multirotor.helpers

Module Contents

Classes

DataLog

Records state and action variables for a multirotor and controller for each

Functions

moment_of_inertia_tensor_from_cooords(→ numpy.ndarray)	Calculate the inertial matrix given a distribution of point masses.
vehicle_params_factory(→ multirotor.vehicle.VehicleParams)	Create a simple multirotor vehicle parameters object. The multirotor has
find_nominal_speed(→ float)	Calculate the speed a propeller must spin to balance the weight.
learn_thrust_coefficient() → float)	Assuming a quadratic relationship between thrust and propeller speed,
learn_speed_voltage_scaling() → float)	Assuming a linear relationship between voltage and motor speed, learn
moment_of_inertia_disk(→ float)
control_allocation_matrix(→ Tuple[numpy.ndarray, ...)	Calculate the control allocation matrix such that:
get_vehicle_ability(vp, sp[, max_tilt, max_rads])

multirotor.helpers.moment_of_inertia_tensor_from_cooords(point_masses: Iterable[float], coords: Iterable[numpy.ndarray]) → numpy.ndarray

Calculate the inertial matrix given a distribution of point masses.

Parameters

point_massesIterable[float]

A list of masses.

coordsIterable[np.ndarray]

The corresponding coordinates of those masses about the center of rotation. Ideally, this would be the center of mass of the object.

Returns

np.ndarray

The 3x3 inertial matrix.

multirotor.helpers.vehicle_params_factory(n: int, m_prop: float, d_prop: float, params: multirotor.vehicle.PropellerParams, m_body: float, body_shape: str = 'sphere_solid', body_size: float = 0.1) → multirotor.vehicle.VehicleParams

Create a simple multirotor vehicle parameters object. The multirotor has evenly spaced propellers and a simple core shape (shell, cube etc.)

Parameters

nint

The number of arms/propellers.

m_propfloat

The mass of each propeller.

d_propfloat

The distance of each propeller from the center of the multirotor.

paramsPropellerParams

The parameters describing a propeller.

m_bodyfloat

The mass of the central body.

body_shapestr, optional

The shape of the core, by default ‘sphere_solid’

body_sizefloat, optional

The dimension of the core (m), by default 0.1

Returns

VehicleParams

The parameters object.

multirotor.helpers.find_nominal_speed(thrust_fn: Callable[[float], float], weight: float) → float

Calculate the speed a propeller must spin to balance the weight.

Parameters

thrust_fnCallable[[float], float]

A function taking the speed as input and outputting thrust (N).

weightfloat

The weight to balance.

Returns

float

The speed to balance the weight.

multirotor.helpers.learn_thrust_coefficient(thrust_fn: Callable[[float], float], domain: Tuple = (1, 10000)) → float

Assuming a quadratic relationship between thrust and propeller speed, estimate the coefficient of proportionality k_thrust, where

thrust = k_thrust . speed^2

Parameters

thrust_fnCallable[[float], float]

The function accepting speed and returning thrust.

domainTuple, optional

The range of speeds to try, by default (1, 10000)

Returns

float

The thrust coefficient.

multirotor.helpers.learn_speed_voltage_scaling(speed_fn: Callable[[float], float], domain: Tuple = (0, 20)) → float

Assuming a linear relationship between voltage and motor speed, learn the scaling coefficient, k_scaling, where:

voltage = k_scaling * speed

This can be put in MotorParams.speed_voltage_scaling so the speeds generated by the control allocation matrix are converted to corresponding voltages.

Parameters

speed_fnCallable[[float], float]

A function accepting voltage and returning speed. This should be the nominal case for a Motor class which has speed_voltage_scaling==1.

domainTuple, optional

The range of voltages to try to learn the coefficient, by default (0,20)

Returns

float

The scaling coefficient.

multirotor.helpers.moment_of_inertia_disk(m: float, r: float) → float

multirotor.helpers.control_allocation_matrix(params: multirotor.vehicle.VehicleParams) → Tuple[numpy.ndarray, numpy.ndarray]

Calculate the control allocation matrix such that:

action = matrix @ [thrust, torque_x, torque_y, torque_z]

Parameters

paramsVehicleParams

The vehicle parameters for which to compute the matrix

Returns

Tuple[np.ndarray, np.ndarray]

The allocation matrix and its inverse. If no inverse exists, returns the Moore-Penrose Pseudo-inverse.

multirotor.helpers.get_vehicle_ability(vp: multirotor.vehicle.VehicleParams, sp: multirotor.vehicle.SimulationParams, max_tilt: float = np.pi / 12, max_rads: float = 600)

class multirotor.helpers.DataLog(vehicle: Multirotor = None, controller: Controller = None, other_vars=None)

Records state and action variables for a multirotor and controller for each simulation step.

property t

property position

property x

property y

property z

property velocity

property orientation

property roll

property pitch

property yaw

property angular_rate

__bool__()

track(vehicle, controller, other_vars=None)

Register Multirotor and Controller instances to track, along with names of any other variables to be manually added.

>>> DataLog.track(Multirotor(), Controller(), other_vars=('error',))

Parameters

vehicleMultirotor

The vehicle to track.

controllerController

The controller to track.

__getitem__(item)

log(**kwargs)

Add the state and action variables from the Multirotor and Controller. Any keyword arguments should already have been registered in track() and their values are now appended to the list.

>>> DataLog.log(error=5)

done_logging()

Indicate that no more logs are going to be put so the python lists are converted to numpy arrays and discarded.

append(log: DataLog, relative=True)

_make_arrays(relative_to=None)

Convert python list to array and put up a flag that all arrays are up to date. If there was no controller, then actions remains None.

__len__()