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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 OpenAlex
Shaoping Bai, Jorge Angeles

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
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.

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 score0.991
Threshold uncertainty score0.129

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 head0.012
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