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Record W2117631250 · doi:10.1109/robot.2005.1570605

The Design of A Gearless Pitch-Roll Wrist

2006· article· en· W2117631250 on OpenAlexaff
Shaoping Bai, Jorge Angeles

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsBevel gearWorkspaceBevelGear trainKinematicsWristBacklashEngineeringVibrationComputer scienceStiffnessMechanical engineeringRobotStructural engineeringAcousticsArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Robotic wrists are commonly used in manipulators for applications that require a large dexterous workspace. Although bevel-gear wrists are widely used in industrial manipulators due to their simple kinematics and low manufacturing cost, their gear trains function under rolling and sliding, the latter bringing about noise and vibration. Sliding is inherent to the straight teeth of the bevel gears of these trains. To alleviate these drawbacks, a wrist with cam-roller pairs under pure rolling is proposed here. This paper reports the design of an innovative robotic pitch-roll wrist using multi-lobe cams. The wrist has two degrees of freedom and consists of two roller-carriers (RCs) driving a multi-lobe cam to produce roll and pitch motions. The pure rolling between cam and rollers helps reduce the friction. Moreover, the cam transmission works with low backlash and high stiffness. In view of the kinematic performance, the wrist exhibits favorable features, including a singularity-free unlimited workspace.

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.012
GPT teacher head0.210
Teacher spread0.198 · 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

Citations7
Published2006
Admission routes1
Has abstractyes

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