Integrated Path Planning and Tracking Control of an AUV: A Unified Receding Horizon Optimization Approach
Bibliographic record
Abstract
This paper attempts to develop a unified receding horizon optimization (RHO) scheme for the integrated path planning and tracking control of an autonomous underwater vehicle (AUV). Considering that the effective sensing range of onboard sensors is practically short, we formulate the path planning into RHO problems with the spline path template. The planned path is subsequently viewed as the state trajectory of a virtual reference system having the same kinematic and dynamic properties as the AUV's. Appropriately constructed error dynamics makes the AUV tracking control equivalent to the regulation problem of the error dynamic system, which facilitates the derivation of theoretical results via nonlinear MPC techniques. The model predictive control (MPC) tracking controller is designed so that closed-loop stability can be ensured. Due to the inherent RHO nature, both the path planning and tracking control are incorporated into an unified scheme. Simulation studies are conducted using a realistic dynamic model of the Falcon AUV, which was created in our previous experimental work. The simulation results demonstrate the effectiveness of the proposed control algorithm.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".