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Record W2132346881 · doi:10.1109/robot.1998.680664

On the design of gravity-compensated six-degree-of-freedom parallel mechanisms

2002· article· en· W2132346881 on OpenAlexafffund
Clément Gosselin, Jiegao Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsControl theory (sociology)ActuatorRevolute jointPosition (finance)Potential energyCenter of mass (relativistic)GravitationComputer scienceParallel manipulatorCenter of gravityDegree (music)Energy (signal processing)Mechanism (biology)Topology (electrical circuits)PhysicsClassical mechanicsEngineeringRobotAcousticsArtificial intelligenceElectrical engineering

Abstract

fetched live from OpenAlex

The design of gravity-compensated six-degree-of-freedom parallel mechanisms-or manipulators-with revolute actuators is studied. Two methods are studied for the static balancing of these mechanisms, namely, using counterweights and using springs. The first method leads to mechanisms with a stationary global center of mass while the second approach leads to mechanisms whose total potential energy (including the elastic potential energy stored in the springs as well as the gravitational potential energy) is constant. In both cases, the resulting mechanisms are fully compensated for gravity, i.e., the actuators do not contribute to supporting the weight of the moving links in any of the configurations of the mechanisms. The position vector of the global center of mass and the total potential energy of the manipulator are first expressed as functions of the position and orientation of the platform. Then, conditions for static balancing are derived from the resulting expressions. Finally, examples are given in order to illustrate the design methodologies.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.044
GPT teacher head0.203
Teacher spread0.159 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations58
Published2002
Admission routes2
Has abstractyes

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