Model-based On-board Decision Making for Autonomous Aircraft
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
Powerful, small and lightweight sensors in combination with advanced failure detection, diagnosis, and prognostics techniques provide up-to-date data on the health status of a Unmanned Aerial System (UAS). In an autonomous UAS, this information must be used for automatic planning and execution of contingency actions to keep the UAS safe in adverse conditions.We present DM (Decision Maker), a software component which uses model-based reasoning, backtracking search to iteratively construct contingency plansthat are safe for the UAS to execute and pose minimal interruption to the mission goals. The DM, which has been developed within the NASA Autonomous Operating System (AOS) project thus fills the gap between Prognostics and Health Management and autonomous flight operations.In this paper, we describe DM and its reasoning/search algorithm and present the supporting modeling framework for the construction of system and fault models. An flight with a DJI S1000+ octocopter with fault injection will be used as our case study.
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 | 0.001 | 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