Methodology to Develop a Discrete-Event Supervisory Controller for an Autonomous Helicopter Flight
Bibliographic record
Abstract
The National Research Council Canada (NRC) is actively engaged in the development of an advanced autonomy system for the Bell 412 helicopter. This system’s capabilities extend to the execution of complex missions, such as arctic resupply missions. In an arctic resupply mission, the helicopter autonomously delivers supplies to a remote arctic base. During the mission it performs tasks such as takeoff, navigation, obstacle avoidance, and precise landing at its destination, all while minimizing the need for pilot intervention. The complexity of this autonomy system necessitates the inclusion of a high-level supervisory controller. This controller plays a critical role in monitoring mission progress, interacting with system components, and efficiently allocating resources. Conventionally, supervisory controllers are embedded within monolithic programs, lacking transparent state flows. This causes system modification and testing to be a significant challenge. In our research, we present an innovative approach and methodology to develop supervisory controllers for autonomous aircraft on the example of the NRC Bell 412. Using the Discrete Event System Specification (DEVS) formalism and the Cadmium simulation engine, we effectively address the challenges above. We discuss the entire development process for a state-based, event-driven supervisory controller for autonomous rotorcraft using the NRC’s Bell-412 autonomy system as a comprehensive case study. This process includes modeling, implementation, verification, validation, testing, and deployment. It incorporates a simulation phase, in which the supervisor integrates with components within a Digital Twin of the Bell 412, and a real-time operations phase, where the supervisor becomes an integral part of the actual Bell 412 helicopter. Our method outlines the smooth transition between these phases, ensuring a seamless and efficient process.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".