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Parametric Design of a Tendon-Driven Continuum Robot for Industrial Applications

2025· article· en· W4411019350 on OpenAlexaff
Puspita Triana Dewi, Chloe Pogue, Jessica Burgner-Kahrs

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsParametric statisticsRobotComputer scienceMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

Continuum robot researchers must often create entirely new prototypes for their applications as existing continuum robot designs do not meet their requirements, especially for industrial applications. To address this issue, we leverage a parametric design for modular units, which can be utilized to create a Tendon-Driven Continuum Robot (TDCR) tailored to specific task requirements. Using a Design of Experiment approach, we identify the relationship between the modular units’ stiffness and dimensions, establishing a mathematical framework to guide TDCR parametrization. A structured workflow is provided to guide the development of TDCRs, from the definition of the task objectives to the experimental validation. For demonstration, an aircraft wing inspection task was selected and an 816 mm TDCR was prototyped using the parametric design approach. The TDCR successfully inspected all target areas in a 1:3 scaled Cessna 172 wing model, yielding a 100% coverage rate.

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: Methods · Consensus signal: none
Teacher disagreement score0.858
Threshold uncertainty score0.271

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 head0.042
GPT teacher head0.263
Teacher spread0.221 · 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
GenreMethods

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

Citations2
Published2025
Admission routes1
Has abstractyes

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