Giant Big Stik R/C UAV Computer Model Development in JSBSim for Sense and Avoid Applications
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
Open-source aerospace simulation packages often lack unmanned aerial vehicles (UAVs) models, limiting the study of their interaction with other elements in the airspace. These events, which are a consequence of encounters between manned and unmanned aircraft, have recently attracted interest due to the uncertainties created by UAVs in real environments. In this paper, a fit-for-purpose flight dynamics model specific for sense and avoid (SAA) strategies in near mid-air collision scenarios is developed based on existing model development practices and adjusted from flight data. The Giant Big Stik is recognized as the representative aircraft for testing SAA manoeuvres due to its capabilities. The simulation platform is based on the JSBSim open-source flight dynamics model, and the SAA application is carried out following the current regulations and flight recommendations for UAVs in Canada. Through this methodology, the error between the real and the computer model is reduced in every step that is minimal for the SAA application. The relevance of this paper is also shown in future applications, where this model will be incorporated into more complex simulations with manned aircraft for the study of avoidance manoeuvres that will serve the safe integration of UAVs into the airspace.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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