FrozenTransformScenario

Robotics scenarios · Implementation: mlsysim/engine/scenario_definitions/robotics/estimation.py.

Construct this scenario with keyword inputs. The fields below show the types and defaults declared by this class.

site’s conveyor object tracked by a camera lost at loss_time; its last transform republished every republish_period.

Notes

Equation: result evaluates the configured solver; its contract owns the governing equations.

Assumptions: Conveyor speed is constant and republishing changes timestamp freshness without adding observations.

Excludes: Prediction correction, transport jitter, and loss_time as a computational offset.

References

Derivation: The scenario binds declared inputs to FrozenTransformModel. Those implementations document their derivations and sources.

Examples

Execute the registered worked inputs; this is a modeled calculation.

>>> from mlsysim import Scenarios
>>> case = Scenarios.ConveyorMugFrozenTransform
>>> value = case.result
>>> print(f"{value.error:.3f~P}")
20.000 mm

Attributes

Name	Description
freshness_threshold
loss_time
republish_period
result	Evaluate the configured inputs with mlsysim.solvers.FrozenTransformModel.solve.
site
window

site

Type: Any.

Declared default: required.

republish_period

Type: Quantity.

Declared default: required.

freshness_threshold

Type: Quantity.

Declared default: required.

loss_time

Type: Quantity.

Declared default: required.

window

Type: Quantity.

Declared default: required.

result

engine.scenario_types.FrozenTransformScenario.result

Evaluate the configured inputs with mlsysim.solvers.FrozenTransformModel.solve.

The returned typed result retains the solver’s documented units, assumptions, and feasibility semantics; this property adds no measurement or safety claim. Equation: result = FrozenTransformModel().solve(...) using the fields shown below.