Fast NMPC Design for Image-Based Visual Servoing of Autonomous Underwater Vehicles
Bibliographic record
Abstract
This paper presents a new approach to image-based visual servoing (IBVS) for Autonomous Underwater Vehicles (AUVs) with the goal of improved performance and computational efficiency. Traditional IBVS methods, when combined with Model Predictive Control (MPC), face high computational demands due to the nonlinear dynamics and the large degrees of freedom (DOFs) in the variables of the associated optimization problem. Our method addresses this by reducing the DOFs of the optimization variable in the cost function while maintaining a good control performance. To further consider the smoothness of the MPC control signal, a soft constraint handling method is developed. The fast nonlinear MPC, combined with smoother control trajectories and effective constraint handling, makes our method particularly suitable for AUV IBVS applications in dynamic environments. Comparisons with standard strategies confirm the improved performance of our approach in terms of both speed and trajectory quality. Simulation results show that our approach can achieve an improved computation up to 100 times faster than conventional MPC-based IBVS methods, which highlights the great potential for real-time IBVS applications.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".