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Record W1662326029 · doi:10.3233/ifs-130957

A novel fuzzy control algorithm for three-dimensional AUV path planning based on sonar model

2014· article· en· W1662326029 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

VenueJournal of Intelligent & Fuzzy Systems · 2014
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsUniversity of Guelph
FundersShanghai Municipal Education CommissionNational Natural Science Foundation of China
KeywordsSonarComputer scienceMotion planningAlgorithmFuzzy logicPath (computing)Control (management)Artificial intelligence

Abstract

fetched live from OpenAlex

A novel fuzzy control method is presented for AUV (Autonomous underwater vehicles) path planning in both static and dynamic three-dimensional environment. First, on the basis of the forward looking sonar model, the virtual acceleration and velocity of AUV in both horizontal and vertical plane can be gotten through the fuzzy system. Then the velocity synthesis approach is applied to generate the real control variables in the body-fixed frame. In addition, a fuzzy-inference system with an accelerate/break (A/B) module is developed for real-time navigation, which enables AUV to avoid dynamic obstacles automatically. Finally, simulation results indicate the effectiveness and feasibility of the proposed approach.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.971
Threshold uncertainty score0.948

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.030
GPT teacher head0.245
Teacher spread0.214 · 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