step.calibration.calibrate_distance_dsl

Auto-generated step builders and DSL functions — DO NOT EDIT.

Source: calibrate_distance.py

Classes

CalibrateDistanceBuilder

Builder for CalibrateDistance. Auto-generated — do not edit.

Functions

calibrate_distance([distance_cm, speed, ...])

Calibrate per-wheel distance estimation via encoder measurement.

Module Contents

class step.calibration.calibrate_distance_dsl.CalibrateDistanceBuilder

Bases: raccoon.step.step_builder.StepBuilder

Builder for CalibrateDistance. Auto-generated — do not edit.

distance_cm(value: float)
speed(value: float)
calibrate_light_sensors(value: bool)
persist_to_yaml(value: bool)
ema_alpha(value: float)
calibration_sets(value: List[str] | None)
exclude_ir_sensors(value: List[IRSensor] | None)
step.calibration.calibrate_distance_dsl.calibrate_distance(distance_cm: float = 30.0, speed: float = 1.0, calibrate_light_sensors: bool = False, persist_to_yaml: bool = True, ema_alpha: float = 0.7, calibration_sets: List[str] | None = None, exclude_ir_sensors: List[IRSensor] | None = None)

Calibrate per-wheel distance estimation via encoder measurement.

Drives the robot a known distance, then prompts the user to enter the actual measured distance. The step computes a corrected ticks_to_rad value for each drive motor so that odometry matches real-world distances.

The calibration operates in two modes simultaneously:

  • Runtime: Applies the measured ticks_to_rad directly for best accuracy during this run.

  • Persistent: Updates the YAML baseline using an exponential moving average (EMA) so the stored value converges toward the true value over multiple calibration runs.

Parameters:

  • distance_cm – Distance (in cm) the robot drives during calibration. Longer distances yield better accuracy.

  • speed – Drive speed during the calibration runs, as a fraction of max speed in [0.0, 1.0]. Lower speeds reduce wheel slip and usually produce more accurate calibration.

  • calibrate_light_sensors – If True, run IR sensor calibration after the distance calibration is confirmed.

  • persist_to_yaml – If True, write the EMA-filtered baseline to raccoon.project.yml so it persists across program runs.

  • ema_alpha – EMA smoothing coefficient between 0.0 and 1.0. Higher values produce slower convergence but a more stable baseline.

  • calibration_sets – List of named IR calibration surface sets (e.g. ["default", "transparent"]). Only used when calibrate_light_sensors is True.

  • exclude_ir_sensors – List of IRSensor instances to skip during IR calibration.

Returns:

A CalibrateDistanceBuilder (chainable via .distance_cm(), .speed(), .calibrate_light_sensors(), .persist_to_yaml(), .ema_alpha(), .calibration_sets(), .exclude_ir_sensors(), .on_anomaly(), .skip_timing()).

Example:

from raccoon.step.calibration import calibrate_distance

# Distance-only calibration with defaults
calibrate_distance()

# Distance + IR sensor calibration
calibrate_distance(
    distance_cm=50.0,
    calibrate_light_sensors=True,
    calibration_sets=["default", "transparent"],
)