RAOP
Concept Review
This page summarizes the core concepts used throughout RAOP. It is designed for educators who may be new to control systems, robotics, and modeling. Each concept includes what educators should be able to explain using evidence from lab artifacts.
- Use the Resource Navigator to locate supporting materials (manuals, quick-start guides, concept notes).
- During labs, write one short “evidence-based explanation” per concept (what you observed and why).
- Translate the concept into a classroom-friendly activity using your lesson plan template.
Simulation-to-Hardware Workflow
RAOP uses a simulation-to-hardware workflow to help educators build confidence in inquiry-based experimentation while minimizing setup risk. Educators begin in the virtual environment, validate expected behavior, and then confirm key results during the on-site hardware week.
- How to compare expected vs. observed behavior using measurable outputs (plots, logs, screenshots).
- How to interpret model assumptions and what can cause differences when moving to hardware.
- How to document evidence using a guided inquiry structure (predict → test → observe → explain → reflect).
- Parameter differences (mass, friction, sensor calibration).
- Sampling/time-step effects and numerical discretization.
- Hardware constraints and safety limits.
- Noise, bias, and drift in sensing.
Control Foundations (P/PD + Basic Design)
Educators learn core control ideas using accessible experiments: how feedback changes behavior, how gains affect response, and how to diagnose stability and performance using simple metrics.
- Open-loop vs. closed-loop behavior; why feedback improves tracking/disturbance rejection.
- How proportional gain affects rise time, overshoot, and steady-state error.
- How derivative action improves damping and reduces oscillation (qualitative interpretation).
- What “stability” means in practice (bounded response, no runaway oscillation).
- Step-response plots (before/after tuning).
- Short interpretation prompts (what changed and why).
- A minimal tuning record (gain values and observed outcomes).
Sensing & Interpretation
Educators practice reading and interpreting sensor data, linking measurements to physical meaning, and documenting uncertainty and data quality in a classroom-friendly way.
- Sensor signals vs. physical quantities (units, scaling, calibration).
- Noise and bias: how to identify them from data and how they affect conclusions.
- Why filtering is sometimes needed and how to justify it (simple, not math-heavy).
- A short “data quality” note with at least one example plot or screenshot.
- A brief conclusion supported by evidence (and limitations).
Follow RAOP
Updates, announcements, and participant highlights.
Official RAOP social channels. Additional platforms will be added as they launch.