Deep-Learning-Assisted Underwater 3D Tactile Tensegrity.
Journal
Research (Washington, D.C.)
ISSN: 2639-5274
Titre abrégé: Research (Wash D C)
Pays: United States
ID NLM: 101747148
Informations de publication
Date de publication:
2023
2023
Historique:
received:
22
10
2022
accepted:
09
01
2023
entrez:
17
3
2023
pubmed:
18
3
2023
medline:
18
3
2023
Statut:
ppublish
Résumé
The growth of underwater robotic applications in ocean exploration and research has created an urgent need for effective tactile sensing. Here, we propose an underwater 3-dimensional tactile tensegrity (U3DTT) based on soft self-powered triboelectric nanogenerators and deep-learning-assisted data analytics. This device can measure and distinguish the magnitude, location, and orientation of perturbations in real time from both flow field and interaction with obstacles and provide collision protection for underwater vehicles operation. It is enabled by the structure that mimics terrestrial animals' musculoskeletal systems composed of both stiff bones and stretchable muscles. Moreover, when successfully integrated with underwater vehicles, the U3DTT shows advantages of multiple degrees of freedom in its shape modes, an ultrahigh sensitivity, and fast response times with a low cost and conformability. The real-time 3-dimensional pose of the U3DTT has been predicted with an average root-mean-square error of 0.76 in a water pool, indicating that this developed U3DTT is a promising technology in vehicles with tactile feedback.
Identifiants
pubmed: 36930813
doi: 10.34133/research.0062
pii: 0062
pmc: PMC10013964
doi:
Types de publication
Journal Article
Langues
eng
Pagination
0062Informations de copyright
Copyright © 2023 Peng Xu et al.
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