An Auto-Landing Solution for a Drop Test RLV Demonstrator
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Unpowered drop test is very important for reusable launch vehicle (RLV) autolanding technology development. One of the challenges is to design an autolanding trajectory with enough robustness against uncertainties of drop conditions, aerodynamic characteristic and disturbances from control system and environment. In this paper, a solution including trajectory generation and control design is proposed for a drop test RLV demonstrator. Firstly, the drop test and vertical flight trajectory are introduced. Also, parts of the drop flight, segments of landing trajecory and trajectory design parameters in groups are shown. Secondly, an online trajectory generation method including self-adapted capture segment plan and landing trajectory optimization following UAV auto-landing experience are illustrated in detail by designing groups of parameters. Then, simple but practical gain schedule control laws are presented. Finally, mathematic simulation and analysis based on both RSS and Monte Carlo methods indicate that the solution proposed has shown an acceptable robustness and can provide enough capability for the demonstrator to land saftly.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
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) | 0.000 | 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 it