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Design of A Variable Stiffness Actuator and Study on Its Variable Stiffness Characteristics

2022· article· en· W4308659787 on OpenAlexaff
Lan Zhang, Guanyu Huang, Shiqiang Zhu, Lingyu Kong, Anhuan Xie, Lingkai Chen, Dan Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsYork University
Fundersnot available
KeywordsStiffnessActuatorCharacteristic equationRADIUSFunction (biology)Control theory (sociology)Tangent stiffness matrixMathematicsDifferential equationMathematical analysisStructural engineeringEngineeringComputer scienceStiffness matrixControl (management)

Abstract

fetched live from OpenAlex

In this paper, the structural design of a variable stiffness actuator (VSA) is proposed, and the variable stiffness characteristics of the VSA are studied. The VSA is compact and can be used for humanoid robots. Firstly, the structure of the VSA is designed. Then the stiffness model of the structure is derived in theory and the dynamic simulation is performed in ADAMS. The stiffness results of dynamic simulation and theoretical calculation are highly consistent, which verified the correctness of each other. Finally, the influence of the curve equation of the worm gear disc on the stiffness of the system is studied: the radius of the curve is a function of the angle, and when the order of the curve equation is higher, the stiffness adjustment range of the system is wider. Under the premise of meeting the same stiffness requirements, when the order of the curve equation increases, the radius of the higher-order curve equation is only half of that of the lower-order curve equation, and a more compact VSA can be obtained. The results are of great significance to the prototype design, that is, without changing the main design dimensions of each component, the stiffness of the system can reach the design value simply by changing the order of the curve equation.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.554

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 head0.022
GPT teacher head0.230
Teacher spread0.208 · 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 designSimulation or modeling
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

Citations4
Published2022
Admission routes1
Has abstractyes

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