Guidance of Unmanned Aerial Gliders for Wildfire Surveillance
Bibliographic record
Abstract
Sustained aerial surveillance of wildfires provides crucial information on the state of the fire to aid with firefighting operations. In order to provide long-term aerial support, Unmanned Aerial Gliders are capable of utilizing their aerodynamic design to exploit fire-induced updraft generated by the substantial temperature differences in a wildfire environment. This work presents a guidance strategy which allows the glider to locate fire-induced updraft and exploit it. A high-fidelity simulation is developed by characterizing aircraft aerodynamics using Digital DATCOM and modeling the wildfire using WRF-Fire to capture the atmospheric effects of the fire on wind conditions. A cascaded control law is designed to achieve desired airspeed and course values, and a path following approach is implemented to generate the desired guidance commands. A thermal estimator based on an energy state of the vehicle is implemented to locate the point of maximum updraft during flight. To remain within the region of irregularly shaped fire-induced updraft, an oblong Dubins path is designed. Furthermore, based on the behavior of updraft in wildfire conditions, a path manager is proposed to efficiently locate the region of updraft and design the oblong path using the measured properties of the updraft distribution. Simulations are performed in various conditions which show the ability to efficiently locate fire-induced updraft and exploit it.
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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 | 0.000 |
| 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) | 0.001 | 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".