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Record W3157533866 · doi:10.1109/access.2021.3077186

Cosserat Rod-Based Dynamic Modeling of Tendon-Driven Continuum Robots: A Tutorial

2021· article· en· W3157533866 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 Access · 2021
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRobotComputer scienceMATLABSimulationControl engineeringArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

This article provides a tutorial on the dynamic modeling of continuum robots. Continuum robots (CRs) have gained popularity in recent years due to their flexible backbone structure. Modeling and control of CRs motivate accurate and efficient dynamic models. Such models will enable simulation of dynamic behavior, improved structural design, and the development of dynamics-based control systems for CRs. As a unified underlying approach, the Cosserat rod-based modeling of tendon-driven CRs is used as the basis of the modeling techniques discussed in this paper. In addition to conventional continuum robot assemblies, new and emerging assemblies such as tendon-bent concentric tube and co-operative robots are also considered. The governing equations of motion for conventional CRs are first summarized and then extended for other tendon-driven CRs. This tutorial also contributes to developing a MATLAB code package for simulation of the dynamic response of these robots. The presented method and codes provide a useful and compact resource for readers and can be used for further development of tendon-driven continuum robots.

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: Empirical
Teacher disagreement score0.491
Threshold uncertainty score0.494

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 head0.021
GPT teacher head0.278
Teacher spread0.257 · 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