Failsafe Mechanism Design for Autonomous Aerial Refueling using State Tree Structures
Why this work is in the frame
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Bibliographic record
Abstract
Autonomous Aerial Refueling (AAR) is vulnerable to various failures and involves cooperation among autonomous receivers, tankers and remote pilots. Dangerous flight maneuvers may be executed when unexpected failures or command conflicts happen. To solve this problem, a failsafe mechanism based on State Tree Structures (STS) is proposed. The failsafe mechanism is a control logic that guides what subsequent actions the autonomous receiver should take, by observing real-time information of internal low-level subsystems such as guidance and drogue&probe and external instructions from tankers and pilots. To generate such a controller using STS, the AAR procedure is decomposed into several modes, and safety issues related with seven low-level subsystems are summarized. Then common functional demands and safety requirements are textually described. On this basis, the AAR plants and specifications are modeled by STS, and a supervisor is synthesized to control the AAR model. To prove its feasibility and correctness, a simulation environment incorporating such a logic supervisor is built and tested. The design procedures presented in this paper can be used in decision-making strategies for similar flight tasks. Supporting materials can be downloaded in Github, [ https://github.com/KevinDong0810/Failsafe-Design-for-AAR-using-STS ] including related software, input documents and output files.
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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 | 0.001 | 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 | 0.001 | 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