Week 1 · Learn: Sense, decide, act

Session 1 of 4 · Meet your robot · Phase 1

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 can your robot actually measure, and which claims still need evidence? This session focuses on sense, decide, act.

Before you start

Week 1 hardware orientation and a notebook; no prior programming is assumed.

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

Sense, decide, act

A robot connects information to action. A sensor supplies an observation, a program decides what to do, and an actuator changes the world. A pushbutton is a sensor even though a person operates it. A wheel motor is an actuator. The battery supplies energy, not instructions. Feedback closes this chain: after acting, the robot measures the result and adjusts. Without feedback a timed motor command cannot know whether a wheel slipped. A system diagram is useful because it exposes missing measurements before you write code.

Worked example — illustrative values

For fictional distances 19, 20, 20, 21 and 20 cm, the mean is (19 + 20 + 20 + 21 + 20)/5 = 20 cm. The range is 21 − 19 = 2 cm. Travelling 0.20 m in 1.0 s gives 0.20 m/s. This average says nothing about the speed at each instant.

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/w01.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.
distances_cm = [19, 20, 20, 21, 20]
print("mean_cm", sum(distances_cm) / len(distances_cm))
print("range_cm", max(distances_cm) - min(distances_cm))

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 list holds five distance observations in centimetres. len counts them, sum adds them, and division produces their mean. max and min select endpoints of the observed spread. Neither calculation estimates a cause. Replacing the last value with 30 cm changes the mean and range: work out both before running. Retain that value if it was genuinely measured; first investigate whether it represents a transcription error, a setup change or actual unusual motion. The calculation is simple enough to check independently, which is the point of the first experiment.

Practical instructions

1. Identify battery, controller, wheels and three sensors from the Pololu manual.
2. Draw one sensing-to-action chain and label each physical quantity.
3. If BB-8 or Dash is available, record its exact model, app availability and pairing result; do not make it a dependency.
4. Write a confirmed/unknown capability table with a source for each claim.

Experiment

Predict which robot capabilities are directly measurable; check five claims against manuals or observable behaviour and classify each as confirmed, contradicted or unknown.

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 labelled system map and five evidence-backed capability claims; correctly explain feedback versus a timed command.

Save notebook/w01-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

• Explain sense, decide, act in your own words using this session’s example and its units/assumptions.
• Produce the specific evidence above: A labelled system map and five evidence-backed capability claims; correctly explain feedback versus a timed command.
• 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: Pololu: overview and getting started. Study task: Identify which sensors measure motion and which measure the surrounding floor.
• Video/lecture option: CS50 Python: functions and variables. Study task: Use the lecture/notes to identify input, output and a function call. 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.