def __init__(self, randomize=False, network=False, verbose=False):
"""Initialize
Creates the environment, viewer, and (Phantom Omni) controller objects
required to run the human data capture simulation.
Arguments:
randomize: Determines if the test article gates will be randomized.
(Default: False)
network: Determines if the omni connection is local or networked.
(Default: False)
verbose: Determines the level out debug output generated.
(Default: False)
"""
# Generate the XODE file
XODE_FILENAME = 'model' # .xode is appended automatically
print('>>> Generating world model')
if os.path.exists('./' + XODE_FILENAME + '.xode'):
os.remove('./' + XODE_FILENAME + '.xode')
xode_model = TrainingSimWorld(
name=XODE_FILENAME, randomize_test_article=randomize)
xode_model.generate()
# Start environment
print('>>> Starting environment')
self.env = EnvironmentInterface(
xode_filename='./' + XODE_FILENAME + '.xode',
realtime=False,
verbose=verbose,
gravity=constants.G_ENVIRONMENT_GRAVITY)
# Set up all grouped bodies in the environment
self.env.groups = {
'pointer': ['tooltip', 'stick'],
}
# Start viewer
print('>>> Starting viewer')
self.viewer = ViewerInterface(verbose=verbose)
self.viewer.start()
# Start controller
print('>>> Starting Phantom Omni interface')
self.omni = PhantomOmniInterface()
if network:
ip = raw_input('<<< Enter host ip: ')
port = int(raw_input('<<< Enter tcp port: '))
else:
ip = constants.G_IP_LOCAL_DEFAULT
port = constants.G_PORT_DEFAULT
# Try to connect to the Phantom Omni controller
self.omni.connect(ip, port)
self.saved_data = np.array([])
return
def __init__(self, randomize=False, network=False, verbose=False):
"""Initialize
Creates the environment, viewer, and (Phantom Omni) controller objects
required to run the human data capture simulation.
Arguments:
randomize: Determines if the test article gates will be randomized.
(Default: False)
network: Determines if the omni connection is local or networked.
(Default: False)
verbose: Determines the level out debug output generated.
(Default: False)
"""
# Generate the XODE file
XODE_FILENAME = 'model' # .xode is appended automatically
print('>>> Generating world model')
if os.path.exists('./'+XODE_FILENAME+'.xode'):
os.remove('./'+XODE_FILENAME+'.xode')
xode_model = TrainingSimWorld(
name=XODE_FILENAME,
randomize_test_article=randomize
)
xode_model.generate()
# Start environment
print('>>> Starting environment')
self.env = EnvironmentInterface(
xode_filename='./'+XODE_FILENAME+'.xode',
realtime=False,
verbose=verbose,
gravity=constants.G_ENVIRONMENT_GRAVITY
)
# Set up all grouped bodies in the environment
self.env.groups = {
'pointer': ['tooltip', 'stick'],
}
# Start viewer
print('>>> Starting viewer')
self.viewer = ViewerInterface(verbose=verbose)
self.viewer.start()
# Start controller
print('>>> Starting Phantom Omni interface')
self.omni = PhantomOmniInterface()
if network:
ip = raw_input('<<< Enter host ip: ')
port = int(raw_input('<<< Enter tcp port: '))
else:
ip = constants.G_IP_LOCAL_DEFAULT
port = constants.G_PORT_DEFAULT
# Try to connect to the Phantom Omni controller
self.omni.connect(ip, port)
self.saved_data = np.array([])
return
class TrainingSimulation(object):
"""TrainingSimulation class
Responsible for initialization of all children objects such as the
Open Dynamics Engine environment, Phantom Omni controller, and OpenGL
viewer. Starts the main event loop with real-time constraints.
Attributes:
env: The Open Dynamics Engine environment.
omni: The Phantom Omni robotic controller connection.
viewer: The OpenGL viewer for the ODE environment.
saved_data: A list of tuples containing data from the simulation.
Methods:
start: Begins the main event loop.
"""
env = None
omni = None
viewer = None
saved_data = None
def __init__(self, randomize=False, network=False, verbose=False):
"""Initialize
Creates the environment, viewer, and (Phantom Omni) controller objects
required to run the human data capture simulation.
Arguments:
randomize: Determines if the test article gates will be randomized.
(Default: False)
network: Determines if the omni connection is local or networked.
(Default: False)
verbose: Determines the level out debug output generated.
(Default: False)
"""
# Generate the XODE file
XODE_FILENAME = 'model' # .xode is appended automatically
print('>>> Generating world model')
if os.path.exists('./'+XODE_FILENAME+'.xode'):
os.remove('./'+XODE_FILENAME+'.xode')
xode_model = TrainingSimWorld(
name=XODE_FILENAME,
randomize_test_article=randomize
)
xode_model.generate()
# Start environment
print('>>> Starting environment')
self.env = EnvironmentInterface(
xode_filename='./'+XODE_FILENAME+'.xode',
realtime=False,
verbose=verbose,
gravity=constants.G_ENVIRONMENT_GRAVITY
)
# Set up all grouped bodies in the environment
self.env.groups = {
'pointer': ['tooltip', 'stick'],
}
# Start viewer
print('>>> Starting viewer')
self.viewer = ViewerInterface(verbose=verbose)
self.viewer.start()
# Start controller
print('>>> Starting Phantom Omni interface')
self.omni = PhantomOmniInterface()
if network:
ip = raw_input('<<< Enter host ip: ')
port = int(raw_input('<<< Enter tcp port: '))
else:
ip = constants.G_IP_LOCAL_DEFAULT
port = constants.G_PORT_DEFAULT
# Try to connect to the Phantom Omni controller
self.omni.connect(ip, port)
self.saved_data = np.array([])
return
def start(self, fps):
"""Start
Begin the continuous event loop for the simulation. This event loop
can be exited using the ctrl+c keyboard interrupt. Real-time
constraints are enforced.
Arguments:
fps: The value of frames per second of the simulation.
"""
paused = False
stopped = False
# Define the simulation frame rate
t = 0.0
dt = 1.0 / float(fps) # [s]
# Keep track of time overshoot in the case that simulation time must be
# increased in order to maintain real-time constraints
t_overshoot = 0.0
while not stopped:
t_start = time.time()
# If the last calculation took too long, catch up
dt_warped = dt + t_overshoot
self.env.set_dt(dt_warped)
self.omni.set_dt(dt_warped)
# Determine if the viewer is stopped. Then we can quit
if self.viewer.is_dead:
break
if paused:
self.env.step(paused=True)
continue
# Populate the controller with the most up-to-date data
self.omni.update()
# Determine the input data to record
sample_input = np.array([
t,
]).flatten()
# Capture the gate position at each time step
for gate_idx in range(constants.G_NUM_GATES):
gate_pos = np.array(self.env.get_body_pos('gate%d'%gate_idx)).flatten()
gate_rot = constants.G_GATE_NORM_ROT[gate_idx]
sample_input = np.hstack((sample_input, gate_pos, gate_rot))
# Determine the output data to record
sample_output = np.array([
self.env.get_body_pos('tooltip'),
]).flatten()
# Join the sample input/output
data_sample = np.hstack((sample_input, sample_output))
# Save the data
self.save_data(data_sample)
# Get the updated linear/angular velocities of the tooltip
linear_vel = self.omni.get_linear_vel()
angular_vel = self.omni.get_angular_vel()
# Set the linear and angular velocities of the simulation
self.env.set_group_linear_vel('pointer', linear_vel)
self.env.set_group_angular_vel('pointer', angular_vel)
# Step through the world by 1 time frame
self.env.step()
t += dt_warped
# Determine the difference in virtual vs actual time
t_warped = dt - (time.time() - t_start)
# Attempt to enforce real-time constraints
if t_warped >= 0.0:
# The calculation took less time than the virtual time. Sleep
# the rest off
time.sleep(t_warped)
t_overshoot = 0.0
else:
# The calculation took more time than the virtual time. We need
# to catch up with the virtual time on the next time step
t_overshoot = -t_warped
return
def save_data(self, data_sample):
"""Save Data
Saves a sample of data into the simulation's saved data array.
Arguments:
data_sample: A sample of input/output data from a single time step.
"""
if len(self.saved_data):
self.saved_data = np.vstack((self.saved_data, data_sample))
else:
self.saved_data = data_sample.copy()
return
def __del__(self):
"""Delete (del)
Attempts to shut down any active engines and kills off spawned
class objects.
"""
# Kill the OpenGL viewer process
if self.viewer is not None:
self.viewer.stop()
del self.viewer
# Kill the ODE environment objects
if self.env is not None:
self.env.stop()
del self.env
# Kill the Phantom Omni controller process
if self.omni is not None:
self.omni.disconnect()
del self.omni
return
