# Simulation Physics¶

pyfrc supports simplistic custom physics model implementations for simulation and testing support. It can be as simple or complex as you want to make it. We will continue to add helper functions (such as the pyfrc.physics.drivetrains module) to make this a lot easier to do. General purpose physics implementations are welcome also!

The idea is you provide a PhysicsEngine object that interacts with the simulated HAL, and modifies motors/sensors accordingly depending on the state of the simulation. An example of this would be measuring a motor moving for a set period of time, and then changing a limit switch to turn on after that period of time. This can help you do more complex simulations of your robot code without too much extra effort.

Note

One limitation to be aware of is that the physics implementation currently assumes that you are only calling wpilib.delay() once per main loop iteration. If you do it more than that, you may get some rather funky results.

By default, pyfrc doesn’t modify any of your inputs/outputs without being told to do so by your code or the simulation GUI.

See the physics sample for more details.

## Enabling physics support¶

You must create a python module called physics.py next to your robot.py. A physics module must have a class called PhysicsEngine which must have a function called update_sim. When initialized, it will be passed an instance of this object.

You must also create a ‘sim’ directory, and place a config.json file there, with the following JSON information:

{
"pyfrc": {
"robot": {
"w": 2,
"h": 3,
"starting_x": 2,
"starting_y": 20,
"starting_angle": 0
}
}
}

class pyfrc.physics.core.PhysicsEngine(physics_controller)[source]

Your physics module must contain a class called PhysicsEngine, and it must implement the same functions as this class.

Alternatively, you can inherit from this object. However, that is not required.

The constructor must take the following arguments:

Parameters: physics_controller (PhysicsInterface) – The physics controller interface
initialize(hal_data)[source]

Called with the hal_data dictionary before the robot has started running. Some values may be overwritten when devices are initialized… it’s not consistent yet, sorry.

update_sim(hal_data, now, tm_diff)[source]

Called when the simulation parameters for the program need to be updated. This is mostly when wpilib.delay() is called.

Parameters: hal_data – A giant dictionary that has all data about the robot. See hal-sim/hal_impl/data.py in robotpy-wpilib’s repository for more information on the contents of this dictionary. now (float) – The current time tm_diff (float) – The amount of time that has passed since the last time that this function was called
exception pyfrc.physics.core.PhysicsInitException[source]
class pyfrc.physics.core.PhysicsInterface(robot_path, fake_time, config_obj)[source]

An instance of this is passed to the constructor of your PhysicsEngine object. This instance is used to communicate information to the simulation, such as moving the robot on the field displayed to the user.

add_analog_gyro_channel(ch)[source]

This function is no longer needed

add_device_gyro_channel(angle_key)[source]
Parameters: angle_key – The name of the angle key in hal_data['robot']
add_gyro_channel(ch)

This function is no longer needed

distance_drive(x, y, angle)[source]

Call this from your PhysicsEngine.update_sim() function. Will update the robot’s position on the simulation field.

This moves the robot some relative distance and angle from its current position.

Parameters: x – Feet to move the robot in the x direction y – Feet to move the robot in the y direction angle – Radians to turn the robot
drive(speed, rotation_speed, tm_diff)[source]

Call this from your PhysicsEngine.update_sim() function. Will update the robot’s position on the simulation field.

You can either calculate the speed & rotation manually, or you can use the predefined functions in pyfrc.physics.drivetrains.

The outputs of the drivetrains.* functions should be passed to this function.

Note

The simulator currently only allows 2D motion

Parameters: speed – Speed of robot in ft/s rotation_speed – Clockwise rotational speed in radians/s tm_diff – Amount of time speed was traveled (this is the same value that was passed to update_sim)
get_offset(x, y)[source]

Computes how far away and at what angle a coordinate is located.

Distance is returned in feet, angle is returned in degrees

Returns: distance,angle offset of the given x,y coordinate

New in version 2018.1.7.

get_position()[source]
Returns: Robot’s current position on the field as (x,y,angle). x and y are specified in feet, angle is in radians
vector_drive(vx, vy, vw, tm_diff)[source]

Call this from your PhysicsEngine.update_sim() function. Will update the robot’s position on the simulation field.

This moves the robot using a velocity vector relative to the robot instead of by speed/rotation speed.

Parameters: vx – Speed in x direction relative to robot in ft/s vy – Speed in y direction relative to robot in ft/s vw – Clockwise rotational speed in rad/s tm_diff – Amount of time speed was traveled

## Drivetrain support¶

Warning

These drivetrain models are not particularly realistic, and if you are using a tank drive style drivetrain you should use the TankModel instead.

Based on input from various drive motors, these helper functions simulate moving the robot in various ways. Many thanks to Ether for assistance with the motion equations.

When specifying the robot speed to the below functions, the following may help you determine the approximate speed of your robot:

• Slow: 4ft/s
• Typical: 5 to 7ft/s
• Fast: 8 to 12ft/s

Here’s an example usage of the drivetrains:

from pyfrc.physics import drivetrains

class PhysicsEngine:

def __init__(self, physics_controller):
self.physics_controller = physics_controller

def update_sim(self, hal_data, now, tm_diff):
# TODO: get motor values from hal_data
speed, rotation = self.drivetrain.get_vector(l_motor, r_motor)
self.physics_controller.drive(speed, rotation, tm_diff)

# optional: compute encoder
# l_encoder = self.drivetrain.l_speed * tm_diff

class pyfrc.physics.drivetrains.FourMotorDrivetrain(x_wheelbase=2, speed=5, deadzone=None)[source]

Four motors, each side chained together. The motion equations are as follows:

FWD = (L+R)/2
RCW = (L-R)/W

• L is forward speed of the left wheel(s), all in sync
• R is forward speed of the right wheel(s), all in sync
• W is wheelbase in feet

If you called “SetInvertedMotor” on any of your motors in RobotDrive, then you will need to multiply that motor’s value by -1.

Note

WPILib RobotDrive assumes that to make the robot go forward, the left motors must be set to -1, and the right to +1

New in version 2018.2.0.

Parameters: x_wheelbase (float) – The distance in feet between right and left wheels. speed (float) – Speed of robot in feet per second (see above) deadzone (Optional[Callable[[float], float]]) – A function that adjusts the output of the motor (see linear_deadzone())
get_vector(lr_motor, rr_motor, lf_motor, rf_motor)[source]
Parameters: lr_motor (float) – Left rear motor value (-1 to 1); -1 is forward rr_motor (float) – Right rear motor value (-1 to 1); 1 is forward lf_motor (float) – Left front motor value (-1 to 1); -1 is forward rf_motor (float) – Right front motor value (-1 to 1); 1 is forward Tuple[float, float] speed of robot (ft/s), clockwise rotation of robot (radians/s)
l_speed = 0

Use this to compute encoder data after get_vector is called

class pyfrc.physics.drivetrains.MecanumDrivetrain(x_wheelbase=2, y_wheelbase=3, speed=5, deadzone=None)[source]

Four motors, each with a mechanum wheel attached to it.

If you called “SetInvertedMotor” on any of your motors in RobotDrive, then you will need to multiply that motor’s value by -1.

Note

WPILib RobotDrive assumes that to make the robot go forward, all motors are set to +1

New in version 2018.2.0.

Parameters: x_wheelbase (float) – The distance in feet between right and left wheels. y_wheelbase (float) – The distance in feet between forward and rear wheels. speed (float) – Speed of robot in feet per second (see above) deadzone (Optional[Callable[[float], float]]) – A function that adjusts the output of the motor (see linear_deadzone())
get_vector(lr_motor, rr_motor, lf_motor, rf_motor)[source]

Given motor values, retrieves the vector of (distance, speed) for your robot

Parameters: lr_motor (float) – Left rear motor value (-1 to 1); 1 is forward rr_motor (float) – Right rear motor value (-1 to 1); 1 is forward lf_motor (float) – Left front motor value (-1 to 1); 1 is forward rf_motor (float) – Right front motor value (-1 to 1); 1 is forward Tuple[float, float, float] Speed of robot in x (ft/s), Speed of robot in y (ft/s), clockwise rotation of robot (radians/s)
lr_speed = 0

Use this to compute encoder data after get_vector is called

class pyfrc.physics.drivetrains.TwoMotorDrivetrain(x_wheelbase=2, speed=5, deadzone=None)[source]

Two center-mounted motors with a simple drivetrain. The motion equations are as follows:

FWD = (L+R)/2
RCW = (L-R)/W

• L is forward speed of the left wheel(s), all in sync
• R is forward speed of the right wheel(s), all in sync
• W is wheelbase in feet

If you called “SetInvertedMotor” on any of your motors in RobotDrive, then you will need to multiply that motor’s value by -1.

Note

WPILib RobotDrive assumes that to make the robot go forward, the left motor must be set to -1, and the right to +1

New in version 2018.2.0.

Parameters: x_wheelbase (float) – The distance in feet between right and left wheels. speed (float) – Speed of robot in feet per second (see above) deadzone (Optional[Callable[[float], float]]) – A function that adjusts the output of the motor (see linear_deadzone())
get_vector(l_motor, r_motor)[source]

Given motor values, retrieves the vector of (distance, speed) for your robot

Parameters: l_motor (float) – Left motor value (-1 to 1); -1 is forward r_motor (float) – Right motor value (-1 to 1); 1 is forward Tuple[float, float] speed of robot (ft/s), clockwise rotation of robot (radians/s)
pyfrc.physics.drivetrains.four_motor_drivetrain(lr_motor, rr_motor, lf_motor, rf_motor, x_wheelbase=2, speed=5, deadzone=None)[source]

Deprecated since version 2018.2.0: Use FourMotorDrivetrain instead

pyfrc.physics.drivetrains.four_motor_swerve_drivetrain(lr_motor, rr_motor, lf_motor, rf_motor, lr_angle, rr_angle, lf_angle, rf_angle, x_wheelbase=2, y_wheelbase=2, speed=5, deadzone=None)[source]

Four motors that can be rotated in any direction

If any motors are inverted, then you will need to multiply that motor’s value by -1.

Parameters: lr_motor – Left rear motor value (-1 to 1); 1 is forward rr_motor – Right rear motor value (-1 to 1); 1 is forward lf_motor – Left front motor value (-1 to 1); 1 is forward rf_motor – Right front motor value (-1 to 1); 1 is forward lr_angle – Left rear motor angle in degrees (0 to 360 measured clockwise from forward position) rr_angle – Right rear motor angle in degrees (0 to 360 measured clockwise from forward position) lf_angle – Left front motor angle in degrees (0 to 360 measured clockwise from forward position) rf_angle – Right front motor angle in degrees (0 to 360 measured clockwise from forward position) x_wheelbase – The distance in feet between right and left wheels. y_wheelbase – The distance in feet between forward and rear wheels. speed – Speed of robot in feet per second (see above) deadzone – A function that adjusts the output of the motor (see linear_deadzone()) Speed of robot in x (ft/s), Speed of robot in y (ft/s), clockwise rotation of robot (radians/s)
pyfrc.physics.drivetrains.linear_deadzone(deadzone)[source]

Real motors won’t actually move unless you give them some minimum amount of input. This computes an output speed for a motor and causes it to ‘not move’ if the input isn’t high enough. Additionally, the output is adjusted linearly to compensate.

Example: For a deadzone of 0.2:

• Input of 0.0 will result in 0.0
• Input of 0.2 will result in 0.0
• Input of 0.3 will result in ~0.12
• Input of 1.0 will result in 1.0

This returns a function that computes the deadzone. You should pass the returned function to one of the drivetrain simulation functions as the deadzone parameter.

Parameters: motor_input – The motor input (between -1 and 1) deadzone (float) – Minimum input required for the motor to move (between 0 and 1) Callable[[float], float]
pyfrc.physics.drivetrains.mecanum_drivetrain(lr_motor, rr_motor, lf_motor, rf_motor, x_wheelbase=2, y_wheelbase=3, speed=5, deadzone=None)[source]

Deprecated since version 2018.2.0: Use MecanumDrivetrain instead

pyfrc.physics.drivetrains.two_motor_drivetrain(l_motor, r_motor, x_wheelbase=2, speed=5, deadzone=None)[source]

Deprecated since version 2018.2.0: Use TwoMotorDrivetrain instead

## Motor configurations¶

pyfrc.physics.motor_cfgs.MOTOR_CFG_775PRO = MotorModelConfig(name='775Pro', nominalVoltage=<Quantity(12, 'volt')>, freeSpeed=<Quantity(18730, 'counts_per_minute')>, freeCurrent=<Quantity(0.7, 'ampere')>, stallTorque=<Quantity(0.71, 'N_m')>, stallCurrent=<Quantity(134, 'ampere')>)

Motor configuration for 775 Pro

pyfrc.physics.motor_cfgs.MOTOR_CFG_775_125 = MotorModelConfig(name='RS775-125', nominalVoltage=<Quantity(12, 'volt')>, freeSpeed=<Quantity(5800, 'counts_per_minute')>, freeCurrent=<Quantity(1.6, 'ampere')>, stallTorque=<Quantity(0.28, 'N_m')>, stallCurrent=<Quantity(18.0, 'ampere')>)

Motor configuration for Andymark RS 775-125

pyfrc.physics.motor_cfgs.MOTOR_CFG_AM_9015 = MotorModelConfig(name='AM-9015', nominalVoltage=<Quantity(12, 'volt')>, freeSpeed=<Quantity(14270, 'counts_per_minute')>, freeCurrent=<Quantity(3.7, 'ampere')>, stallTorque=<Quantity(0.36, 'N_m')>, stallCurrent=<Quantity(71.0, 'ampere')>)

Motor configuration for Andymark 9015

pyfrc.physics.motor_cfgs.MOTOR_CFG_BAG = MotorModelConfig(name='Bag', nominalVoltage=<Quantity(12, 'volt')>, freeSpeed=<Quantity(13180, 'counts_per_minute')>, freeCurrent=<Quantity(1.8, 'ampere')>, stallTorque=<Quantity(0.43, 'N_m')>, stallCurrent=<Quantity(53.0, 'ampere')>)

Motor configuration for Bag Motor

pyfrc.physics.motor_cfgs.MOTOR_CFG_BB_RS550 = MotorModelConfig(name='RS550', nominalVoltage=<Quantity(12, 'volt')>, freeSpeed=<Quantity(19000, 'counts_per_minute')>, freeCurrent=<Quantity(0.4, 'ampere')>, stallTorque=<Quantity(0.38, 'N_m')>, stallCurrent=<Quantity(84.0, 'ampere')>)

Motor configuration for Banebots RS 550

pyfrc.physics.motor_cfgs.MOTOR_CFG_BB_RS775 = MotorModelConfig(name='RS775', nominalVoltage=<Quantity(12, 'volt')>, freeSpeed=<Quantity(13050, 'counts_per_minute')>, freeCurrent=<Quantity(2.7, 'ampere')>, stallTorque=<Quantity(0.72, 'N_m')>, stallCurrent=<Quantity(97.0, 'ampere')>)

Motor configuration for Banebots RS 775

pyfrc.physics.motor_cfgs.MOTOR_CFG_CIM = MotorModelConfig(name='CIM', nominalVoltage=<Quantity(12, 'volt')>, freeSpeed=<Quantity(5310, 'counts_per_minute')>, freeCurrent=<Quantity(2.7, 'ampere')>, stallTorque=<Quantity(2.42, 'N_m')>, stallCurrent=<Quantity(133, 'ampere')>)

Motor configuration for CIM

pyfrc.physics.motor_cfgs.MOTOR_CFG_MINI_CIM = MotorModelConfig(name='MiniCIM', nominalVoltage=<Quantity(12, 'volt')>, freeSpeed=<Quantity(5840, 'counts_per_minute')>, freeCurrent=<Quantity(3.0, 'ampere')>, stallTorque=<Quantity(1.41, 'N_m')>, stallCurrent=<Quantity(89.0, 'ampere')>)

Motor configuration for Mini CIM

class pyfrc.physics.motor_cfgs.MotorModelConfig

Configuration parameters useful for simulating a motor. Typically these parameters can be obtained from the manufacturer via a data sheet or other specification.

RobotPy contains MotorModelConfig objects for many motors that are commonly used in FRC. If you find that we’re missing a motor you care about, please file a bug report and let us know!

Note

The motor configurations that come with pyfrc are defined using the pint units library. See Unit conversions

Create new instance of MotorModelConfig(name, nominalVoltage, freeSpeed, freeCurrent, stallTorque, stallCurrent)

freeCurrent

freeSpeed

No-load motor speed (1 / [time])

name

Descriptive name of motor

nominalVoltage

Nominal voltage for the motor

stallCurrent

Stall current

stallTorque

Stall torque ([length]**2 * [mass] / [time]**2)

## Motion support¶

class pyfrc.physics.motion.LinearMotion(name, motor_ft_per_sec, ticks_per_feet, max_position=None, min_position=0)[source]

Helper for simulating motion involving a encoder directly coupled to a motor.

Here’s an example that shows a linear motion of 6ft at 2 ft/s with a 360-count-per-ft encoder, coupled to a PWM motor on port 0 and the first encoder object:

from pyfrc.physics import motion

class PhysicsEngine:

def __init__(self, physics_controller):
self.motion = pyfrc.physics.motion.LinearMotion('Motion', 2, 360, 6)

def update_sim(self, hal_data, now, tm_diff):

motor_value = hal_data['pwm'][0]['value']
hal_data['encoder'][0]['value'] = self.motion.compute(motor_value)


New in version 2018.3.0.

Parameters: name (str) – Name of motion, shown in pyfrc simulation UI motor_ft_per_sec (float) – Motor travel in feet per second (or whatever units you want) ticks_per_feet (int) – Number of encoder ticks per feet max_position (Optional[float]) – Maximum position that this motion travels to min_position (Optional[float]) – Minimum position that this motion travels to
position_ft = 0

Current computed position of motion, in feet

position_ticks = 0

Current computed position of motion (encoder ticks)

## Tank drive model support¶

New in version 2018.4.0.

Note

The equations used in our TankModel is derived from Noah Gleason and Eli Barnett’s motor characterization whitepaper. It is recommended that users of this model read the paper so they can more fully understand how this works.

In the interest of making progress, this API may receive backwards-incompatible changes before the start of the 2019 FRC season.

class pyfrc.physics.tankmodel.MotorModel(motor_config, kv, ka, vintercept)[source]

Motor model used by the TankModel. You should not need to create this object if you’re using the TankModel class.

Parameters: motor_config (MotorModelConfig) – The specification data for your motor kv (tm_kv) – Computed kv for your robot ka (tm_ka) – Computed ka for your robot vintercept (volt) – The minimum voltage required to generate enough torque to overcome steady-state friction (see the paper for more details)
acceleration = None

Current computed acceleration (in ft/s^2)

compute(motor_pct, tm_diff)[source]
Parameters: motor_pct (float) – Percentage of power for motor in range [1..-1] tm_diff (float) – Time elapsed since this function was last called float velocity
position = None

Current computed position (in ft)

velocity = None

Current computed velocity (in ft/s)

class pyfrc.physics.tankmodel.TankModel(motor_config, robot_mass, x_wheelbase, robot_width, robot_length, l_kv, l_ka, l_vi, r_kv, r_ka, r_vi, timestep=<Quantity(5, 'millisecond')>)[source]

This is a model of a FRC tankdrive-style drivetrain that will provide vaguely realistic motion for the simulator.

This drivetrain model makes a number of assumptions:

• N motors per side
• Constant gearing
• Motors are geared together
• Wheels do not ‘slip’ on the ground
• Each side of the robot moves in unison

There are two ways to construct this model. You can use the theoretical model via TankModel.theory() and provide robot parameters such as gearing, total mass, etc.

Alternatively, if you measure kv, ka, and vintercept as detailed in the paper mentioned above, you can plug those values in directly instead using the TankModel constructor instead. For more information about measuring your own values, see the paper and this thread on ChiefDelphi.

Note

You must specify the You can use whatever units you would like to specify the input parameter for your robot, RobotPy will convert them all to the correct units for computation.

Output units for velocity and acceleration are in ft/s and ft/s^2

Example usage for a 90lb robot with 2 CIM motors on each side with 6 inch wheels:

from pyfrc.physics import motors, tankmodel
from pyfrc.physics.units import units

class PhysicsEngine:

def __init__(self, physics_controller):
self.physics_controller = physics_controller

self.drivetrain = tankmodel.TankModel.theory(motors.MOTOR_CFG_CIM_IMP,
robot_mass=90 * units.lbs,
gearing=10.71, nmotors=2,
x_wheelbase=2.0*feet,
wheel_diameter=6*units.inch)

def update_sim(self, hal_data, now, tm_diff):
# TODO: get motor values from hal_data
velocity, rotation = self.drivetrain.get_vector(l_motor, r_motor)
self.physics_controller.drive(velocity, rotation, tm_diff)

# optional: compute encoder
# l_encoder = self.drivetrain.l_position * ENCODER_TICKS_PER_FT
# r_encoder = self.drivetrain.r_position * ENCODER_TICKS_PER_FT


Use the constructor if you have measured kv, ka, and Vintercept for your robot. Use the theory() function if you haven’t.

Vintercept is the minimum voltage required to generate enough torque to overcome steady-state friction (see the paper for more details).

The robot width/length is used to compute the moment of inertia of the robot. Don’t forget about bumpers!

Parameters: motor_config (MotorModelConfig) – Motor specification robot_mass (Quantity) – Mass of robot x_wheelbase (Quantity) – Wheelbase of the robot robot_width (Quantity) – Width of the robot robot_length (Quantity) – Length of the robot l_kv (Quantity) – Left side kv l_ka (Quantity) – Left side ka l_vi (volt) – Left side Vintercept r_kv (Quantity) – Right side kv r_ka (Quantity) – Right side ka r_vi (volt) – Right side Vintercept timestep (Quantity) – Model computation timestep
get_distance(l_motor, r_motor, tm_diff)[source]

Given motor values and the amount of time elapsed since this was last called, retrieves the x,y,angle that the robot has moved. Pass these values to PhysicsInterface.distance_drive().

To update your encoders, use the l_position and r_position attributes of this object.

Parameters: l_motor (float) – Left motor value (-1 to 1); -1 is forward r_motor (float) – Right motor value (-1 to 1); 1 is forward tm_diff (float) – Elapsed time since last call to this function Tuple[float, float] x travel, y travel, angle turned (radians)

Note

If you are using more than 2 motors, it is assumed that all motors on each side are set to the same speed. Only pass in one of the values from each side

inertia

The model computes a moment of inertia for your robot based on the given mass and robot width/length. If you wish to use a different moment of inertia, set this property after constructing the object

Units are [mass] * [length] ** 2

l_position

The linear position of the left side wheel (in feet)

l_velocity

The velocity of the left side (in ft/s)

r_position

The linear position of the right side wheel (in feet)

r_velocity

The velocity of the right side (in ft/s)

classmethod theory(motor_config, robot_mass, gearing, nmotors=1, x_wheelbase=<Quantity(21.0, 'inch')>, robot_width=<Quantity(27.5, 'inch')>, robot_length=<Quantity(36.5, 'inch')>, wheel_diameter=<Quantity(6, 'inch')>, vintercept=<Quantity(1.3, 'volt')>, timestep=<Quantity(5, 'millisecond')>)[source]

Use this to create the drivetrain model when you haven’t measured kv and ka for your robot.

Parameters: motor_config (MotorModelConfig) – Specifications for your motor robot_mass (Quantity) – Mass of the robot gearing (float) – Gear ratio .. so for a 10.74:1 ratio, you would pass 10.74 nmotors (int) – Number of motors per side x_wheelbase (Quantity) – Wheelbase of the robot robot_width (Quantity) – Width of the robot robot_length (Quantity) – Length of the robot wheel_diameter (Quantity) – Diameter of the wheel vintercept (volt) – The minimum voltage required to generate enough torque to overcome steady-state friction (see the paper for more details) timestep_ms – Model computation timestep

Computation of kv and ka are done as follows:

• $$\omega_{free}$$ is the free speed of the motor
• $$\tau_{stall}$$ is the stall torque of the motor
• $$n$$ is the number of drive motors
• $$m_{robot}$$ is the mass of the robot
• $$d_{wheels}$$ is the diameter of the robot’s wheels
• $$r_{gearing}$$ is the total gear reduction between the motors and the wheels
• $$V_{max}$$ is the nominal max voltage of the motor
\begin{align}\begin{aligned}velocity_{max} = \frac{\omega_{free} \cdot \pi \cdot d_{wheels} }{r_{gearing}}\\acceleration_{max} = \frac{2 \cdot n \cdot \tau_{stall} \cdot r_{gearing} }{d_{wheels} \cdot m_{robot}}\\k_{v} = \frac{V_{max}}{velocity_{max}}\\k_{a} = \frac{V_{max}}{acceleration_{max}}\end{aligned}\end{align}

## Unit conversions¶

pyfrc uses the pint library in some places for representing physical quantities to allow users to specify the physical parameters of their robot in a natural and non-ambiguous way. For example, to represent 5 feet:

from pyfrc.physics.units import units

five_feet = 5 * units.feet


Unfortunately, actually using the quantities is a huge performance hit, so we don’t use them to perform actual physics computations. Instead, pyfrc uses them to convert to known units, then performs computations using the magnitude of the quantity.

pyfrc defines the following custom units:

• counts_per_minute or cpm: Counts per minute, which should be used instead of pint’s predefined rpm (because it is rad/s). Used to represent motor free speed
• N_m: Shorthand for N-m or newton-meter. Used for motor torque.
• tm_ka: The kA value used in the tankmodel (uses imperial units)
• tm_kv: The kV value used in the tankmodel (uses imperial units)

Refer to the pint documentation for more information on how to use pint.

pyfrc.physics.units.units = <pint.registry.UnitRegistry object>

All units that pyfrc uses are defined in this global object

## Custom labels¶

If you write data to the ‘custom’ key of the hal_data dictionary, the data will be shown on the pyfrc simulation UI. The key will be the label for the data, and the value will be rendered as text inside the box:

# first time you set the data
hal_data.setdefault('custom', {})['My Label'] = '---'

# All other times
hal_data['custom']['My Label'] = 1


New in version 2018.3.0.

## Custom drawing¶

pyfrc.sim.get_user_renderer()[source]

This retrieves an object that can be used to draw on the simulated field when running the robot in simulation.

Returns: None if no renderers are available, else a UserRenderer object
class pyfrc.sim.field.user_renderer.UserRenderer[source]
draw_line(line_pts, color='#ff0000', robot_coordinates=False, relative_to_first=False, arrow=True, scale=(1, 1), **kwargs)[source]
Parameters: line_pts – A list of (x,y) pairs to draw. (x,y) are in field units which are measured in feet color – The color of the line, expressed as a 6-digit hex color robot_coordinates – If True, the pts will be adjusted such that the first point starts at the center of the robot and that x and y coordinates are rotated according to the robot’s current heading. If a tuple, then the pts are adjusted relative to the robot center AND the x,y in the tuple relative_to_first – If True, the points will be adjusted such that the first point is considered to be (0,0) arrow – If True, draw the line with an arrow at the end scale – Multiply all points by this (x,y) tuple kwargs – Keyword options to pass to tkinter.create_line
draw_pathfinder_trajectory(trajectory, color='#ff0000', offset=None, scale=(1, 1), show_dt=False, dt_offset=0.0, **kwargs)[source]

Special helper function for drawing trajectories generated by robotpy-pathfinder

Parameters: trajectory – A list of pathfinder segment objects offset – If specified, should be x/y tuple to add to the path relative to the robot coordinates scale – Multiply all points by this (x,y) tuple show_dt – draw text every N seconds along path, or False dt_offset – add this to each dt shown kwargs – Keyword options to pass to tkinter.create_line
draw_text(text, pt, color='#000000', fontSize=10, robot_coordinates=False, scale=(1, 1), **kwargs)[source]
Parameters: text – Text to render pt – A tuple of (x,y) in field units (which are measured in feet) color – The color of the text, expressed as a 6-digit hex color robot_coordinates – If True, the pt will be adjusted such that the point starts at the center of the robot and that x and y coordinates are rotated according to the robot’s current heading. If a tuple, then the pt is adjusted relative to the robot center AND the x,y in the tuple arrow – If True, draw the line with an arrow at the end scale – Multiply all points by this (x,y) tuple kwargs – Keyword options to pass to tkinter.create_text

## Camera ‘simulator’¶

The ‘vision simulator’ provides objects that assist in modeling inputs from a camera processing system.

class pyfrc.physics.visionsim.VisionSim(targets, camera_fov, view_dst_start, view_dst_end, data_frequency=15, data_lag=0.05, physics_controller=None)[source]

This helper object is designed to help you simulate input from a vision system. The algorithm is a very simple approximation and has some weaknesses, but it should be a good start and general enough to work for many different usages.

There are a few assumptions that this makes:

• Your camera code sends new data at a constant frequency
• The data from the camera lags behind at a fixed latency
• If the camera is too close, the target cannot be seen
• If the camera is too far, the target cannot be seen
• You can only ‘see’ the target when the ‘front’ of the robot is around particular angles to the target
• The camera is in the center of your robot (this simplifies some things, maybe fix this in the future…)

To use this, create an instance in your physics simulator:

targets = [
VisionSim.Target(...)
]


Then call the compute() method from your update_sim method whenever your camera processing is enabled:

# in physics engine update_sim()
x, y, angle = self.physics_controller.get_position()

if self.camera_enabled:
data = self.vision_sim.compute(now, x, y, angle)
if data is not None:
self.nt.putNumberArray('/camera/target', data[0])
else:
self.vision_sim.dont_compute()


Note

There is a working example in the examples repository you can use to try this functionality out

There are a lot of constructor parameters:

Parameters: targets – List of target positions (x, y) on field in feet view_angle_start – Center angle that the robot can ‘see’ the target from (in degrees) camera_fov – Field of view of camera (in degrees) view_dst_start – If the robot is closer than this, the target cannot be seen view_dst_end – If the robot is farther than this, the target cannot be seen data_frequency – How often the camera transmits new coordinates data_lag – How long it takes for the camera data to be processed and make it to the robot physics_controller – If set, will draw target information in UI
Target

alias of VisionSimTarget

compute(now, x, y, angle)[source]

Call this when vision processing should be enabled

Parameters: now – The value passed to update_sim x – Returned from physics_controller.get_position y – Returned from physics_controller.get_position angle – Returned from physics_controller.get_position None or list of tuples of (found=0 or 1, capture_time, offset_degrees, distance). The tuples are ordered by absolute offset from the target. If a list is returned, it is guaranteed to have at least one element in it. Note: If your vision targeting doesn’t have the ability to focus on multiple targets, then you should ignore the other elements.
dont_compute()[source]

Call this when vision processing should be disabled

get_immediate_distance()[source]

Use this data to feed to a sensor that is mostly instantaneous (such as an ultrasonic sensor).

Note

You must call compute() first.

class pyfrc.physics.visionsim.VisionSimTarget(x, y, view_angle_start, view_angle_end)[source]

Target object that you pass the to the constructor of VisionSim

Parameters: x – Target x position y – Target y position view_angle_start – view_angle_end – clockwise from start angle

View angle is defined in degrees from 0 to 360, with 0 = east, increasing clockwise. So, if the robot could only see the target from the south east side, you would use a view angle of start=0, end=90.