MétaCan
Menu
Back to cohort
Record W4404844013 · doi:10.1016/j.procs.2024.11.013

Prototype Design and Experimental Test for A Hydraulic-Driven Soft Robotic Arm

2024· article· en· W4404844013 on OpenAlexaff
Lei Yu, Peng Du, Minghao Xie, Zheng Chen, Jason Gu

Bibliographic record

VenueProcedia Computer Science · 2024
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsDalhousie University
FundersNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsComputer scienceTest (biology)Soft roboticsSimulationRobotic armHuman–computer interactionArtificial intelligenceRobot

Abstract

fetched live from OpenAlex

Soft robotics has gained considerable attention in recent years for its structural flexibility and inherent safety in environmental interactions. To address the pitfalls of pneumatic actuation systems, namely sluggish response and inefficiencies, this paper introduces the design and fabrication of a Hydraulic-Driven Soft Robotic Arm (HDSRA). The study establishes a comprehensive platform that integrates actuation, sensing, and control software to provide an experimental prototype for validating control algorithms. A novel fabrication technique utilizing water-soluble PVA for single-step mold creation enhances the structural reliability of soft actuators. Through closed-loop control experiments, the HDSRA demonstrates rapid and precise tracking of 1Hz signals, encompassing sine, square, and ramp waves, thus confirming the platform's reliability. This foundational work lays a robust foundation for future research and the verification of control algorithms in HDSRAs.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.020
GPT teacher head0.256
Teacher spread0.236 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueProcedia Computer ScienceSame topicSoft Robotics and ApplicationsFrench-language works237,207