def main():
"""
OUTPUT THE MINIMUM REQUIRED TIME FOR THE RNN PATH TO COMPLETE
"""
# Define generated data file name
generated_file = '../../results/generated/efficiency-test.dat'
# Collect path data from the file
generated_path = datastore.retrieve(generated_file)
# Calculate the indices of the end effector position columns
pos_start_col = constants.G_POS_IDX
pos_end_col = pos_start_col + constants.G_NUM_POS_DIMS
# Assume time to complete path is 1 second
dt = 1.0 / 60.0 # [s]
t_total = (1.0 / dt) * float(len(generated_path))
# Get the defined acceleration limit of the PA-10
a_limit_norm = constants.G_MAX_ACCEL
a_max_norm = 0.0
x_curr = np.array([0.0, 0.0, 0.0])
v_curr = np.array([0.0, 0.0, 0.0])
for idx in xrange(len(generated_path)-1):
# Get the tooltip position for this and the next sample
x_curr = pathutils.get_path_tooltip_pos(generated_path, idx)
x_next = pathutils.get_path_tooltip_pos(generated_path, idx+1)
# Calculate the velocity from current to next sample
v_next = (x_next - x_curr) / dt
# Calculate the acceleration from current to next sample
a = (v_next - v_curr) / dt
a_norm = np.linalg.norm(a)
# Store this acceleration if it's greater than the current max
if a_norm > a_max_norm:
a_max_norm = a_norm
# Set the current to the next velocity
v_curr = v_next.copy()
print('dt: %f [s]' % dt)
print('a_max: %f [m/s^2]' % a_max_norm)
# Calculate the gain factor needed to bring the max norm acceleration to
# the acceleration norm limit
a_gain = a_limit_norm / a_max_norm
print('gain_factor: %f' % a_gain)
return
def main():
"""
OUTPUT THE MINIMUM REQUIRED TIME FOR THE RNN PATH TO COMPLETE
"""
# Define generated data file name
generated_file = '../../results/generated/efficiency-test.dat'
# Collect path data from the file
generated_path = datastore.retrieve(generated_file)
# Calculate the indices of the end effector position columns
pos_start_col = constants.G_POS_IDX
pos_end_col = pos_start_col + constants.G_NUM_POS_DIMS
# Assume time to complete path is 1 second
dt = 1.0 / 60.0 # [s]
t_total = (1.0 / dt) * float(len(generated_path))
# Get the defined acceleration limit of the PA-10
a_limit_norm = constants.G_MAX_ACCEL
a_max_norm = 0.0
x_curr = np.array([0.0, 0.0, 0.0])
v_curr = np.array([0.0, 0.0, 0.0])
for idx in xrange(len(generated_path) - 1):
# Get the tooltip position for this and the next sample
x_curr = pathutils.get_path_tooltip_pos(generated_path, idx)
x_next = pathutils.get_path_tooltip_pos(generated_path, idx + 1)
# Calculate the velocity from current to next sample
v_next = (x_next - x_curr) / dt
# Calculate the acceleration from current to next sample
a = (v_next - v_curr) / dt
a_norm = np.linalg.norm(a)
# Store this acceleration if it's greater than the current max
if a_norm > a_max_norm:
a_max_norm = a_norm
# Set the current to the next velocity
v_curr = v_next.copy()
print('dt: %f [s]' % dt)
print('a_max: %f [m/s^2]' % a_max_norm)
# Calculate the gain factor needed to bring the max norm acceleration to
# the acceleration norm limit
a_gain = a_limit_norm / a_max_norm
print('gain_factor: %f' % a_gain)
return
def calculate_closest_approaches(path):
"""Calculate Closest Approaches
Given a standard SurgicalSim path, the closest approaches of that path to
each marker will be calculated.
Arguments:
path: A SurgicalSim formatted path.
Returns:
A list of distances of size N, where N is the number of markers.
"""
segments = pathutils._detect_segments(path)
distances = []
for gate_idx in np.arange(constants.G_NUM_GATES):
x_gate = pathutils.get_path_gate_pos(path, segments[gate_idx], gate_idx)
x_tooltip = pathutils.get_path_tooltip_pos(path, segments[gate_idx])
dist = np.sqrt((x_gate[0] - x_tooltip[0]) ** 2 + (x_gate[1] - x_tooltip[1]) ** 2 + (x_gate[2] - x_tooltip[2]) ** 2)
distances.append(dist)
return distances
def start(self, fps, fast_step=False):
"""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. [Hz]
Arguments:
fps: The value of frames per second of the simulation.
fast_step: If True, the ODE fast step algorithm will be used.
This is faster and requires less memory but is less accurate.
(Default: False)
"""
paused = False
stopped = False
# Define the total time for the tooltip traversal
t_total = 20.0
# Define the simulation frame rate
t = 0.0 # [s]
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
# Get the initial path position (center of gate7)
pos_start = self.env.get_body_pos('gate7') # [m]
# Get the first position of the PA10 at rest
pos_init = self.env.get_body_pos('tooltip') # [m]
# Calculate the new required joint angles of the PA10
#pa10_joint_angles = self.kinematics.calc_inverse_kinematics(pos_init, pos_start)
# TODO: Move the PA10 end-effector to the starting position along the path
# TODO: TEMP - Move the temporary end-effector pointer to the starting position
self.env.set_group_pos('pointer', pos_start)
# Generate long-term path from initial position
t_input = np.linspace(start=0.0, stop=1.0, num=t_total / dt)
t_input = np.reshape(t_input, (len(t_input), 1))
rnn_path = self.rnn.extrapolate(t_input, [pos_start], len(t_input) - 1)
# Add the initial condition point back onto the data
rnn_path = np.vstack((pos_start, rnn_path))
# Retrieve one set of standard gate position/orientation data
file_path = pathutils.list_data_files(constants.G_TRAINING_DATA_DIR)[0]
gate_data = datastore.retrieve(file_path)
gate_start_idx = constants.G_GATE_IDX
gate_end_idx = gate_start_idx + constants.G_NUM_GATE_INPUTS
# Reshape the gate positions data
gate_data = gate_data[0:1, gate_start_idx:gate_end_idx]
gate_data = np.tile(gate_data, (len(rnn_path), 1))
# Complete the rnn path data
rnn_path = np.hstack((t_input, gate_data, rnn_path))
# Save generated path for later examination
datastore.store(rnn_path, constants.G_RNN_STATIC_PATH_OUT)
# Define a variable to hold the final path (with real-time correction)
final_path = rnn_path[:-1].copy()
path_saved = False
# Detect all path segments between gates in the generated path
segments = pathutils._detect_segments(rnn_path)
path_idx = 0
x_path_offset = np.array([0.0, 0.0, 0.0]) # [m]
v_curr = np.array([0.0, 0.0, 0.0]) # [m/s]
a_max = constants.G_MAX_ACCEL # [m/s^2]
# Get the static table position
x_table = self.env.get_body_pos('table')
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)
# Determine if the viewer is stopped. Then we can quit
if self.viewer.is_dead:
break
# Pause the simulation if we are at the end
if path_idx == len(rnn_path) - 1 or paused:
self.env.step(paused=True, fast=fast_step)
# If we have really hit the end of the simulation, save/plot the path
if not paused and not path_saved:
# Save the final data to a file
datastore.store(final_path,
constants.G_RNN_DYNAMIC_PATH_OUT)
path_saved = True
continue
# Not a very elegant solution to pausing at the start, but it works
if t <= 1000.0:
self.env.step(paused=True, fast=fast_step)
t += dt_warped
continue
# Determine the current path segment
curr_segment_idx = 0
for segment_idx, segment_end in enumerate(segments):
if path_idx <= segment_end:
curr_segment_idx = segment_idx
break
x_curr = pathutils.get_path_tooltip_pos(rnn_path,
path_idx) + x_path_offset
x_next = pathutils.get_path_tooltip_pos(
rnn_path, path_idx + 1) + x_path_offset
# Get the expected gate position
x_gate_expected = pathutils.get_path_gate_pos(
rnn_path, segments[curr_segment_idx], curr_segment_idx)
# Get the actual gate position
x_gate_actual = self.env.get_body_pos('gate%d' % curr_segment_idx)
# Calculate the new position from change to new gate position
dx_gate = x_gate_actual - (x_gate_expected + x_path_offset)
x_new = x_next + dx_gate
# Calculate the new velocity
v_new = (x_new - x_curr) / dt_warped
# Calculate the new acceleration
a_new = (v_new - v_curr) / dt_warped
# Calculate the acceleration vector norm
a_new_norm = np.linalg.norm(a_new)
# Limit the norm vector
a_new_norm_clipped = np.clip(a_new_norm, -a_max, a_max)
# Determine the ratio of the clipped norm, protect against divide by zero
if a_new_norm != 0.0:
ratio_unclipped = a_new_norm_clipped / a_new_norm
else:
ratio_unclipped = 0.0
# Scale the acceleration vector by this ratio
a_new = a_new * ratio_unclipped
# Calculate the new change in velocity
dv_new = a_new * dt_warped
v_new = v_curr + dv_new
# Calculate the new change in position
dx_new = v_new * dt_warped
x_new = x_curr + dx_new
# Modify final path data with current tooltip and gate positions
pathutils.set_path_time(final_path, path_idx, t)
pathutils.set_path_tooltip_pos(final_path, path_idx, x_curr)
for gate_idx in range(constants.G_NUM_GATES):
gate_name = 'gate%d' % gate_idx
x_gate = self.env.get_body_pos(gate_name)
pathutils.set_path_gate_pos(final_path, path_idx, gate_idx,
x_gate)
# Store this velocity for the next time step
v_curr = v_new
# Recalculate the current offset
x_path_offset += x_new - x_next
# Perform inverse kinematics to get joint angles
pa10_joint_angles = self.kinematics.calc_inverse_kinematics(
x_curr, x_new)
# TODO: TEMP - MOVE ONLY POINTER, NO PA10
self.env.set_group_pos('pointer', x_new)
if constants.G_TABLE_IS_OSCILLATING:
# Move the table with y-axis oscillation
x_table_next = shaker_table(t, x_table)
else:
x_table_next = x_table
self.env.set_body_pos('table', x_table_next)
# Step through the world by 1 time frame and actuate pa10 joints
self.env.performAction(pa10_joint_angles, fast=fast_step)
# Update current time after this step
t += dt_warped
path_idx += 1
# 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 main():
path_file = '../../neuralsim/generated.dat' #'../../results/sample5.dat'
path = datastore.retrieve(path_file)
# A list of the the segments of the optimized path
segments = pathutils._detect_segments(path)
# The new path generated by original path and corrective algorithm
new_path = None
x_path_offset = np.array([0.0, 0.0, 0.0]) # [m]
v_curr = np.array([0.0, 0.0, 0.0]) # [m/s]
a_max = constants.G_MAX_ACCEL # [m/s^2]
for i, _ in enumerate(path):
if i == len(path) - 1:
continue
# Detect current segment
seg_idx = 0
for j in range(len(segments)):
if i <= segments[j]:
seg_idx = j
break
# Get current time and position
t_curr = pathutils.get_path_time(path, i) * t_total
t_next = pathutils.get_path_time(path, i + 1) * t_total
dt = (t_next - t_curr)
x_curr = pathutils.get_path_tooltip_pos(path, i) + x_path_offset
x_next = pathutils.get_path_tooltip_pos(path, i + 1) + x_path_offset
# Get the expected gate position at this timestep
x_gate_expected = pathutils.get_path_gate_pos(path, segments[seg_idx],
seg_idx)
# Get current gate position
x_gate_actual = generate_gate_pos(t_curr, path, seg_idx)
dx_gate = x_gate_actual - (x_gate_expected + x_path_offset)
# Calculate the new position with positional change from target to gate
x_new = x_next + dx_gate
# Calculate the new velocity
v_new = (x_new - x_curr) / dt
# Calculate the new acceleration
a_new = (v_new - v_curr) / dt
# Calculate the acceleration vector norm
a_new_norm = np.linalg.norm(a_new)
# Limit the norm vector
a_new_norm_clipped = np.clip(a_new_norm, -a_max, a_max)
# Determine the ratio of the clipped norm
if a_new_norm != 0:
ratio_unclipped = a_new_norm_clipped / a_new_norm
else:
ratio_unclipped = 0.0
# Scale the acceleration vector by this ratio
a_new = a_new * ratio_unclipped
# Calculate the new change in velocity
dv_new = a_new * dt
v_new = v_curr + dv_new
# Calculate the new change in position
dx_new = v_new * dt
x_new = x_curr + dx_new
# Store the next movement for later
if new_path is None:
new_path = x_new
else:
new_path = np.vstack((new_path, x_new))
# Store this velocity for the next time step
v_curr = v_new
# Recalculate the current offset
x_path_offset += x_new - x_next
# Plot the inputted path
fig = plt.figure(facecolor='white')
axis = fig.gca(projection='3d')
pos_start_idx = constants.G_POS_IDX
pos_end_idx = pos_start_idx + constants.G_NUM_POS_DIMS
full_path = path[:-1].copy()
full_path[:, pos_start_idx:pos_end_idx] = new_path
pathutils.display_path(axis, full_path, title='Path')
plt.show()
return
def start(self, fps, fast_step=False):
"""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. [Hz]
Arguments:
fps: The value of frames per second of the simulation.
fast_step: If True, the ODE fast step algorithm will be used.
This is faster and requires less memory but is less accurate.
(Default: False)
"""
paused = False
stopped = False
# Define the total time for the tooltip traversal
t_total = 20.0
# Define the simulation frame rate
t = 0.0 # [s]
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
# Get the initial path position (center of gate7)
pos_start = self.env.get_body_pos('gate7') # [m]
# Get the first position of the PA10 at rest
pos_init = self.env.get_body_pos('tooltip') # [m]
# Calculate the new required joint angles of the PA10
#pa10_joint_angles = self.kinematics.calc_inverse_kinematics(pos_init, pos_start)
# TODO: Move the PA10 end-effector to the starting position along the path
# TODO: TEMP - Move the temporary end-effector pointer to the starting position
self.env.set_group_pos('pointer', pos_start)
# Generate long-term path from initial position
t_input = np.linspace(start=0.0, stop=1.0, num=t_total/dt)
t_input = np.reshape(t_input, (len(t_input), 1))
rnn_path = self.rnn.extrapolate(t_input, [pos_start], len(t_input)-1)
# Add the initial condition point back onto the data
rnn_path = np.vstack((pos_start, rnn_path))
# Retrieve one set of standard gate position/orientation data
file_path = pathutils.list_data_files(constants.G_TRAINING_DATA_DIR)[0]
gate_data = datastore.retrieve(file_path)
gate_start_idx = constants.G_GATE_IDX
gate_end_idx = gate_start_idx + constants.G_NUM_GATE_INPUTS
# Reshape the gate positions data
gate_data = gate_data[0:1,gate_start_idx:gate_end_idx]
gate_data = np.tile(gate_data, (len(rnn_path), 1))
# Complete the rnn path data
rnn_path = np.hstack((t_input, gate_data, rnn_path))
# Save generated path for later examination
datastore.store(rnn_path, constants.G_RNN_STATIC_PATH_OUT)
# Define a variable to hold the final path (with real-time correction)
final_path = rnn_path[:-1].copy()
path_saved = False
# Detect all path segments between gates in the generated path
segments = pathutils._detect_segments(rnn_path)
path_idx = 0
x_path_offset = np.array([0.0, 0.0, 0.0]) # [m]
v_curr = np.array([0.0, 0.0, 0.0]) # [m/s]
a_max = constants.G_MAX_ACCEL # [m/s^2]
# Get the static table position
x_table = self.env.get_body_pos('table')
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)
# Determine if the viewer is stopped. Then we can quit
if self.viewer.is_dead:
break
# Pause the simulation if we are at the end
if path_idx == len(rnn_path) - 1 or paused:
self.env.step(paused=True, fast=fast_step)
# If we have really hit the end of the simulation, save/plot the path
if not paused and not path_saved:
# Save the final data to a file
datastore.store(final_path, constants.G_RNN_DYNAMIC_PATH_OUT)
path_saved = True
continue
# Not a very elegant solution to pausing at the start, but it works
if t <= 1000.0:
self.env.step(paused=True, fast=fast_step)
t += dt_warped
continue
# Determine the current path segment
curr_segment_idx = 0
for segment_idx, segment_end in enumerate(segments):
if path_idx <= segment_end:
curr_segment_idx = segment_idx
break
x_curr = pathutils.get_path_tooltip_pos(rnn_path, path_idx) + x_path_offset
x_next = pathutils.get_path_tooltip_pos(rnn_path, path_idx+1) + x_path_offset
# Get the expected gate position
x_gate_expected = pathutils.get_path_gate_pos(
rnn_path,
segments[curr_segment_idx],
curr_segment_idx
)
# Get the actual gate position
x_gate_actual = self.env.get_body_pos('gate%d'%curr_segment_idx)
# Calculate the new position from change to new gate position
dx_gate = x_gate_actual - (x_gate_expected + x_path_offset)
x_new = x_next + dx_gate
# Calculate the new velocity
v_new = (x_new - x_curr) / dt_warped
# Calculate the new acceleration
a_new = (v_new - v_curr) / dt_warped
# Calculate the acceleration vector norm
a_new_norm = np.linalg.norm(a_new)
# Limit the norm vector
a_new_norm_clipped = np.clip(a_new_norm, -a_max, a_max)
# Determine the ratio of the clipped norm, protect against divide by zero
if a_new_norm != 0.0:
ratio_unclipped = a_new_norm_clipped / a_new_norm
else:
ratio_unclipped = 0.0
# Scale the acceleration vector by this ratio
a_new = a_new * ratio_unclipped
# Calculate the new change in velocity
dv_new = a_new * dt_warped
v_new = v_curr + dv_new
# Calculate the new change in position
dx_new = v_new * dt_warped
x_new = x_curr + dx_new
# Modify final path data with current tooltip and gate positions
pathutils.set_path_time(final_path, path_idx, t)
pathutils.set_path_tooltip_pos(final_path, path_idx, x_curr)
for gate_idx in range(constants.G_NUM_GATES):
gate_name = 'gate%d' % gate_idx
x_gate = self.env.get_body_pos(gate_name)
pathutils.set_path_gate_pos(final_path, path_idx, gate_idx, x_gate)
# Store this velocity for the next time step
v_curr = v_new
# Recalculate the current offset
x_path_offset += x_new - x_next
# Perform inverse kinematics to get joint angles
pa10_joint_angles = self.kinematics.calc_inverse_kinematics(x_curr, x_new)
# TODO: TEMP - MOVE ONLY POINTER, NO PA10
self.env.set_group_pos('pointer', x_new)
if constants.G_TABLE_IS_OSCILLATING:
# Move the table with y-axis oscillation
x_table_next = shaker_table(t, x_table)
else:
x_table_next = x_table
self.env.set_body_pos('table', x_table_next)
# Step through the world by 1 time frame and actuate pa10 joints
self.env.performAction(pa10_joint_angles, fast=fast_step)
# Update current time after this step
t += dt_warped
path_idx += 1
# 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 main():
path_file = '../../neuralsim/generated.dat'#'../../results/sample5.dat'
path = datastore.retrieve(path_file)
# A list of the the segments of the optimized path
segments = pathutils._detect_segments(path)
# The new path generated by original path and corrective algorithm
new_path = None
x_path_offset = np.array([0.0, 0.0, 0.0]) # [m]
v_curr = np.array([0.0, 0.0, 0.0]) # [m/s]
a_max = constants.G_MAX_ACCEL # [m/s^2]
for i, _ in enumerate(path):
if i == len(path) - 1:
continue
# Detect current segment
seg_idx = 0
for j in range(len(segments)):
if i <= segments[j]:
seg_idx = j
break
# Get current time and position
t_curr = pathutils.get_path_time(path, i) * t_total
t_next = pathutils.get_path_time(path, i+1) * t_total
dt = (t_next - t_curr)
x_curr = pathutils.get_path_tooltip_pos(path, i) + x_path_offset
x_next = pathutils.get_path_tooltip_pos(path, i+1) + x_path_offset
# Get the expected gate position at this timestep
x_gate_expected = pathutils.get_path_gate_pos(path, segments[seg_idx], seg_idx)
# Get current gate position
x_gate_actual = generate_gate_pos(t_curr, path, seg_idx)
dx_gate = x_gate_actual - (x_gate_expected + x_path_offset)
# Calculate the new position with positional change from target to gate
x_new = x_next + dx_gate
# Calculate the new velocity
v_new = (x_new - x_curr) / dt
# Calculate the new acceleration
a_new = (v_new - v_curr) / dt
# Calculate the acceleration vector norm
a_new_norm = np.linalg.norm(a_new)
# Limit the norm vector
a_new_norm_clipped = np.clip(a_new_norm, -a_max, a_max)
# Determine the ratio of the clipped norm
if a_new_norm != 0:
ratio_unclipped = a_new_norm_clipped / a_new_norm
else:
ratio_unclipped = 0.0
# Scale the acceleration vector by this ratio
a_new = a_new * ratio_unclipped
# Calculate the new change in velocity
dv_new = a_new * dt
v_new = v_curr + dv_new
# Calculate the new change in position
dx_new = v_new * dt
x_new = x_curr + dx_new
# Store the next movement for later
if new_path is None:
new_path = x_new
else:
new_path = np.vstack((new_path, x_new))
# Store this velocity for the next time step
v_curr = v_new
# Recalculate the current offset
x_path_offset += x_new - x_next
# Plot the inputted path
fig = plt.figure(facecolor='white')
axis = fig.gca(projection='3d')
pos_start_idx = constants.G_POS_IDX
pos_end_idx = pos_start_idx + constants.G_NUM_POS_DIMS
full_path = path[:-1].copy()
full_path[:,pos_start_idx:pos_end_idx] = new_path
pathutils.display_path(axis, full_path, title='Path')
plt.show()
return
