MétaCan
Menu
Back to cohort
Record W2131014276 · doi:10.1109/iros.2000.895200

Hardware-in-loop simulation of robots interacting with environment via algebraic differential equation

2002· article· en· W2131014276 on OpenAlexaff
Farhad Aghili

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsCanadian Space Agency
Fundersnot available
KeywordsRobotComputer scienceHardware-in-the-loop simulationAccelerationIntegratorControl theory (sociology)Coordinate systemRobot kinematicsProjection (relational algebra)SimulationMobile robotAlgorithmArtificial intelligencePhysicsBandwidth (computing)

Abstract

fetched live from OpenAlex

Hardware-in-the-loop simulation is an attractive tool to validate the functionality of space robots. It is the purpose of the hardware-in-the-loop simulation to make a rigid robot prototype behave, in contact/noncontact phases, as closely as possible to a given robot reference model that would be encountered in the real-world. We introduce a method for the hardware-in-the-loop simulation that is based on the projection of the generalized coordinates of the robot model into two subspaces representing independent and dependent coordinates. The independent coordinate is obtained via simulation by making use of its acceleration model expressed in a closed form. While, the dependent coordinate is obtained from the generalized coordinated of the linearized robot prototype acting as a double integrator on the input dependent-coordinate acceleration. It is followed by investigating the effect of external disturbance on the performance of the hardware-in-the-loop simulation, by the method of Lagrangian multiplier. Finally experimental results obtained from implementation of a single axis arm is presented.

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 score0.846

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)0.0010.000

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 head0.017
GPT teacher head0.193
Teacher spread0.176 · 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

Citations15
Published2002
Admission routes1
Has abstractyes

Explore more

Same topicRobotic Mechanisms and DynamicsFrench-language works237,207