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Record W2084893120 · doi:10.1177/0954406214538781

Elastodynamics of a two-limb Schönflies motion generator

2014· article· en· W2084893120 on OpenAlexafffundabout
Afshin Taghvaeipour, Jorge Angeles, Larry Lessard

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science · 2014
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsGenerator (circuit theory)RobotMotion (physics)StiffnessModal analysisTask (project management)Computer scienceModalSpring (device)ComputationPoint (geometry)Matrix (chemical analysis)Control theory (sociology)EngineeringMathematicsAcousticsArtificial intelligenceVibrationStructural engineeringAlgorithmPhysicsGeometryControl (management)

Abstract

fetched live from OpenAlex

The elastodynamic analysis of a two-limb Schönflies motion generators is the subject of this paper. This analysis calls for the calculation of the stiffness and mass matrices. By resorting to the generalized spring concept, the posture-dependent stiffness matrix of the robot is computed. With the motors locked, the motion caused by the flexible components leads to the robot mass matrix. The generalized springs help to simplify the model. Although this simplification filters out the higher natural frequencies, it eases the computation of the posture-dependent stiffness and mass matrices. This provides a valuable tool to simplify the evaluation of the robot performance from an elastodynamic point of view, while the robot executes a given task. Finally, the modal analysis of the McGill Schönflies motion generator, while executing a pick-and-place operation, is conducted; under these conditions, the evolution of the first six natural frequencies is obtained. The elastodynamic performance of the robot for the given task is assessed using the results of the analysis.

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 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)0.002
metaresearch head score (Gemma)0.002
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 score0.646
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.204
Teacher spread0.197 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations6
Published2014
Admission routes3
Has abstractyes

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