Unmanned Aerial Vehicle Landing on Maritime Vessels using Signal Prediction of the Ship Motion
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) are becoming more prevalent in maritime operations. For safe operation, one of the key challenges of using UAVs at sea is the relative motion that exists between the UAV and ship. For perpetual maritime operations, UAV systems need to be able to land safely on ocean vessels. Determining a `quiescent period', where the roll and pitch angles of the ship are below a danger threshold, is a challenging problem for UAV systems. In general, current strategies rely on reactive systems and often use sensors on board the maritime vessel. The scope of the current paper is a proof-of-concept methodology which uses a signal prediction algorithm to facilitate safer autonomous UAV-ship landings. This study uses laser ranging and detecting devices (LIDAR) in conjunction with a signal prediction algorithm (SPA) to forecast when the ship motion is within safe landing limits. ShipMo3D was used to generate twelve trial cases for UAV-ship landings on a 33 m ship. The results show that with the use of the SPA, the number of UAV landing attempts was decreased by an average of 2 attempts, per test case, when compared to a system that did not use an SPA. Moreover, the results indicate that with revised tuning of the SPA, the likelihood of a safe landing can be further improved.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".