Development of a Landing Period Indicator and the Use of Signal Prediction to Improve Landing Methodologies of Autonomous Unmanned Aerial Vehicles on Maritime Vessels
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) are becoming more prevalent in maritime operations.One of the key challenges to the safe operation of UAVs at sea is the relative motion that exists between the UAV and ship.The scope of this thesis is the creation and evaluation of a methodology for improving the overall landing performance for UAVs using signal prediction and a developed Landing Period Indicator (LPI).The research is conducted in a synthetic environment, where the test vehicle is a quad rotor UAV that is equipped with a Light Detection and Ranging (LIDAR) system to aerially detect ship motion.The observed ship motion is forecasted using signal prediction which identifies and notifies the UAV of potential landing opportunities.The Signal Prediction Algorithm (SPA) is also used for Active Heave Compensation (AHC) to facilitate the UAV in maintaining a safe low hover position above the ship deck.Further, an algorithm is developed to use the AHC system to plan trajectories that land the UAV with a specified impact velocity.The development of the LPI system is presented and its performance as a standalone and supplemental system is evaluated.ShipMo3D was used to generate 105 sets of ship motion in sea states 2-6.The results in this thesis indicate that the developed landing methodologies can improve the landing performance of ocean going helicopters.For the 105 sets of ship motion, using a combination of the SPA, AHC, and the LPI improved landing performance by 25% in two separate test groups.Moreover, the results indicate that with further 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".