Bibliographic record
Abstract
This thesis focuses on the development of an autonomous system capable of iden- tifying, tracking, and landing on suitable sites aboard a moving ship. Leveraging modifications to the Hazard-Aware Landing Optimization (HALO) algorithm, origi- nally designed for static terrain, the system integrates robust mapping, point cloud registration, and site selection algorithms to enable reliable performance in dynamic maritime conditions. A simulation environment was developed, utilizing Microsoft AirSim and ShipMo3D. This simulation incorporated a quadrotor equipped with Light Detec- tion and Ranging (LiDAR) to map ship decks and evaluate potential landing sites. Key innovations included dynamic point cloud registration using FilterReg and the integration of a modified Landing Period Indicator (LPI) algorithm. The results demonstrated the system’s ability to autonomously map ship decks, identify suitable landing sites, and execute landings on a ship moving under diffi- cult sea conditions. This work establishes a foundation for further development in autonomous maritime operations.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".