Bibliographic record
Abstract
Image-based visual servoing (IBVS) applications for autonomous underwater vehicles (AUVs) face significant challenges, including frequent recalibration and lack of constraint handling ability. This letter introduces a novel nonlinear model predictive control (NMPC) approach that integrates the Broyden method for uncalibrated IBVS and incorporates the min-max strategy to tolerate the errors in Jacobian matrix estimation. Our proposed min-max NMPC-IBVS framework estimates the Jacobian matrix online, allowing for continuous adaptation to the underwater environment without the need for prior calibration. This approach significantly enhances computational efficiency and robust control performance, enabling real-time uncalibrated applications. A rigorous proof of recursive feasibility is provided in this letter, ensuring that our NMPC-IBVS method consistently finds feasible optimal solutions that satisfy all constraints over time. Simulation results show that the proposed method is able to respect all design constraints in the AUV IBVS control and achieve robust stability with boosted computational efficiency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".