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A Fast Geometric Framework for Dynamic Cosserat Rods with Discrete Actuated Joints

2023· article· en· W4383108278 on OpenAlexafffund
Hossain Samei, Robin Chhabra

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFinite element methodDiscretizationCartesian coordinate systemFinite differenceFinite difference methodIntegratorComputer scienceDegrees of freedom (physics and chemistry)Control theory (sociology)MathematicsFinite difference coefficientApplied mathematicsAlgorithmMathematical optimizationMathematical analysisMixed finite element methodControl (management)GeometryEngineeringPhysicsArtificial intelligenceStructural engineering

Abstract

fetched live from OpenAlex

Current dynamical models of Cosserat rods often use the finite element method limited by computational efficiency or the finite difference method in a Cartesian framework with a compromise to accuracy. We employ the finite difference method in a geometric framework to develop solutions that are both computationally efficient and accurate. A numerical study is conducted on various backward-differentiation discretization and Runge-Kutta-Munthe-Kaas integration schemes, focusing on their accuracy and computational efficiency. Case studies are conducted on a single-degree-of-freedom joint actuated Cosserat rod to mitigate additional sources of undesired error from the numerical analysis, e.g. multi-body interactions, moving base dynamics, etc. The proposed geometric integrators are demonstrated to improve solution accuracy compared to the published finite difference models. The presented solution is parameterization-free and also computationally efficient with the potential for use in real-time applications, e.g., model-based control of soft manipulators.

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: none
Teacher disagreement score0.871
Threshold uncertainty score0.330

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 communication0.0000.000
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 head0.013
GPT teacher head0.262
Teacher spread0.250 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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