Formation Control of Autonomous Underwater Vehicles With Unknown Absolute Position Using Rigid Graph-Based MPC
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
Formation control of unmanned underwater vehicles (UUVs) is of great significance to the Internet of Underwater Things (IoUT). Currently, various methods are employed for UUV formation control. Among them, rigid graph-based approaches, which rely solely on relative information between UUVs, are particularly suitable for underwater environments. However, traditional rigid graph-based formation control methods often face challenges such as control jumps and weak robustness in dynamic underwater environments. To address these challenges, a novel rigid graph-based model predictive controller (RGMPC) is proposed in this paper. The proposed approach integrates model predictive control with traditional rigid graph-based methods, effectively avoiding issues such as control jumps and thrust saturation while enhancing robustness. Moreover, constraints are constructed using backstepping methods to theoretically ensure the closed-loop stability of the algorithm. Finally, the algorithm is validated through extensive simulations based on mathematical models and further tested in the Gazebo simulator, fully demonstrating its feasibility and effectiveness.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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