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Record W2101141060 · doi:10.3233/ifs-151799

Real-time hybrid design of tracking control and obstacle avoidance for underactuated underwater vehicles

2015· article· en· W2101141060 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 · 2015
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsUniversity of Guelph
FundersShanghai Maritime UniversityNational Natural Science Foundation of China
KeywordsUnderactuationObstacle avoidanceControl theory (sociology)TrajectoryObstacleComputer scienceTracking (education)Position (finance)Control engineeringControl (management)EngineeringMobile robotArtificial intelligenceRobotLawPhysics

Abstract

fetched live from OpenAlex

For underactuated underwater vehicles, a real-time hybrid design of dynamic tracking control law is proposed for trajectory tracking and obstacle avoidance. In recent works, sliding mode control (SMC) law has been presented and experimentally implemented for position tracking of an underactuated autonomous surface vessel. It is extended to the underactuated underwater vehicle case and finds it still work for trajectory tracking problem. The thruster saturation problem is considered for the real case. The major innovation is the solution of how to deal with obstacle avoidance in the predefined trajectory tracking mission. In order to deal with this problem, a hybrid control strategy is proposed for static and dynamic obstacle case respectively. Then, to show the effectiveness of the proposed method, trajectory tracking control under different conditions are conducted including static and dynamic obstacles. The experiment results show that the proposed method can deal with tracking and obstacle avoidance quite well.

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)0.002
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 score0.781
Threshold uncertainty score0.850

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.046
GPT teacher head0.256
Teacher spread0.210 · 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