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Record W2005342375 · doi:10.1109/iros.2008.4650682

A gauge-invariant formulation for constrained robotic systems using square-root factorization and unitary transformation

2008· article· en· W2005342375 on OpenAlexaff
Farhad Aghili

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsCanadian Space Agency
Fundersnot available
KeywordsSquare rootUnitary stateInvariant (physics)FactorizationMathematicsEquations of motionUnitary transformationSquare (algebra)Control theory (sociology)Transformation matrixKinematicsMathematical analysisApplied mathematicsClassical mechanicsComputer sciencePhysicsAlgorithmGeometryControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

A gauge-invariant formulation for deriving the equations of motion of constrained or unconstrained multi-body systems (MBS) in terms of (reduced) quasi-velocities is presented. We show that the square-root factorization of mass matrix and hence the quasi-velocities are not unique, rather they are related by unitary transformations. Subsequently, we show that a particular transformation leads to significant simplification of the dynamic modeling. In this formulation the equations of motion are decoupled from those of constrained force and each system has its own independent input (that is not attainable by other formulations). This allows the possibility to develop a simpler force control action that is totally independent from the motion control action. Moreover, the square-root of the mass matrix is used to transform disparate units into homogeneous units for all the reduced quasi-velocities making the formulation suitable for hybrid force/motion control of robots.

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)0.000
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 score0.853
Threshold uncertainty score0.453

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 head0.050
GPT teacher head0.240
Teacher spread0.189 · 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
Published2008
Admission routes1
Has abstractyes

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