Using formal methods to verify safe deep stall landing of a MAV
Bibliographic record
Abstract
The various fields of application for miniature air vehicles often do not provide distinct landing areas or even require additional equipment like nets or parachutes to land the aircraft without damaging it. This work introduces the deep stall landing (DSL) as a maneuver that uses the extraordinary aerodynamic characteristics of a delta wing MAV that come into effect after the angle of attack passes the stall angle. This landing maneuver is modeled based on a longitudinal aerodynamic model that takes lift, drag, thrust, weight, and pitching moment into account. By determining the operational modes that the aircraft has to perform in order to either complete the landing maneuver or abort it in case of a missed approach a hybrid system is identified. This system contains both continuous and discrete state dynamics that model the aircraft in each landing phase. Based on this hybrid system reachability analyses are performed which utilize level set methods to calculate backwards reachable sets. These sets are used to identify transitions within the modeled system that bring the aircraft form one operational mode to another without leaving the safe flight envelope. The final result is a discrete event system that covers all possible transitions within the refined model. Based on this discrete model an autonomous system can be implemented that is able to determine whether the initiation of the landing maneuver is safe in terms of keeping the aircraft within the safe flight envelope during the whole maneuver. Furthermore the results of the reachability analysis determine for which states of the aircraft it would be safe to initiate a recovery maneuver in case of a missed landing approach.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".