MétaCan
Menu
Back to cohort
Record W2773183030 · doi:10.1109/iros.2017.8206283

Path-following control for Unmanned Surface Vehicles

2017· article· en· W2773183030 on OpenAlex
Zhi Li, Ralf Bachmayer, Andrew Vardy

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
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsRobustness (evolution)Line-of-sightComputer scienceAdverse weatherUnmanned surface vehicleMotion planningPath (computing)SightField (mathematics)Control (management)Real-time computingSimulationArtificial intelligenceEngineeringAerospace engineeringMarine engineeringComputer networkRobotMeteorologyMathematics

Abstract

fetched live from OpenAlex

This paper introduces two well-accepted path-following control methods, namely Vector Field Method (VF) and Line-Of-Sight Method (LOS) for Unmanned Surface Vehicles (USVs). We provide a comprehensive study of each algorithm, which includes investigating their mathematical origins, performing simulation evaluations and carrying out real-world field tests in adverse weather conditions. We compare different characteristics of the two methods, and the successful field trial results demonstrate their accuracy and robustness considering unexpected environmental influences. The presented work can assist USV practitioners to decide on a proper strategy for completion of a specific USV mission.

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: Empirical
Teacher disagreement score0.811
Threshold uncertainty score0.524

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 head0.249
Teacher spread0.231 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

Explore more

Same topicAdaptive Control of Nonlinear SystemsFrench-language works237,207