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Record W2049279591 · doi:10.1109/cdc.2011.6161413

Path following for a car-like robot using transverse feedback linearization and tangential dynamic extension

2011· article· en· W2049279591 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicControl and Dynamics of Mobile Robots
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsControl theory (sociology)Path (computing)Controller (irrigation)Extension (predicate logic)KinematicsLinearizationFeedback linearizationComputer scienceDimension (graph theory)Manifold (fluid mechanics)RobotPosition (finance)Process (computing)MathematicsControl (management)Nonlinear systemArtificial intelligenceEngineeringPhysics

Abstract

fetched live from OpenAlex

This article proposes a path following controller for the two input kinematic model of a car-like robot. A smooth dynamic feedback control law is designed to make the car's position follow a large class of curves with the desired speed along the curve. The controller guarantees the property of path invariance. The controller is designed by characterizing the path following manifold when one input is fixed. Once the path following manifold is found we apply dynamic extension to increase its dimension. We refer to this process as tangential dynamic extension. We then find a physically meaningful differentially flat output for the extended system which allows us to easily solve the path following problem.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.651
Threshold uncertainty score0.561

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 head0.018
GPT teacher head0.213
Teacher spread0.195 · 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

Quick stats

Citations19
Published2011
Admission routes1
Has abstractyes

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