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Record W2910317595 · doi:10.1109/iros.2018.8593806

Designing Concentric Tube Manipulators for Stability Using Topology Optimization

2018· article· en· W2910317595 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsConcentricWorkspaceTube (container)StiffnessTopology (electrical circuits)Stability (learning theory)Computer scienceFinite element methodBendingRobotStructural engineeringMechanical engineeringEngineeringMathematicsGeometryArtificial intelligence

Abstract

fetched live from OpenAlex

One of the major problems facing the development and road to practical usage of concentric tube continuum robots in surgical environments is that of instability. This issue, also known as the snapping problem, is caused by a tube having a high bending to torsional stiffness ratio (BTSR). Past efforts have shown that by cutting patterns on the tubes, this problem can be avoided. This paper seeks to redesign the topology of the tubes so that BTSR is decreased and the snapping problem is resolved in a particular tube set. The generated designs are then tested through finite element analysis as well as experimental testing to demonstrate the elimination of the snapping problem. Using this novel tube design on a concentric tube robotic system can increase its stable workspace because it allows the usage of greater tube curvatures and/or curve lengths.

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: Methods · Consensus signal: none
Teacher disagreement score0.653
Threshold uncertainty score0.226

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 head0.052
GPT teacher head0.275
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

Quick stats

Citations20
Published2018
Admission routes1
Has abstractyes

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