Week 16 · Build: Validity before fusion

Session 2 of 4 · Combine evidence · Phase 4

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

How should the robot act when different sensors give incomplete or conflicting evidence? This session focuses on validity before fusion.

Before you start

The previous week’s recorded baseline and Week 15, 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

Validity before fusion

Check freshness, range and calibration before interpreting a value. A stale encoder sample cannot support a speed estimate, and an uncalibrated boundary sensor should not authorize motion. Use explicit valid/unknown flags and timestamps. A consensus policy must define what happens when one signal is missing. Conservative stop intent can be a valid decision under uncertainty, although excessive false stops reduce usefulness. Track nuisance stops separately from missed hazards so you can improve the policy without weakening safety.

Worked example — illustrative values

If contact is true OR boundary is true, stop intent is true. If either measurement is stale, the policy also stops. With 18 successful approaches out of 20, observed success is 90%; it does not establish a 90% guarantee for all future routes.

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/w16.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 should_stop(contact, boundary, fresh):
    return not fresh or contact or boundary

for contact, boundary, fresh in [(False, False, True), (True, False, True),
                                  (False, True, True), (False, False, False)]:
    print(contact, boundary, fresh, "stop", should_stop(contact, boundary, fresh))

Run the example on desktop Python before adapting it. Change one valid input and check the result; keep device-only calls in a separate adapter. 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 Boolean stop rule is intentionally conservative: contact, a floor boundary or nonfresh evidence each blocks motion. Only all-clear fresh evidence permits continued intent. Enumerating all eight combinations proves this small logical contract, but not the physical detection quality. A sensor can supply a fresh but wrong reading. Keep calibration/confusion tests alongside the truth table. Do not average unrelated quantities such as encoder counts and reflectance values into one confidence number. Combine their physical meanings: pose supports route progress, floor sensing supports boundary guards and contact sensing supports an immediate stop.

Practical instructions

1. Implement the fusion rule and connect it to your state-machine stop intent in desktop replay.
2. Log validity separately from each reading and decision.
3. Replay missing, stale and contradictory inputs and verify no invalid signal authorizes movement.
4. Retain individual sensor readings so future analysis can identify the cause of a stop.

Experiment

Replay four missing/disagreement cases and record the exact reason code for each decision.

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

Logged valid/unknown handling and deterministic safe-stop behaviour.

Save notebook/w16-s2.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

• Explain validity before fusion in your own words using this session’s example and its units/assumptions.
• Produce the specific evidence above: Logged valid/unknown handling and deterministic safe-stop behaviour.
• Keep predictions and raw outcomes, distinguish observations from interpretation, and explain one limitation or unresolved failure.
• Review the Git diff, commit the session’s intended files and state the next experiment or safe continuation point.

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

• Focused reading: Robotics Lab: hardware and measurement guide. Study task: Identify which validity failures should prevent movement.
• Video/lecture option: MathWorks: What is PID control?. Study task: Draw the closed-loop signal path and label the measured quantity. Watch a relevant 5–10 minute excerpt or use the linked notes if video is inaccessible. This is supporting conceptual material; hardware in a demonstration may differ from yours.
• Practical reference: Engineering handbook and hardware setup. Manufacturer/API references and video metadata were checked on 2026-10-09; recheck the actual firmware before transferring code.

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.