ArticleResearch (Washington, D.C.)2023
Deep-Learning-Assisted Underwater 3D Tactile Tensegrity.
Article in Research (Washington, D.C.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 41 citations in OpenAlex.
- Design and Sensing Frameworks of Soft Octopus-Inspired Grippers Toward Artificial Intelligence.Biomimetics (Basel, Switzerland) · 2025Review
- Exploration of Advanced Applications of Triboelectric Nanogenerator-Based Self-Powered Sensors in the Era of Artificial Intelligence.Sensors (Basel, Switzerland) · 2025Review
- Real-time Trajectory Planning and Tracking Control of Bionic Underwater Robot in Dynamic Environment.Cyborg and bionic systems (Washington, D.C.) · 2024Article
- Microsecond-Scale Transient Thermal Sensing Enabled by Flexible MoResearch (Washington, D.C.) · 2024Article
- Octopus-Inspired Underwater Soft Robotic Gripper with Crawling and Swimming Capabilities.Research (Washington, D.C.) · 2024Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.