RL Robotics LabSearch ↗
Lesson / authored

Week 21 · Learn: Requirements are testable promises

Session 1 of 4 · Define the mission · Phase 6

Plan about 15 minutes for explanation, 30 minutes for practical work and 10–15 minutes for documentation. A longer build may continue into the next session: stop safely, commit the current state and record the next check. Desktop simulations count as software evidence; label them clearly and record physical validation separately.

Engineering challenge

What exactly will your robot prove, and how will someone else check it? This session focuses on requirements are testable promises.

Before you start

The previous week’s recorded baseline and Week 20, Improve. For later sessions this week, retain the preceding session’s files and predictions.

Equipment: Desktop Python, editor, paper and ruler; for physical work, the configured 3pi+ 2040, clear floor mat and hardware checklist. Week 3 additionally uses the separate low-voltage LED circuit described in its procedure.

For any motion, verify the stop button, short time limit and clear floor area first. Keep the wheels raised for a new device program until its commands and stop behaviour are checked. A hazard or uncertain input is a reason to stop and document, not to force the trial to finish.

Theory and mathematics

Requirements are testable promises

A capstone mission should name its start, goal, map, permitted surface and success tolerance. “Navigate well” is not testable. “Reach within 10 cm of the surveyed goal in at most 60 seconds with no boundary breach” is testable, provided the course is designed to make it feasible. Limits here are student-selected teaching requirements, not Pololu performance claims. Define a safe-stop outcome separately from successful arrival. Freeze the evaluation procedure before final runs so success does not change to fit the results.

Worked example — illustrative values

An illustrative mission requires endpoint error ≤0.10 m, elapsed time ≤60 s and zero boundary breaches on a 1.2 m mapped route. A run ending 0.07 m away in 42 s passes those two numerical checks, but fails overall if it crosses a boundary. A safe timeout stop is a successful guard test and a failed navigation run.

Write the calculation in your notebook before running code. State which values you measured, which you assumed and which the program calculates. A correct numerical calculation cannot rescue an incorrect physical assumption.

Run and explain the model

The following is desktop Python, not a ready-to-run motor program. Download this week’s example, save it in your student repository and run python3 code/w21.py from the repository root. The same small model is reused across the week so you can learn it, build with it, test it and revise it.

# Desktop Python teaching example. Numerical inputs are illustrative.
def evaluate(error_m, elapsed_s, boundary_breaches, arrived):
    navigation_pass = arrived and error_m <= 0.10 and elapsed_s <= 60 and boundary_breaches == 0
    return {"navigation_pass": navigation_pass,
            "error_m": error_m, "elapsed_s": elapsed_s,
            "boundary_breaches": boundary_breaches}

print(evaluate(0.07, 42, 0, True))
print(evaluate(0.07, 42, 1, True))
print(evaluate(0.07, 60, 0, False))

Predict the example’s output by hand. Mark the inputs, units and assumptions; explain where this model could fail. If an exception appears, read its final line, identify the input or assumption that caused it and make the smallest explained correction. Do not delete validation merely to obtain output.

Understanding the model and its limits

The acceptance function combines arrived state, independent endpoint error, elapsed time and boundary count with logical AND. Every required condition must hold. A timeout can be a correct guard response while failing navigation because arrived is false. Separate guard-test outcomes from the mission success table. Before coding this rule, classify several fictional rows manually and resolve ambiguities such as missing measurements. A missing endpoint must not silently become zero error. The final criteria should reflect the surveyed course and your demonstrated capabilities, rather than a target chosen after seeing the results.

Practical instructions

  1. Choose a mapped start/goal route within the Phase 5 qualified area.
  2. Write four numerical or Boolean requirements, including independent endpoint measurement and stopping.
  3. State what the mission excludes and which optional extensions are deferred.
  4. Predict two passing and two failing outcomes against the same written rules.

Experiment

Classify four fictional outcomes before coding the acceptance rule; include one safe-stop-but-not-arrived result.

Before testing, record your prediction, changed factor, measured response, fixed conditions and stopping rule. Save every attempted run, including failures, with a condition and source version. If hardware is unavailable, use an explicitly labelled synthetic/replay dataset and list the physical question it cannot answer. Do not invent completed trials.

Deliverable

A mission with four testable requirements and separately defined safe-stop outcomes.

Save notebook/w21-s1.md, the relevant code revision, raw CSV or test-case records, and one labelled diagram/plot/table. Link the files relatively from your notebook. Use the entry template and report guide.

Completion criteria

A documented failed prediction can meet the learning criteria. A missing physical trial must remain marked untested; software success alone does not validate the robot.

Reading and video

Reflection and next step

Which assumption most affected your result? Point to one observation that supports your explanation and one alternative explanation the evidence has not ruled out. Write a specific next test with a changed factor and measurable outcome, then proceed through the week’s Learn → Build → Experiment → Improve cycle.

← Previous sessionCourse roadmapNext session →