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Record W4395462511 · doi:10.1631/jzus.a2300479

Novel soft robotic finger model driven by electrohydrodynamic (EHD) pump

2024· article· en· W4395462511 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Zhejiang University. Science A · 2024
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsRoboticsRobotArtificial intelligenceSoft roboticsMechanism (biology)Agile software developmentEngineeringComputer scienceMechanical designMechanical engineeringSimulationControl engineeringSoftware engineering

Abstract

fetched live from OpenAlex

In summary, this work has pioneered the integration of an EHD robotic finger that incorporates a flexible rubber sheet embedded with soft EHD pumps. The model of this study links the electrical characteristics of the EHD pump (voltage input) with the geometric constraints of the robotic mechanism (deflection angle). The validity of this model has been empirically demonstrated. Our research findings primarily revolve around the manipulation of robotic arms and grasping mechanism. The synthesis of an experimental design and a comprehensive mathematical model showcases the potential of EHD pump-driven soft robotic applications ranging from delicate medical procedures to agile manufacturing processes. Moreover, the achieved maximum bending angle of 37° at 10 kV stands as a testament to the effectiveness of the proposed design and methodology. As an important foundation work in the realm of soft robotics, this research intends to serve as a catalyst for subsequent investigations in EHD pump-robot systems. Noteworthy examples include precision grasping in robotics, complex robotic hand operations, and promoting human-robot collaboration.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.798
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.202
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it