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Lesson / authored

Week 22 · Build: One controller owns the motors

Session 2 of 4 · Integrate the system · 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

Can every module cooperate without weakening the stop and measurement contracts? This session focuses on one controller owns the motors.

Before you start

The previous week’s recorded baseline and Week 21, 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

One controller owns the motors

Two loops writing motor commands can race: a safety stop might immediately be overwritten by navigation. Use one arbitration point that gives hazard, invalid-data and timeout conditions priority over movement. Clear old targets when leaving a state. A final cleanup stops motors, but periodic stop checks are also necessary while the program is running. A hardware power switch remains a backup; software does not replace supervision. Build an event log that records the selected action and its reason, not only sensor values.

Worked example — illustrative values

Consider a sample with goal_distance = 0.02 m and bumper_pressed = True. Even if 0.02 m is inside the arrival tolerance, hazard priority selects STOP_HAZARD. With a 60 s mission budget, an elapsed time of 60 s selects STOP_TIMEOUT. A lower-priority target must not overwrite either decision.

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/w22.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 arbitrate(*, fresh, hazard, elapsed_s, arrived):
    if hazard:
        return "STOP_HAZARD"
    if not fresh:
        return "STOP_INVALID"
    if elapsed_s >= 60:
        return "STOP_TIMEOUT"
    if arrived:
        return "STOP_ARRIVED"
    return "FOLLOW"

for case in [(True, True, 2, True), (False, False, 2, False),
             (True, False, 60, False), (True, False, 2, True)]:
    fresh, hazard, elapsed, arrived = case
    print(arbitrate(fresh=fresh, hazard=hazard, elapsed_s=elapsed, arrived=arrived))

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 arbitration function gives hazard priority, followed by invalid data, timeout and arrival. Thus simultaneous arrival and contact reports STOP_HAZARD. The sample returns decision strings; the device adapter’s single motor owner converts any stop decision into motors.off and prevents stale targets from being reused. Test combinations, not only one condition at a time. A later navigation write can undo a stop unless motor ownership is explicit. Record the input sample and selected reason so replay can identify the exact decision boundary where the system diverged.

Practical instructions

  1. Integrate sensor validity, encoder increments, planner and waypoint controller through explicit messages.
  2. Apply the arbitration rule before every motor write and clear targets on stops.
  3. Verify device button stop and finally cleanup with wheels raised before a short floor segment.
  4. Save raw inputs and decision reason codes together.

Experiment

Exercise simultaneous arrival/hazard and stale-data/target conditions to verify priority rather than accidental order.

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 bounded integrated segment or labelled simulation with verified stop priority.

Save notebook/w22-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

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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