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Lesson / authored

Week 16 · Improve: A defensible avoidance claim

Session 4 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 a defensible avoidance claim.

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

A defensible avoidance claim

Name the exact claim supported by the evidence: for example, detects taped boundaries or stops on soft front contact within the tested low-speed enclosure. This is weaker and more accurate than claiming general obstacle avoidance in an arbitrary room. Non-contact range sensing remains an optional extension requiring a specified sensor, mounting, power and driver. Explain blind spots, especially rear/side space and surfaces unseen by downward sensors. A useful report includes success, false-stop and safe-failure rates rather than hiding interrupted runs.

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))

Compare the unchanged baseline and your proposed change on the same inputs. Keep the original files so another reader can reproduce the comparison. 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. Choose one evidence-supported improvement, such as a narrower hysteresis band or clearer validity timeout.
  2. Replay the complete frozen dataset before a small physical validation.
  3. Write a Phase 4 report with operating envelope, confusion counts and recovery limitations.
  4. Carry the stop/validity contract into localization and capstone integration.

Experiment

Change one policy parameter, compare on the same frozen replay data, then validate on four fresh cases.

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 qualified avoidance report with verified limits and a reusable stop contract.

Save notebook/w16-s4.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.

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