Examples: Avionics & Control
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The Avionics & Control examples demonstrate the application of the DeepCausality framework in high-assurance aerospace software patterns. These examples focus on safety-critical systems, Guidance, Navigation & Control (GNC), and autonomous interventions. By leveraging causal monads, geometric algebra, and stateful hypergraphs, the project illustrates how to build rigorous, coordinate-free, and auditable control logic for advanced aerospace platforms.
The primary goal of these examples is to showcase how to separate natural history (nominal operations) from forced history (autonomous interventions) and how to manage complex state transitions across sensor collection, filtering, and envelope monitoring stages. Sources: examples/avionics_examples/README.md, openspec/changes/archive/2026-05-03-add-stateful-flight-envelope-example/tasks.md:7.1-7.2
Core Avionics Patterns
The avionics module utilizes several advanced architectural patterns to ensure high-assurance and safety-critical compliance. These patterns revolve around formal computational interventions and coordinate-free dynamics.
Safety Interlocks via Intervenable
Instead of using ad-hoc conditional logic for safety overrides, the examples utilize the formal Intervenable trait (Pearl's Layer 2). This allows for a clean separation between the pilot's actions and autonomous system overrides, providing a rigorous audit trail for "Black Box" flight recorders. Sources: examples/avionics_examples/README.md
Coordinate-Free Dynamics
Kinematics and physics calculations in the avionics examples leverage Geometric Algebra (deep_causality_multivector). This approach avoids the singularities inherent in coordinate systems like Euler angles.
- Collision Detection: Uses bivector magnitude to calculate impact parameters directly.
- Hypersonic Tracking: Uses Conformal Geometric Algebra (CGA) or 6D phase space to linearize complex trajectories.
Sources: examples/avionics_examples/README.md
Flight Envelope Monitor
The flight_envelope_monitor is a primary example of a three-stage stateful pipeline. It demonstrates uniform composition through the PropagatingProcess type, which manages both a value channel and a state channel.
Pipeline Architecture
The monitor processes data through three distinct stages:
- Sensor Collection: Aggregates health signals from multiple sensors using
AggregateLogic::All. - Bind Chain: Sequentially updates risk, executes Kalman filtering for covariance, and writes state estimates.
- Envelope Hypergraph: A
CausaloidGraphthat evaluates complex interdependencies like stall risk, icing, and traffic conflicts.
The diagram shows the flow of data through the value channel (solid lines) and the accumulation of state (dotted lines) across the flight envelope monitor pipeline.
Sources: examples/avionics_examples/flight_envelope_monitor/README.md, openspec/changes/archive/2026-05-03-add-stateful-flight-envelope-example/tasks.md:7.2
Data Models
The system relies on structured models to track aircraft configuration and flight dynamics.
| Structure | Purpose | Key Fields |
|---|---|---|
FlightState | Dynamic state tracking | estimate, covariance, risk |
AircraftConfig | Static configuration | mass_kg, stall_margin, service_ceiling |
SensorReading | Raw input data | airspeed_kn, altitude_ft, vertical_speed |
SafetyVerdict | Outcome classification | Nominal, Caution, Warning, Failure |
Sources: openspec/changes/archive/2026-05-03-add-stateful-flight-envelope-example/tasks.md:2.1-2.5, examples/avionics_examples/flight_envelope_monitor/README.md
Control Flow Systems
For hard real-time systems, the ControlFlowBuilder provides a "Correct-by-Construction" methodology. This system ensures that nodes (functions) and edges (data flows) are compile-time verified, eliminating runtime integration errors.
Real-Time Characteristics
The avionics control flow is designed for environments requiring formal verification and Worst-Case Execution Time (WCET) analysis.
- Zero Heap Allocation: When used with the
strict-zstfeature, the system guarantees no heap allocations. - Static Dispatch: Avoids dynamic dispatch in the execution path to ensure deterministic timing.
- Fixed-Size Types: Utilizes fixed-size arrays and primitive types (e.g.,
[f32; 3]) for sensor readings and control updates.
The sequence illustrates the compile-time verification and subsequent deterministic execution of an avionics control graph.
Sources: openspec/changes/archive/control_flow_builder_next.md:3.1-4.0
Example Inventory
The avionics directory contains several domain-specific implementations:
| Example | Domain | Technical Focus |
|---|---|---|
magnav | Navigation | Causal Particle Filters and Bayesian estimation. |
geometric_tcas | Collision Avoidance | Geometric Algebra for impact parameters and safety interlocks. |
hypersonic_2t | Defense | Tracking Hypersonic Glide Vehicles in 6D phase space. |
flight_envelope_monitor | Health Monitoring | Multi-stage monadic pipelines and hypergraph risk evaluation. |
Sources: examples/avionics_examples/README.md, website/web/DESIGN.md:D8a
Implementation Details: Flight State Estimation
The estimation process utilizes a specific bind chain sequence to ensure that the flight state is updated incrementally before being evaluated by the envelope hypergraph.
- Health Fold: Extracts health from the sensor collection and increments the
state.risk. - Kalman Step: Performs a one-iteration scalar Kalman update on the diagonal elements of the
state.covariance. - Estimate Step: Writes the finalized estimates (
airspeed,altitude,attitude,vsi) into thestate.estimatearray.
This sequence ensures that the PropagatingProcess carries a full causal history in its EffectLog, which is essential for post-flight analysis and safety auditing. Sources: openspec/changes/archive/2026-05-03-add-stateful-flight-envelope-example/tasks.md:4.1-4.3, examples/avionics_examples/flight_envelope_monitor/README.md
Summary
Avionics and Control examples in DeepCausality serve as the blueprint for high-assurance, safety-critical software. By combining monadic state propagation with coordinate-free geometric math and compile-time verified control flows, the framework provides a robust environment for autonomous aerospace operations. These examples demonstrate not just causal reasoning, but the practical engineering required to deploy such systems in environments where failure is not an option.