step.calibration.calibrate_dsl

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

Source: calibrate.py

Classes

CalibrateBuilder

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

Functions

calibrate([distance_cm, speed, persist_to_yaml, ...])

Run a unified distance and IR sensor calibration.

Module Contents

class step.calibration.calibrate_dsl.CalibrateBuilder

Bases: raccoon.step.step_builder.StepBuilder

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

distance_cm(value: float)
speed(value: float)
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_dsl.calibrate(distance_cm: float = 30.0, speed: float = 1.0, persist_to_yaml: bool = True, ema_alpha: float = 0.7, calibration_sets: List[str] | None = None, exclude_ir_sensors: List[IRSensor] | None = None)

Run a unified distance and IR sensor calibration.

This is the recommended all-in-one calibration entry point. It drives the robot a known distance, prompts the user to measure the actual distance traveled, then adjusts the per-wheel ticks_to_rad values to correct odometry. After distance calibration, it automatically calibrates IR sensors by sampling them during a drive over the calibration surface(s).

Calibration values are persisted to raccoon.project.yml using an exponential moving average (EMA), so the baseline converges toward the true value over multiple calibration runs.

Prerequisites:

The robot must have drive motors with encoder feedback and a configured kinematics model. For IR calibration, IR sensors must be registered in robot.defs.analog_sensors.

Parameters:

  • distance_cm – Distance (in cm) the robot drives during calibration. Longer distances yield more accurate results.

  • 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.

  • persist_to_yaml – If True, write the EMA-filtered baseline back to raccoon.project.yml so it persists across 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 to run (e.g. ["default", "transparent"]). Each set beyond the first triggers an additional drive-and-sample cycle.

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

Returns:

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

Example:

from raccoon.step.calibration import calibrate

# Basic calibration with defaults
calibrate()

# Custom: longer drive, two IR surface sets
calibrate(
    distance_cm=50.0,
    calibration_sets=["default", "transparent"],
)