def __init__(self, direct=False):
self.robot_config = arm.Config(use_cython=False, hand_attached=True)
# Timestap is fixed and specified in .ttt file
self.dt = 0.005
self.direct = direct
if direct:
self.interface = VREP(robot_config=self.robot_config, dt=self.dt)
self.interface.connect()
else:
self.interface = None
self.requested_reset_q = None
self.requested_target_shadow_q = None
self.close_requested = False
"""
Move the UR5 VREP arm to a target position.
The simulation ends after 1.5 simulated seconds, and the
trajectory of the end-effector is plotted in 3D.
"""
import numpy as np
import traceback
from abr_control.arms import jaco2 as arm
from abr_control.controllers import OSC, Damping, signals
from abr_control.interfaces import VREP
# initialize our robot config for the jaco2
robot_config = arm.Config(use_cython=True, hand_attached=False)
# damp the movements of the arm
damping = Damping(robot_config, kv=10)
# instantiate controller
ctrlr = OSC(
robot_config,
kp=200,
null_controllers=[damping],
vmax=[0.5, 0], # [m/s, rad/s]
# control (x, y, z) out of [x, y, z, alpha, beta, gamma]
ctrlr_dof=[True, True, True, False, False, False])
# create our adaptive controller
adapt = signals.DynamicsAdaptation(
n_neurons=1000,
n_ensembles=5,
n_input=2, # we apply adaptation on the most heavily stressed joints
Move the UR5 CoppeliaSim arm to a target position.
The simulation ends after 1500 time steps, and the
trajectory of the end-effector is plotted in 3D.
"""
import traceback
import numpy as np
# from abr_control.arms import ur5 as arm
from abr_control.arms import jaco2 as arm
# from abr_control.arms import onejoint as arm
from abr_control.controllers import Sliding
from abr_control.interfaces import CoppeliaSim
# initialize our robot config
robot_config = arm.Config()
# instantiate controller
# NOTE: These values are non-optimal
ctrlr = Sliding(robot_config, kd=10.0, lamb=30.00)
# create our CoppeliaSim interface
interface = CoppeliaSim(robot_config, dt=0.001)
interface.connect()
# set up lists for tracking data
ee_track = []
target_track = []
try:
# get the end-effector's initial position
""" A basic script for connecting and moving the arm to a target configuration in joint space, offset from its starting position. The simulation simulates 1500 time steps and then plots the results. """ import numpy as np import traceback # from abr_control.arms import ur5 as arm from abr_control.arms import jaco2 as arm # from abr_control.arms import onelink as arm from abr_control.controllers import Joint from abr_control.interfaces import VREP # initialize our robot config robot_config = arm.Config(use_cython=True) # if using the Jaco 2 arm with the hand attached, use the following instead: # robot_config = arm.Config(use_cython=True, hand_attached=False) # instantiate the REACH controller for the jaco2 robot ctrlr = Joint(robot_config, kp=50) # create interface and connect interface = VREP(robot_config=robot_config, dt=.005) interface.connect() # make the target an offset of the current configuration feedback = interface.get_feedback() target = feedback['q'] + np.ones(robot_config.N_JOINTS) * .3 # For VREP files that have a shadow arm to show the target configuration
from download_examples_db import check_exists as examples_db
from abr_analyze.paths import figures_dir
from abr_analyze.plotting import Draw3dData, DrawArm
examples_db()
# the number of samples to interpolate our data to, set to None for no
# interpolation
interpolated_samples = 100
# list our tests and their relevant save locations
db_name = "abr_analyze_examples"
test = "test_1"
baseline = "baseline_1"
# instantiate our robot config
robot_config = jaco2.Config()
# Instantiate our arm drawing module
draw_arm = DrawArm(
db_name=db_name,
robot_config=robot_config,
interpolated_samples=interpolated_samples,
)
# instantiate our generic trajectory drawing module
draw_3d = Draw3dData(db_name=db_name,
interpolated_samples=interpolated_samples)
plt.figure()
ax = plt.subplot(111, projection="3d")
