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Record W4362654044 · doi:10.1109/lra.2023.3264869

Modeling and Analysis of Tendon-Driven Continuum Robots for Rod-Based Locking

2023· article· en· W4362654044 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.

Bibliographic record

VenueIEEE Robotics and Automation Letters · 2023
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWorkspaceRobotCurvatureRodStiffnessControl theory (sociology)TwistComputer scienceSimulationCompliant mechanismEngineeringStructural engineeringArtificial intelligenceMathematicsGeometryFinite element methodControl (management)

Abstract

fetched live from OpenAlex

Various design modifications have been proposed for tendon-driven continuum robots to improve their stiffness and workspace. One of them is using locking mechanisms to constrain the lengths of rods or passive backbones along the robot. However, physics-based models used to predict these robots' behaviour commonly assume that the curvature of the locked portion does not change during robot actuation or that the effects of friction and gravity are negligible. In addition, these models do not consider the variations in twist on force application. In this letter, we propose a 3D static model for tendon-driven continuum robots experiencing locking due to length constraints on rods along their backbone. The proposed model is evaluated on prototypes of length 240 mm, with up to three locking mechanisms and has an accuracy of 3.63% w.r.t. length. Using the proposed model, a compliance analysis is performed studying the evolution of the robot compliance with the position of the locking mechanisms. An actuation strategy is proposed that can allow the robot to achieve the same shape with different compliance.

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 score0.605
Threshold uncertainty score0.437

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 head0.018
GPT teacher head0.242
Teacher spread0.223 · 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