Collaborative AUV Localization and Tracking of an Underway Ship with Adaptive Pinging and a Planner for Trilateration
Bibliographic record
Abstract
The efficacy of 3 collaborative autonomous under-water vehicles (AUV) to track a dark ship is studied in a scenario where the ship has entered a marine protected area through a choke point. This is performed in simulations. The objective is to quantity the advantage of mobile transducers over traditional moored long baseline (LBL) transponders. An underwater environment with a constant sound velocity profile captures the one-way travel time inter-AUV communications as well as the two-way travel time for AUVs ranging the ship. The novel contributions include an assessment of the ship localization error given the self-localization error of the three AUVs and their range measurement uncertainty. Another contribution is an adaptive ping strategy, which potentially gives more accurate ship localization by scheduling the pings to arrive simultaneously at the ship. The third contribution is a collaborative AUV mission planner which strives to increase the range that the 3 AUVs can trilaterate the ship over. The results show that the adaptive ping strategy and the collaborative AUV mission planner are effective in reducing ship localization error. With these encouraging results, future work includes a refined environment model that integrates BELLHOP and considers environmental and range dependent communications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".