Robust NMPC for Uncalibrated IBVS Control of AUVs
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it