README.md

sorotraj

Generate trajectories for soft robots from yaml files (accompanies the Ctrl-P project and SoMo simulation framework)

Installation

Install the release version

This package is on pypi, so anyone can install it with pip: pip install sorotraj

Install the most-recent development version

1. Clone the package from the github repo
2. Navigate into the main folder
3. pip install .

Usage

Minimal Example

import sorotraj

file_to_use = 'traj_setup/setpoint_traj_demo.yaml'

traj = sorotraj.TrajBuilder()
traj.load_traj_def(file_to_use)
trajectory = traj.get_trajectory()
interp = sorotraj.Interpolator(trajectory)
actuation_fn = interp.get_interp_function(
                num_reps=1,
                speed_factor=2.0,
                invert_direction=False)
print(actuation_fn(2.155))

Check out the examples folder for more detailed usage examples

Set Up Trajectories:

Trajectories are made of three parts:

1. main: used in a looping trajectory
2. prefix: happens once before the main part
3. suffix: happens once after the main part

Here's an example of what that might look like defined in a yaml file:

config:
    setpoints:
        # [time, finger1, finger2, n/c, n/c]
        main:
            - [0.0,   10, 12, 14,  16]
            - [1.0,    20, 0, 0,  0]
            - [2.0,   0, 20, 0,  0]
            - [3.0,     0, 0, 20, 0]
            - [4.0,     0, 0, 0, 20]
            - [5.0,    10, 12, 14, 16]

        prefix:
            - [0.000,   0, 0, 0,  0]
            - [1.0,    10, 12, 14,  16]

        suffix:
            - [2.000,   10, 12, 14,  16]
            - [3.0,  0, 0, 0,  0]

There are currently three types of ways to generate the main part of a trajectory:

1. direct: You enter waypoints directly
   • Define waypoints as a list of lists of the form: [time in sec], [a_1], [a_2], ..., [a_n]
2. interp: Interpolate between waypoints
   • Define waypoints as a list of lists of the form: [time in sec], [a_1], [a_2], ..., [a_n]
   • Set a few more parameters:
     • interp_type: (string) The type of interpolation to use. right now types include: 'linear', 'cubic', and 'none'
     • subsample_num: (int) The total number of subsamples over the whole trajectory
3. waveform: Generate waveforms (very basic, still just in-phase waveforms across all channels)
   • Set up the waveform:
     • waveform_type: (string) Types include: square-sampled, square, sin, cos-up, cos-down, triangle, sawtooth-f, and sawtooth-r
     • waveform_freq: (float) Frequency in Hertz
     • waveform_max: (float) A list of the maximum values for the waveform, in the form: [20, 0, 15, 5]
     • waveform_min: (float) A list of the minimum values for the waveform, in the form: [0, 20, 0, 15]
   • Set a few more parameters:
     • subsample_num: (int) The total number of subsamples over the whole trajectory
     • num_cycles: (int) The number of cycles of the waveform
     • channels: (bool) Flags to turn channels on and off. A list of the form: [1,1,0,0]

Convert Trajectories Line-by-Line

Check out the build_convert_trajectories.py example.

1. Set up a conversion function
   • Inputs: one original trajectory line (list)
   • Outputs: one new trajectory line (list)
2. Load the trajectory like normal
   • traj.load_traj_def(file_to_use)
3. Convert the trajectory by passing the conversion function
   • traj.convert_traj(conversion_function)
4. This conversion overwrites the original trajectory. Now you can save it like normal
   • traj.save_traj(file_to_save)
5. Convert the trajectory definition by passing the conversion function
   • traj.convert_definition(conversion_function)
6. This conversion overwrites the original trajectory definition and reguilds the trajectory. Now you can save the definition like normal
   • traj.save_definition(file_to_save)

Build an interpolator

interp = sorotraj.Interpolator(trajectory)

• trajectory: A trajectory object generated by sorotraj.TrajBuilder

actuation_fn, final_time = interp.get_traj_function(
                num_reps=1,
                speed_factor=1.0,
                invert_direction=False)

• num_reps: (int, default=1) Number of times to repeat the main looping trajectory
  • Must be positive, nonzero
• speed_factor: (float, default=1.0) A speed multiplier that is applied to the main loop (but not the prefix or suffix)
  • Must be positive, nonzero
• invert_direction: (bool, default=False) Negate the whole trajectory (useful if actuators have different directionalities)
  • (bool): Negate all channels
  • (list of ints): Choose which channels to negate with a list of channel indices

cycle_fn = interp.get_cycle_function(
                num_reps=1,
                speed_factor=1.0,
                invert_direction=False)

• Same inputs as get_interp_function(), but returns a cycle function (returns the current cycle as a function of time)
• cycle_fn takes these values:
  • -2 = Prefix
  • -1 = Suffix
  • 0-N = Main loop index