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Record W1669963647 · doi:10.1109/icsmc.1992.271728

Effects of non-tip external forces and impulses on robot dynamics

2003· article· en· W1669963647 on OpenAlexaff
O.M. Ismaeil, R.E. Ellis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsQueen's University
Fundersnot available
KeywordsDynamics (music)RobotVirtual workComputer scienceMechanism (biology)TorqueControl theory (sociology)Contact forceWork (physics)Manipulator (device)SimulationPlanarArtificial intelligenceEngineeringClassical mechanicsPhysicsControl (management)Mechanical engineeringComputer graphics (images)

Abstract

fetched live from OpenAlex

The dynamics of robot manipulation with arbitrary forces, torques, and impulses applied to any link in the mechanism are studied. Using the principle of virtual work, and formulating the Lagrangian dynamics with spatial vectors, a modification to a linear-time forward dynamics algorithm can express the equations of motion of a manipulator that has either time-varying or impulsive forces applied anywhere on the manipulator. This permits the efficient simulation of accidental contact, and of multiple contacts that arise naturally in dextrous manipulation. The authors have implemented the equations as part of a computer simulation of robot dynamics, to test the effects of various external loadings. The initial results were derived from the study of free, unpowered planar mechanisms of two and three links subject to gravity.>

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.002
GPT teacher head0.183
Teacher spread0.181 · 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 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

Citations4
Published2003
Admission routes1
Has abstractyes

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