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Record W2921785030 · doi:10.1109/robio.2018.8665145

Research on Bending and Torsion Properties of Bionic Square Continuum Robot

2018· article· en· W2921785030 on OpenAlexaff
Jiaorong Liu, Tao Jin, Long Li, Fei Xiang Yang, Yingzhong Tian, Yang Xian, Fengfeng Xi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTorsion (gastropod)CurvatureRobotStiffnessTorqueBiomimeticsBending momentStructural engineeringComputer scienceEngineeringMechanical engineeringPhysicsArtificial intelligenceGeometryMathematics

Abstract

fetched live from OpenAlex

Continuum robot, often taking inspiration from biomimetics, is an exciting novel research field and has great capability. The higher stiffness continuum robot may be inspired by the biology, such as seahorse, pipefish and pipehorse, which has both strong skeleton and great dexterity. In this paper, we present a novel square continuum robot (SCR), according to the simplified model of those square shape fish, in which both the bending curvature and torsional angle are controllable. The idea is to employ square components to mimic the armor and connected those components with ball pair and soft skin. The bending and twisting capabilities of SCR are explored in both numerical model and prototype, while a comparison has been done to analysis the dexterity difference in different situations. Based on the numerical model, relationship between angle-moment in pure bending and angle-torque in pure torsion are shown respectively in simulation curve. This topic is expected to provide a type of new structure for continuum robot, which not only expands the dexterity, but also make the robot stiffer, and provide basic for the further study.

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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.116

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.094
GPT teacher head0.323
Teacher spread0.228 · 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 teacher head, 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

Citations3
Published2018
Admission routes1
Has abstractyes

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