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| en:safeav:ctrl:testing [2026/03/26 13:30] – airi | en:safeav:ctrl:testing [2026/04/23 11:27] (current) – [Cross-Domain Insight] raivo.sell |
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| ^ # ^ Project Title ^ Description ^ Learning Objectives ^ | Space systems have the most specialized and constrained physical testing infrastructure. Because full end-to-end testing in the operational environment is impossible, engineers rely on **high-fidelity ground facilities** that replicate aspects of space conditions. These include thermal vacuum chambers (e.g., NASA Johnson Space Center Chamber A), vibration and acoustic test facilities for launch loads, and propulsion test stands (e.g., Stennis Space Center). RF anechoic chambers validate communication and sensing systems. While these facilities achieve extreme fidelity for specific physics, **system-level validation is fragmented**, requiring heavy reliance on simulation and incremental subsystem testing. The cost and irreversibility of failure drive a test philosophy centered on qualification, redundancy, and conservative margins. |
| | 1 |Classical vs AI Control Benchmark Study | Implement and compare a classical controller (e.g., PID or LQR) with an AI-based controller (e.g., reinforcement learning) for a simplified vehicle model in simulation. Evaluate performance under nominal and disturbed conditions. |- Understand differences between model-based and data-driven control \\ - Analyze stability, robustness, and interpretability trade-offs \\ - Evaluate controller performance under uncertainty and disturbances | | |
| | 2 |Behavioral & Motion Planning Stack Design | Design a hierarchical autonomy stack that includes a behavioral layer (FSM or behavior tree) and a motion planner (A*, RRT*, or MPC). Apply it to a scenario such as lane change or obstacle avoidance. | * Distinguish between behavioral decision-making and motion planning \\ * Implement planning algorithms under constraints \\ * Understand integration between perception, planning, and control | | ===== Cross-Domain Insight ===== |
| | 3 |Scenario-Based Validation Framework | Develop a scenario-based testing framework using parameterized scenarios (e.g., varying speeds, distances, agent behaviors). Use a simulator to evaluate planning/control performance across these scenarios. | * Apply design-of-experiments (DOE) to autonomy validation \\ * Define and measure safety metrics (e.g., TTC, collision rate) \\ * Understand coverage and edge-case testing challenges | | |
| | 4 |Digital Twin & Multi-Fidelity Simulation Study | Build a simplified digital twin of a vehicle and environment. Perform validation using both low-fidelity and high-fidelity simulation setups, comparing results and identifying discrepancies. | * Understand role of digital twins in V&V \\ * Analyze trade-offs between simulation fidelity and scalability \\ * Quantify sim-to-real gaps and their implications | | Across all four domains, physical testing evolves from **highly repeatable, scenario-rich environments (ground)** to **physics-constrained, partial-reality validation (space)**. Airborne and marine systems sit in between, blending controlled facilities with real-world trials. A consistent trend is the integration of **instrumented test environments with digital twins**, enabling bidirectional feedback between physical experiments and simulation models—an increasingly critical capability for validating autonomous and safety-critical systems. |
| | 5 |Formal Methods for Safety Validation | Define safety requirements using a formal specification approach (e.g., temporal logic or rule-based constraints). Apply these to simulation traces and identify violations or edge cases. | * Translate safety requirements into formal, testable properties \\ * Use formal methods for falsification and validation \\ * Understand limitations of simulation without formal rigor | | |
