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Record W4231777898 · doi:10.1115/1.4045937

A Cable-Driven Parallel Robot With An Embedded Tilt-Roll Wrist

2020· article· en· W4231777898 on OpenAlexaff
Saman Lessanibahri, Philippe Cardou, Stéphane Caro

Bibliographic record

VenueJournal of Mechanisms and Robotics · 2020
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsWorkspaceParallel manipulatorTilt (camera)Computer scienceMechanism (biology)InertiaRobotWristRotation (mathematics)Translation (biology)SimulationEngineeringComputer visionArtificial intelligenceMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Abstract This paper addresses the optimum design, configuration, and workspace analysis of a cable-driven parallel robot (CDPR) with an embedded tilt-roll wrist. The manipulator consists in a tilt-roll wrist mounted on the moving platform of a suspended CDPR. The embedded wrist provides large amplitudes of tilt and roll rotations and a large translational workspace obtained by the CDPR. This manipulator is suitable for tasks requiring large rotation and translation workspaces such as tomography scanning, camera-orienting devices, and visual surveillance. The moving-platform is an eight-degree-of-freedom articulated mechanism with large translational and rotational workspaces, and it is suspended from a fixed frame by six cables. The manipulator employs two bi-actuated cables, i.e., cable-loops to transmit the power from motors fixed on the ground to the tilt-roll wrist. Therefore, the manipulator achieves better dynamic performances due to a lower inertia of its moving-platform.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.213
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations24
Published2020
Admission routes1
Has abstractyes

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