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Record W2114298530 · doi:10.1109/robot.2000.846381

A novel manipulator architecture for the production of SCARA motions

2002· article· en· W2114298530 on OpenAlexaff
Jorge Angeles, Alexei A. Morozov, Oscar M. Navarro

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicModular Robots and Swarm Intelligence
Canadian institutionsMcGill University
Fundersnot available
KeywordsSCARAKinematicsRotation (mathematics)Control theory (sociology)Motion (physics)Computer scienceEngineeringRobotArtificial intelligencePhysicsClassical mechanics

Abstract

fetched live from OpenAlex

SCARA denotes a class of 4-DOF manipulators that exhibit a finite stiffness in four directions of their motion space, while being infinitely stiff in the remaining two directions. These four directions comprise translation along three axes and rotation about one single axis. Therefore, a SCARA is noncompliant in two directions of rotation. SCARA systems were originally developed for assembly tasks. We term here any motion with the foregoing features a SCARA motion. Current manipulators producing this class of motions are invariably designed with a common architecture, namely, three revolutes and one prismatic joint, the latter being indistinctly collocated either at the proximal to the base or at the distal joint. We introduce here a novel architecture producing SCARA motions that is based on what is known as a II-joint, as introduced elsewhere. The kinematics of this manipulator is fully discussed.

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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.042
GPT teacher head0.224
Teacher spread0.182 · 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

Citations54
Published2002
Admission routes1
Has abstractyes

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