environments Zhao, Yao Chi, Yinding Hong, Yaoye Li, Yanbin Yang, Shu Yin, Jie Proc Natl Acad Sci U S A Physical Sciences Soft robots that can harvest energy from environmental resources for autonomous locomotion is highly desired; however, few are capable of adaptive navigation without human interventions. Here, we report twisting soft robots with embodied physical intelligence for adaptive, intelligent autonomous locomotion in various unstructured environments, without on-board or external controls and human interventions. The soft robots are constructed of twisted thermal-responsive liquid crystal elastomer ribbons with a straight centerline. They can harvest thermal energy from environments to roll on outdoor hard surfaces and challenging granular substrates without slip, including ascending loose sandy slopes, crossing sand ripples, escaping from burying sand, and crossing rocks with additional camouflaging features. The twisting body provides anchoring functionality by burrowing into loose sand. When encountering obstacles, they can either self-turn or self-snap for obstacle negotiation and avoidance. Theoretical models and finite element simulation reveal that such physical intelligence is achieved by spontaneously snapping-through its soft body upon active and adaptive soft body-obstacle interactions. Utilizing this strategy, they can intelligently escape from confined spaces and maze-like obstacle courses without any human intervention. This work presents a de novo design of embodied physical intelligence by harnessing the twisting geometry and snap-through instability for adaptive soft robot-environment interactions. National Academy of Sciences 2022-05-23 2022-05-31 /pmc/articles/PMC9295757/ /pubmed/35605115 http://dx.doi.org/10.1073/pnas.2200265119 Text en Copyright © 2022 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Physical Sciences Zhao, Yao Chi, Yinding Hong, Yaoye Li, Yanbin Yang, Shu Yin, Jie Twisting for soft intelligent autonomous robot in unstructured environments |
title | Twisting for soft intelligent autonomous robot in unstructured environments |
title_full | Twisting for soft intelligent autonomous robot in unstructured environments |
title_fullStr | Twisting for soft intelligent autonomous robot in unstructured environments |
title_full_unstemmed | Twisting for soft intelligent autonomous robot in unstructured environments |
title_short | Twisting for soft intelligent autonomous robot in unstructured environments |
title_sort | twisting for soft intelligent autonomous robot in unstructured environments |
topic | Physical Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9295757/ https://www.ncbi.nlm.nih.gov/pubmed/35605115 http://dx.doi.org/10.1073/pnas.2200265119 |
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