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Week 4 · Experiment: Command-response curves

Session 3 of 4 · Motors and motion · 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

How much can a timed motor command tell you about actual motion? This session focuses on command-response curves.

Before you start

The previous week’s recorded baseline and Week 3, 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

Command-response curves

Plot input command on the horizontal axis and measured travel or speed on the vertical axis. A straight trend in the middle does not imply a line through the origin across the full range. Deadband and saturation create different regions. Repeat each setting to reveal variation instead of drawing a line through one lucky trial. If one wheel drifts, measuring only total forward distance hides the mismatch. Observe heading or lateral displacement too. Keep battery set, surface, starting pose and pulse duration fixed so command is the main changed variable.

Worked example — illustrative values

For illustrative 0.5 s trials at command 300 and 600 travelling 0.04 and 0.10 m, speeds are 0.08 and 0.20 m/s. The local slope is (0.20 − 0.08)/(600 − 300) = 0.0004 m/s per command unit. It predicts 0.14 m/s at 450. These are fictional values, not a safe command recommendation.

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/w04.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.
command = [300, 600]
speed_m_s = [0.08, 0.20]  # fictional
slope = (speed_m_s[1] - speed_m_s[0]) / (command[1] - command[0])
intercept = speed_m_s[0] - slope * command[0]
print("predicted_m_s", slope * 450 + intercept)

Use your own recorded data or named test fixtures instead of the illustrative inputs. Save expected and actual values side by side. 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 two-point slope is rise in measured speed divided by rise in command: its units are m/s per command unit. The intercept makes the straight line pass through the first point. With the illustrative points the model predicts 0.14 m/s at command 450. This is interpolation between measured settings. Extrapolating to zero or a very large command is unjustified because friction creates a dead zone and the motor/battery imposes limits. Plot raw points beside the fitted segment. A two-point fit has no spare evidence to assess its own quality, so reserve another setting for validation.

Practical instructions

  1. Choose three low settings within the pilot-tested command limit; do not use the full vendor range.
  2. Run three trials at each setting with one fixed duration and starting method.
  3. Record distance, time, heading drift and stop behaviour; label startup/no-motion cases.
  4. Plot mean speed versus command with individual trials visible.

Experiment

Vary command only across three tested settings with three repeats each. Look for a deadband rather than removing zero-travel trials.

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

Nine trials with a command-response plot, units and heading observations.

Save notebook/w04-s3.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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