Measuring wheel diameter & axle track

DriveBase converts your commands from millimeters and body-degrees into wheel rotations using exactly two numbers:

db = DriveBase(left, right, wheel_diameter_mm=88, axle_track_mm=138)

wheel_diameter_mm — how far one wheel rotation moves the robot. Every straight() distance scales linearly with it.
axle_track_mm — the distance between the two wheels’ contact points with the floor. Every turn() angle scales linearly with it.

Getting these two right is worth more than any controller tuning: a 2 % diameter error is 20 mm of error on a 1 m drive, and a 2 % track error is ~7° of error on a full spin. A ruler gets you close; the two test drives below get you to a few tenths of a percent.

Calibrate wheel diameter first, then axle track — the turn calculation uses the wheel diameter, so a diameter error contaminates the track measurement.

Step 1 — wheel diameter, from a straight drive

Start from the nominal value (caliper across the tire, or the manufacturer’s spec). Then:

Put a strip of masking tape on the floor and align a marked point of the robot (e.g. the axle center) with its edge.
Run a long straight — the longer the drive, the better the resolution:
```
db.settings(straight_speed=150)
db.straight(1000)
```
Measure the distance actually traveled, from tape edge to the same marked point, in millimeters.
Scale the diameter by how far the robot really went:
```
new_diameter = old_diameter × measured_mm / 1000
```
Traveled 1023 mm with wheel_diameter_mm=88? Then 88 × 1023 / 1000 = 90.0 is your real diameter.

Repeat once with the new value: the measured distance should now land within a few millimeters of the command. Soft tires compress under the robot’s weight, so the effective diameter is often ~1-3 % smaller than the caliper says — the test drive measures reality, load included.

Step 2 — axle track, from an in-place spin

With the wheel diameter calibrated:

Align the robot against a straightedge (a wall or ruler touching both wheels works well) and note its exact heading.
Command several full spins in place — more turns amplify the error so it’s easier to measure. Keep the gyro off for this test (the default): the point is to measure what the encoders produce.
```
db.settings(turn_rate=120)
db.turn(3600)        # ten full clockwise spins
```
Measure the final heading error err_deg against your straightedge: positive if the robot rotated past the start alignment, negative if it stopped short. A protractor or a phone compass app is plenty — over ten spins, each degree of final error is only 0.1 % of track.
Scale the track by how far the robot really rotated:
```
new_track = old_track × (3600 + err_deg) / 3600
```
Overshot by 18° with axle_track_mm=138? Then 138 × 3618 / 3600 = 138.7 is your real track.

Note the direction: if the robot turns too far, the real track is larger than configured (each wheel-degree of travel produces less body rotation than the math assumed), so the correction increases the configured value.

The contact points matter, not the wheel centers: wide, soft tires effectively touch the ground inboard of their centerline, so the real track is usually a few millimeters less than what you measure center-to-center with a ruler.

Checking the result

Drive a square and see how close the robot returns to its start pose:

for _ in range(4):
    db.straight(300)
    db.turn(90)

With both values calibrated, the return error on a 300 mm square is typically under 1 cm and a few degrees. Do the calibration on the same surface the robot will compete on — carpet, foam mats, and wood all load the tires differently.

What about the gyro?

use_gyro(True) (with an imu= attached) makes turns terminate on measured body rotation, so heading no longer depends on the axle track being exact — wheel slip included. Calibrate the track anyway: the controller still uses it to shape the commanded wheel speeds, and encoder-only operation (gyro off, or no IMU on the robot) depends on it entirely.

How much the gyro buys depends on which IMU: an ICM-45686 read inside the 1 kHz control tick returned a four-turn square within +0.6° total on the reference bench, versus +0.5° to +1.8° per turn for a Python-pumped BNO055 and a few degrees encoder-only. examples/icm45686_square.py measures all of this on your own robot — run it and read the two drift numbers.