Direction-Guided Model Predictive Planning for High-Speed Obstacle Avoidance of Extra-Large AUVs
Bibliographic record
Abstract
Abstract This paper presents a novel Direction-Guided Model Predictive Planning (DG-MPP) scheme specifically developed for real-time obstacle avoidance of high-speed extra-large autonomous underwater vehicles (XLAUVs) navigating in the vertical plane. Recognizing the limitations of traditional Euclidean-distance-based methods, the scheme enhances the obstacle avoidance constraint by incorporating vehicle shape dimension and heading information to formulate a constraint with Directional Guidance (DG) capability. Subsequently, to improve the robustness of the optimization process and guarantee safety, hard obstacle avoidance constraints are effectively handled by converting them into continuously differentiable penalty terms using a logarithmic barrier function, thereby preventing abrupt changes in the feasible region. High-fidelity simulation results convincingly demonstrate that the proposed planning scheme exhibits superior foresight, reliable real-time performance, and robust safety assurance, showcasing its effectiveness for challenging high-speed XLAUV navigation tasks.
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.
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 | 0.001 | 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 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".