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Record W1941119060 · doi:10.1109/icar.1997.620305

Modeling and control of free flying flexible joint coordinated robots

2002· article· en· W1941119060 on OpenAlexaff
Yan‐Ru Hu, George Vukovich

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSpace Satellite Systems and Control
Canadian institutionsCanadian Space Agency
Fundersnot available
KeywordsKinematicsControl theory (sociology)RobotPosition (finance)Controller (irrigation)Joint (building)Flexibility (engineering)Computer scienceRobot kinematicsSpacecraftKinematic chainInternal forcesControl engineeringEngineeringControl (management)PhysicsMathematicsArtificial intelligenceMobile robotAerospace engineeringClassical mechanicsStructural engineering

Abstract

fetched live from OpenAlex

A system consisting of several flexible joint robots mounted on a spacecraft with closed kinematic chain constraints is studied in this paper. First, the kinematics and dynamics of such coordinated space robotic systems in the presence of joint flexibility and with closed kinematic constraints are established. Using the singular perturbation theory, the entire free-flying coordinated space robotic system is decomposed into two subsystems, one the slow subsystem governing the object position and internal forces, the other the fast subsystem governing the joint elastic forces. A coordinated controller consisting of a position and internal force controller for the slow subsystem and a joint elastic force controller for the fast subsystem is designed to distribute the load between the coordinated flexible joint robots and to control the object position and internal forces as well as the joint elastic forces.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.184
Teacher spread0.165 · 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

Citations7
Published2002
Admission routes1
Has abstractyes

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