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Record W4205250459 · doi:10.1109/lra.2021.3137535

Dynamic Modeling of Tendon-Driven Co-Manipulative Continuum Robots

2021· article· en· W4205250459 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Robotics and Automation Letters · 2021
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFlexibility (engineering)RobotComputer scienceObject (grammar)Equations of motionDynamic equationMotion (physics)BendingSimulationClassical mechanicsControl theory (sociology)PhysicsStructural engineeringEngineeringArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

This study presents a general framework for the dynamic analysis of tendon-driven co-manipulative continuum robots with flexible objects. Hence, the actuations of the arms have been provided by <i>bending</i>. Furthermore, the object is modeled as a flexible rod to account for the flexibility of the object into the coupled dynamics of the robot. The equations of motion are summarized based on Cosserat-rod modeling of the arms and the object to account for large deflections. A numerical solution method is developed to solve the equations of motion and to obtain the dynamic response. The numerical results are verified through the experiments. The results show that the presented algorithm has a great potential for the dynamic modeling of cooperative continuum robots. Although integration of the object flexibility increases the complexity of the model, the coupled flexibility of the arms and object has unavoidable effects on the dynamic response of the robot and should be considered for analysis and identification purposes.

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 score0.724
Threshold uncertainty score0.547

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 head0.017
GPT teacher head0.243
Teacher spread0.226 · 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