Applying Formal Methods to Build a Safe Continuous-Control Architecture for an Unmanned Aerial Vehicle
Bibliographic record
Abstract
<title>Abstract</title> Cyber-Physical Systems (CPS) are systems composed of computational and physical processes where constant interaction with the surrounding environment exists. Unmanned Aerial Vehicles (UAVs) can be highlighted as a typical example of CPS. It contains devices that sense the surrounding environment (e.g., IMU, GPS) and provide data for the embedded continuous-control software to compute the CPS reactions. Such reactions are, in fact, actions in the physical (electro-mechanical) process, which occur using actuators (e.g., motors' speed controllers). Such a CPS is typically classified as safety-critical because a failure might have severe implications. Therefore, providing safety guarantees is of utmost importance when designing this application. This paper presents a solution for offering safety guarantees during the design of the continuous-control architecture, which is one of the most critical parts of the CPS. The present proposal applies formal verification (FV) techniques to detect software errors and verify if the architecture is suitable to cope with the real-time requirements coming from the system specification. The first verification round targets individual elements of the architecture, especially the continuous-control algorithm. Therefore, the ESBMC model checker is used; it receives the element's source code as input and can check for a set of language-specific properties, such as memory safety and concurrency vulnerabilities. After making all the individual analyses and performing subsequent corrections, another verification process is started using the UPPAAL model checker, aiming to make the schedulability analysis of the proposed architecture. Finally, we conduct a runtime monitoring analysis using our recently developed RMLib tool. This proposal was successfully used within the design process of a UAV, where different classes of design and implementation problems were detected and further corrected, as detailed in the paper.
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How this classification was reachedexpand
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 | 0.019 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".