Assuring Behavior of Multirobot Autonomous Systems With Translation From Formal Verification to ROS Simulation
Bibliographic record
Abstract
Formal verification provides assurance to the modeling and design of robotic applications in executing autonomous operations. With the advancement of technologies, robotic applications have evolved to integrate multiple distributed robots. As a result, the integration of formal verification-based methods to assure the correctness of the interactions between multiple distributed robots has become ever more important. However, going from formally verified models designed in formal environments/software such as UPPAAL to robotic simulation software such as robot operating system (ROS) and Gazebo is time-consuming and prone to human errors. Nonetheless, such a translation from formal to simulation environment is essential for robotic applications that are going to be deployed in the real world, for obvious economical and safety reasons. In this article, we provide our insights into the development of a framework that integrates design and formal verification at a higher level of abstraction and then performing a translation to ROS, focusing on a scenario for distributed drones representing urban air mobility. Through this article, we seek to accelerate the development cycle in transitioning from formally verified systems to simulation.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".