def test_model_eq(self):
urdf_model = URDF.from_xml_file(res_pkg_path('package://kineverse/urdf/testbot.urdf'))
ks1 = ArticulationModel()
ks2 = ArticulationModel()
load_urdf(ks1, Path(urdf_model.name), urdf_model)
load_urdf(ks2, Path(urdf_model.name), urdf_model)
self.assertEquals(ks1, ks2)
def from_dict(cls, init_dict):
km = GeometryModel()
urdf = urdf_filler(URDF.from_xml_string(rospy.get_param('/robot_description')))
load_urdf(km, Path(urdf.name), urdf)
km.clean_structure()
km.dispatch_events()
base_frame = init_dict['reference_frame']
eefs = [Endeffector.from_dict(km.get_data(Path(urdf.name)), base_frame, d) for d in init_dict['links']]
return cls(km, Path(urdf.name), eefs)
def test_double_reload(self):
km = ArticulationModel()
urdf_model = URDF.from_xml_file(res_pkg_path('package://kineverse/urdf/testbot.urdf'))
load_urdf(km, Path(urdf_model.name), urdf_model)
km.clean_structure()
eef_frame_1 = km.get_data(Path(urdf_model.name) + Path('links/gripper_link/pose'))
load_urdf(km, Path(urdf_model.name), urdf_model)
km.clean_structure()
eef_frame_2 = km.get_data(Path(urdf_model.name) + Path('links/gripper_link/pose'))
self.assertEquals(eef_frame_1, eef_frame_2)
def load_model(km, model_path, reference_frame, x, y, z, yaw):
origin_pose = gm.frame3_rpy(0, 0, yaw, gm.point3(x, y, z))
if model_path != 'nobilia':
urdf_model = load_urdf_file(model_path)
load_urdf(km,
Path(urdf_model.name),
urdf_model,
reference_frame,
root_transform=origin_pose)
return urdf_model.name
else:
create_nobilia_shelf(km, Path('nobilia'), origin_pose, parent_path=Path(reference_frame))
return 'nobilia'
kitchen_model = urdf_filler(
URDF.from_xml_string(hacky_urdf_parser_fix(urdf_kitchen_str)))
#
traj_pup = rospy.Publisher('/{}/commands/joint_trajectory'.format(
urdf_model.name),
JointTrajectoryMsg,
queue_size=1)
kitchen_traj_pup = rospy.Publisher('/{}/commands/joint_trajectory'.format(
kitchen_model.name),
JointTrajectoryMsg,
queue_size=1)
# KINEMATIC MODEL
km = GeometryModel()
load_urdf(km, Path(robot), urdf_model)
load_urdf(km, Path('kitchen'), kitchen_model)
# create_nobilia_shelf(km, Path('nobilia'), gm.frame3_rpy(0, 0, 0.4,
# gm.point3(1.2, 0, 0.8)))
km.clean_structure()
km.apply_operation_before('create world', 'create {}'.format(robot),
ExecFunction(Path('world'), Frame, ''))
base_joint_path = Path(f'{robot}/joints/to_world')
# Insert base to world kinematic
if use_omni:
insert_omni_base(km, Path(robot), urdf_model.get_root())
else:
insert_diff_base(km,
fetch_urdf_str = urdf_file.read()
kitchen_urdf_model = urdf_filler(URDF.from_xml_string(kitchen_urdf_str))
rospy.set_param('/{}/robot_description'.format(kitchen_urdf_model.name),
kitchen_urdf_str)
kitchen_js_pub = rospy.Publisher('/{}/joint_states'.format(
kitchen_urdf_model.name),
JointStateMsg,
queue_size=1)
tf_broadcaster = tf.TransformBroadcaster()
# KINEMATIC MODEL
km = GeometryModel()
kitchen_prefix = Path(kitchen_urdf_model.name)
load_urdf(km, kitchen_prefix, kitchen_urdf_model)
plot_graph(generate_dependency_graph(km, {'connect': 'blue'}),
'{}/kitchen_sandbox_dep_graph.pdf'.format(plot_dir))
# ROBOT STATE PUBLISHER
sp_p = subprocess.Popen([
pub_path, '__name:={}_state_publisher'.format(kitchen_urdf_model.name),
'robot_description:=/{}/robot_description'.format(
kitchen_urdf_model.name),
'_tf_prefix:={}'.format(kitchen_urdf_model.name),
'joint_states:=/{}/joint_states'.format(kitchen_urdf_model.name)
])
jsmsg = JointStateMsg()
jsmsg.name = kitchen_urdf_model.joint_map.keys()
def test_apply_operation(self):
self.ops_msg = None
self.model_msg = None
def cb_update(ops, model):
self.ops_msg = ops
self.model_msg = model
server = ModelServerForTesting(cb_update)
def cb_srv_get_model(paths, csymbs):
return server.srv_get_model(
bb(paths=paths, constrained_symbols=csymbs))
def cb_srv_get_constraints(csymbs):
return server.srv_get_constraints(bb(symbols=csymbs))
op_client = OperationsClientForTesting(None, True)
m_client = ModelClientForTesting(cb_srv_get_model,
cb_srv_get_constraints, None)
m_client.has_data('testbot')
urdf_model = URDF.from_xml_file(
res_pkg_path('package://kineverse/urdf/testbot.urdf'))
load_urdf(op_client, urdf_model.name, urdf_model)
op_list = op_client.apply_changes()
req = bb(operations=op_list)
server.srv_apply_operations(req)
server.srv_apply_operations(req)
op_client.cb_operations_update(self.ops_msg)
with open('update.json', 'w') as f:
json.dump(dict(
zip(self.model_msg.update.paths, self.model_msg.update.data)),
f,
indent=2)
m_client.cb_model_update(self.model_msg)
o_model = server.km.get_data('testbot')
t_model = m_client.get_data('testbot')
with open('original.json', 'w') as f:
json.dump(o_model, f, indent=True)
with open('transmitted.json', 'w') as f:
json.dump(t_model, f, indent=True)
self.assertEquals(o_model.name, t_model.name)
l = o_model.links['arm_mount_link']
lt = t_model.links['arm_mount_link']
for n, l in o_model.links.items():
if n not in t_model.links:
print('Link {} missing from transmitted model'.format(n))
continue
lt = t_model.links[n]
if l == lt:
print('Equality check passed for link {}'.format(n))
print('Types: {} {}'.format(type(l), type(lt)))
print('parent: {}'.format(lt.parent == l.parent))
print('pose: {}'.format(lt.pose == l.pose))
print('to_parent: {}'.format(lt.to_parent == l.to_parent))
print('geometry: {}'.format(lt.geometry == l.geometry))
print('collision: {}'.format(lt.collision == l.collision))
print('inertial: {}\n---'.format(lt.inertial == l.inertial))
else:
print('Equality check failed for link {}'.format('arm_mount_link'))
print('Types: {} {}'.format(type(l), type(lt)))
print('parent: {}'.format(lt.parent == l.parent))
print('pose: {}\n{}'.format(lt.pose == l.pose, l.pose - lt.pose))
print('to_parent: {}\n{}'.format(lt.to_parent == l.to_parent,
l.to_parent - lt.to_parent))
print('geometry: {}'.format(lt.geometry == l.geometry))
print('collision: {}'.format(lt.collision == l.collision))
print('inertial: {}'.format(lt.inertial == l.inertial))
self.assertEquals(o_model.joints, t_model.joints)
self.assertEquals(o_model, t_model)
def main(create_figure=False,
vis_mode=False,
log_csv=True,
min_n_dof=1,
samples=300,
n_observations=25,
noise_lin=0.2,
noise_ang=30,
noise_steps=5):
wait_duration = rospy.Duration(0.1)
vis = ROSBPBVisualizer('ekf_vis', 'world') if vis_mode != 'none' else None
km = GeometryModel()
with open(res_pkg_path('package://iai_kitchen/urdf_obj/IAI_kitchen.urdf'),
'r') as urdf_file:
urdf_kitchen_str = urdf_file.read()
kitchen_model = urdf_filler(
URDF.from_xml_string(hacky_urdf_parser_fix(urdf_kitchen_str)))
load_urdf(km, Path('kitchen'), kitchen_model)
km.clean_structure()
km.dispatch_events()
kitchen = km.get_data('kitchen')
tracking_pools = []
for name, link in kitchen.links.items():
symbols = gm.free_symbols(link.pose)
if len(symbols) == 0:
continue
for x in range(len(tracking_pools)):
syms, l = tracking_pools[x]
if len(symbols.intersection(syms)
) != 0: # BAD ALGORITHM, DOES NOT CORRECTLY IDENTIFY SETS
tracking_pools[x] = (syms.union(symbols),
l + [(name, link.pose)])
break
else:
tracking_pools.append((symbols, [(name, link.pose)]))
# tracking_pools = [tracking_pools[7]]
# print('Identified {} tracking pools:\n{}'.format(len(tracking_pools), tracking_pools))
all_ekfs = [
EKFModel(dict(poses), km.get_constraints_by_symbols(symbols))
for symbols, poses in tracking_pools
] # np.eye(len(symbols)) * 0.001
print('Created {} EKF models'.format(len(all_ekfs)))
print('\n'.join(str(e) for e in all_ekfs))
# Sanity constraint
min_n_dof = min(min_n_dof, len(all_ekfs))
iteration_times = []
for u in range(min_n_dof, len(all_ekfs) + 1):
if rospy.is_shutdown():
break
ekfs = all_ekfs[:u]
observed_poses = {}
for ekf in ekfs:
for link_name, _ in ekf.takers:
observed_poses[link_name] = kitchen.links[link_name].pose
names, poses = zip(*sorted(observed_poses.items()))
state_symbols = union([gm.free_symbols(p) for p in poses])
ordered_state_vars = [
s for _, s in sorted((str(s), s) for s in state_symbols)
]
state_constraints = {}
for n, c in km.get_constraints_by_symbols(state_symbols).items():
if gm.is_symbol(c.expr):
s = gm.free_symbols(c.expr).pop()
fs = gm.free_symbols(c.lower).union(gm.free_symbols(c.upper))
if len(fs.difference({s})) == 0:
state_constraints[s] = (float(gm.subs(c.lower, {s: 0})),
float(gm.subs(c.upper, {s: 0})))
state_bounds = np.array([
state_constraints[s]
if s in state_constraints else [-np.pi * 0.5, np.pi * 0.5]
for s in ordered_state_vars
])
state_fn = gm.speed_up(gm.vstack(*poses), ordered_state_vars)
subworld = km.get_active_geometry(state_symbols)
# Generate observation noise
print('Generating R matrices...')
n_cov_obs = 400
full_log = []
dof_iters = []
# EXPERIMENT
for lin_std, ang_std in [(noise_lin, noise_ang * (np.pi / 180.0))]:
# zip(np.linspace(0, noise_lin, noise_steps),
# np.linspace(0, noise_ang * (np.pi / 180.0), noise_steps)):
if rospy.is_shutdown():
break
# INITIALIZE SENSOR MODEL
training_obs = []
state = np.random.uniform(state_bounds.T[0], state_bounds.T[1])
observations = state_fn.call2(state)
for _ in range(n_cov_obs):
noisy_obs = {}
for x, noise in enumerate([
t.dot(r) for t, r in zip(
random_normal_translation(len(poses), 0, lin_std),
random_rot_normal(len(poses), 0, ang_std))
]):
noisy_obs[names[x]] = observations[x * 4:x * 4 +
4, :4].dot(noise)
training_obs.append(noisy_obs)
for ekf in ekfs:
ekf.generate_R(training_obs)
# ekf.set_R(np.eye(len(ekf.ordered_vars)) * 0.1)
# Generate figure
gridsize = (4, samples)
plot_size = (4, 4)
fig = plt.figure(figsize=(gridsize[1] * plot_size[0], gridsize[0] *
plot_size[1])) if create_figure else None
gt_states = []
states = [[] for x in range(samples)]
variances = [[] for x in range(samples)]
e_obs = [[] for x in range(samples)]
print('Starting iterations')
for k in tqdm(range(samples)):
if rospy.is_shutdown():
break
state = np.random.uniform(state_bounds.T[0], state_bounds.T[1])
gt_states.append(state)
observations = state_fn.call2(state).copy()
gt_obs_d = {
n: observations[x * 4:x * 4 + 4, :4]
for x, n in enumerate(names)
}
subworld.update_world(dict(zip(ordered_state_vars, state)))
if vis_mode == 'iter' or vis_mode == 'io':
vis.begin_draw_cycle('gt', 'noise', 'estimate', 't_n',
't0')
vis.draw_world('gt', subworld, g=0, b=0)
vis.render('gt')
estimates = []
for ekf in ekfs:
particle = ekf.spawn_particle()
estimates.append(particle)
initial_state = dict(
sum([[(s, v) for s, v in zip(ekf.ordered_vars, e.state)]
for e, ekf in zip(estimates, ekfs)], []))
initial_state = np.array(
[initial_state[s] for s in ordered_state_vars])
if initial_state.min() < state_bounds.T[0].min(
) or initial_state.max() > state_bounds.T[1].max():
raise Exception(
'Estimate initialization is out of bounds: {}'.format(
np.vstack([initial_state, state_bounds.T]).T))
initial_delta = state - initial_state
for y in range(n_observations):
# Add noise to observations
noisy_obs = {}
for x, noise in enumerate([
t.dot(r) for t, r in zip(
random_normal_translation(
len(poses), 0, lin_std),
random_rot_normal(len(poses), 0, ang_std))
]):
noisy_obs[names[x]] = observations[x * 4:x * 4 +
4, :4].dot(noise)
if vis_mode in {'iter', 'iter-trail'} or (vis_mode == 'io'
and y == 0):
for n, t in noisy_obs.items():
subworld.named_objects[Path(
('kitchen', 'links', n))].np_transform = t
if vis_mode != 'iter-trail':
vis.begin_draw_cycle('noise')
vis.draw_world('noise', subworld, r=0, g=0, a=0.1)
vis.render('noise')
start_time = time()
for estimate, ekf in zip(estimates, ekfs):
if y > 0:
control = np.zeros(len(ekf.ordered_controls))
estimate.state, estimate.cov = ekf.predict(
estimate.state.flatten(), estimate.cov,
control)
obs_vector = ekf.gen_obs_vector(noisy_obs)
estimate.state, estimate.cov = ekf.update(
estimate.state, estimate.cov,
ekf.gen_obs_vector(noisy_obs))
if vis_mode in {'iter', 'iter-trail'}:
subworld.update_world({
s: v
for s, v in zip(ekf.ordered_vars,
estimate.state)
})
else:
obs_vector = ekf.gen_obs_vector(noisy_obs)
for _ in range(1):
h_prime = ekf.h_prime_fn.call2(estimate.state)
obs_delta = obs_vector.reshape(
(len(obs_vector), 1)) - ekf.h_fn.call2(
estimate.state)
estimate.state += (h_prime.T.dot(obs_delta) *
0.1).reshape(
estimate.state.shape)
if vis_mode in {'iter', 'io'}:
subworld.update_world({
s: v
for s, v in zip(ekf.ordered_vars,
estimate.state)
})
if vis_mode != 'none' and y == 0:
vis.draw_world('t0', subworld, b=0, a=1)
vis.render('t0')
elif vis_mode in {'iter', 'iter-trail'}:
if vis_mode != 'iter-trail':
vis.begin_draw_cycle('t_n')
vis.draw_world('t_n', subworld, b=0, a=1)
vis.render('t_n')
if log_csv or fig is not None:
e_state_d = dict(
sum([[(s, v)
for s, v in zip(ekf.ordered_vars, e.state)]
for e, ekf in zip(estimates, ekfs)], []))
covs = dict(
sum([[(s, v) for s, v in zip(
ekf.ordered_vars, np.sqrt(np.trace(e.cov)))]
for e, ekf in zip(estimates, ekfs)], []))
e_state = np.hstack([
e_state_d[s] for s in ordered_state_vars
]).reshape((len(e_state_d), ))
if log_csv:
full_log.append(
np.hstack(
([lin_std,
ang_std], state, e_state.flatten(),
np.array([
covs[s] for s in ordered_state_vars
]))))
if fig is not None:
e_obs[k].append(
np.array([
np.abs(
ekf.gen_obs_vector(gt_obs_d) -
ekf.h_fn.call2(e.state)).max()
for e, ekf in zip(estimates, ekfs)
]))
states[k].append(e_state)
variances[k].append(
np.array([covs[s]
for s in ordered_state_vars]))
else:
if vis_mode == 'io':
for estimate, ekf in zip(estimates, ekfs):
subworld.update_world({
s: v
for s, v in zip(ekf.ordered_vars,
estimate.state)
})
vis.draw_world('t_n', subworld, r=0, b=0, a=1)
vis.render('t_n')
dof_iters.append(time() - start_time)
if fig is not None:
axes = [
plt.subplot2grid(gridsize, (y, 0), colspan=1, rowspan=1)
for y in range(gridsize[0])
]
axes = np.array(
sum([[
plt.subplot2grid(gridsize, (y, x),
colspan=1,
rowspan=1,
sharey=axes[y])
for y in range(gridsize[0])
] for x in range(1, gridsize[1])], axes)).reshape(
(gridsize[1], gridsize[0]))
for x, (gt_s, state, variance, obs_delta,
(ax_s, ax_d, ax_o, ax_v)) in enumerate(
zip(gt_states, states, variances, e_obs, axes)):
for y in gt_s:
ax_s.axhline(y, xmin=0.97, xmax=1.02)
ax_s.set_title('State; Sample: {}'.format(x))
ax_d.set_title('Delta from GT; Sample: {}'.format(x))
ax_o.set_title('Max Delta in Obs; Sample: {}'.format(x))
ax_v.set_title('Standard Deviation; Sample: {}'.format(x))
ax_s.plot(state)
ax_d.plot(gt_s - np.vstack(state))
ax_o.plot(obs_delta)
ax_v.plot(variance)
ax_s.grid(True)
ax_d.grid(True)
ax_o.grid(True)
ax_v.grid(True)
fig.tight_layout()
plt.savefig(
res_pkg_path(
'package://kineverse_experiment_world/test/ekf_object_tracker_{}_{}.png'
.format(lin_std, ang_std)))
iteration_times.append(dof_iters)
if log_csv:
df = pd.DataFrame(
columns=['lin_std', 'ang_std'] +
['gt_{}'.format(x) for x in range(len(state_symbols))] +
['ft_{}'.format(x) for x in range(len(state_symbols))] +
['var_{}'.format(x) for x in range(len(state_symbols))],
data=full_log)
df.to_csv(res_pkg_path(
'package://kineverse_experiment_world/test/ekf_object_tracker.csv'
),
index=False)
df = pd.DataFrame(
columns=[str(x) for x in range(1,
len(iteration_times) + 1)],
data=np.vstack(iteration_times).T)
df.to_csv(res_pkg_path(
'package://kineverse_experiment_world/test/ekf_object_tracker_performance.csv'
),
index=False)
urdf_model = load_urdf_file(description)
else:
if not rospy.has_param(description):
print(
f'Description is supposed to be located at "{description}" but that parameter does not exist'
)
urdf_model = load_urdf_str(rospy.get_param(description))
name = rospy.get_param('~name', urdf_model.name)
reference_frame = rospy.get_param('~reference_frame', 'world')
km = GeometryModel()
load_urdf(km,
Path(name),
urdf_model,
reference_frame,
joint_prefix='',
root_transform=root_transform)
km.clean_structure()
if special_base is not None:
if special_base == 'omni':
insert_omni_base(km, Path(name), urdf_model.get_root(),
reference_frame)
elif special_base == 'diff':
insert_diff_base(km, Path(name), urdf_model.get_root(),
reference_frame)
km.dispatch_events()
'PR2 will be loaded from parameter server. It is currently not there.'
)
exit(1)
urdf_model = load_urdf_file(
'package://iai_pr2_description/robots/pr2_calibrated_with_ft2.xml')
# urdf_model = load_urdf_str(rospy.get_param('/robot_description'))
if urdf_model.name.lower() != 'pr2':
print(
f'The loaded robot is not the PR2. Its name is "{urdf_model.name}"'
)
exit(1)
km = GeometryModel()
load_urdf(km, Path('pr2'), urdf_model)
km.clean_structure()
reference_frame = rospy.get_param('~reference_frame',
urdf_model.get_root())
use_base = reference_frame != urdf_model.get_root()
if use_base:
insert_omni_base(km, Path('pr2'), urdf_model.get_root(),
reference_frame)
base_joint_path = Path(f'pr2/joints/to_{reference_frame}')
visualizer = ROSBPBVisualizer('~vis', base_frame=reference_frame)
model_name = load_localized_model(km, model_path, reference_frame)
def test_load(self):
urdf_model = URDF.from_xml_file(res_pkg_path('package://kineverse/urdf/testbot.urdf'))
ks = ArticulationModel()
load_urdf(ks, Path(urdf_model.name), urdf_model)
from kineverse.motion.integrator import CommandIntegrator
from kineverse.visualization.bpb_visualizer import ROSBPBVisualizer
from kineverse.visualization.plotting import draw_recorders, split_recorders
from kineverse.visualization.trajectory_visualizer import TrajectoryVisualizer
from urdf_parser_py.urdf import URDF
if __name__ == '__main__':
rospy.init_node('microwave_sandbox')
micro_urdf = URDF.from_xml_file(
res_pkg_path('package://kineverse/urdf/microwave.urdf'))
km = EventModel()
#km = GeometryModel()
load_urdf(km, Path('microwave'), micro_urdf)
traj_vis = TrajectoryVisualizer(ROSBPBVisualizer('/bullet_test', '/map'))
traj_vis.add_articulated_object(micro_urdf, km.get_data('microwave'))
door_position = km.get_data('microwave/joints/door_joint').position
button_position = km.get_data('microwave/joints/button_joint').position
button_min = -0.02
door_velocity = get_diff_symbol(door_position)
door_accel = get_diff_symbol(door_velocity)
point_of_release = 0.8 * button_min
button_pushed = less_than(button_position, point_of_release)
condition = alg_and(less_than(door_position, 0.1), button_pushed)
spring_constraint = Constraint((2 * condition)**(-100 * door_position),
1e9, door_accel)
help='Name of the resulting csv file.')
args = parser.parse_args()
rospy.init_node('kineverse_tracking_node')
tracker = TrackerNode('/tracked/state',
'/pose_obs',
args.step,
args.max_iter,
use_timer=False)
with open(res_pkg_path('package://iai_kitchen/urdf_obj/IAI_kitchen.urdf'),
'r') as urdf_file:
urdf_kitchen_str = urdf_file.read()
kitchen_model = urdf_filler(URDF.from_xml_string(urdf_kitchen_str))
load_urdf(tracker.km_client, Path('iai_oven_area'), kitchen_model)
tracker.km_client.clean_structure()
tracker.km_client.dispatch_events()
groups = [
[('iai_oven_area/links/room_link', 'iai_oven_area/room_link'),
('iai_oven_area/links/fridge_area', 'iai_oven_area/fridge_area'),
('iai_oven_area/links/fridge_area_footprint',
'iai_oven_area/fridge_area_footprint'),
('iai_oven_area/links/fridge_area_lower_drawer_handle',
'iai_oven_area/fridge_area_lower_drawer_handle'),
('iai_oven_area/links/fridge_area_lower_drawer_main',
'iai_oven_area/fridge_area_lower_drawer_main')],
[('iai_oven_area/links/iai_fridge_door',
'iai_oven_area/iai_fridge_door'),
def bpb():
import numpy as np
import kineverse.bpb_wrapper as pb
import kineverse.model.geometry_model as gm
import kineverse.operations.urdf_operations as urdf
from kineverse.urdf_fix import urdf_filler
from urdf_parser_py.urdf import URDF
km = gm.GeometryModel()
with open(
res_pkg_path(
'package://iai_pr2_description/robots/pr2_calibrated_with_ft2.xml'
), 'r') as urdf_file:
urdf.load_urdf(km, 'pr2',
urdf_filler(URDF.from_xml_string(urdf_file.read())))
with open(res_pkg_path('package://iai_kitchen/urdf_obj/IAI_kitchen.urdf'),
'r') as urdf_file:
urdf.load_urdf(km, 'kitchen',
urdf_filler(URDF.from_xml_string(urdf_file.read())))
km.clean_structure()
km.dispatch_events()
kitchen = km.get_data('kitchen')
pr2 = km.get_data('pr2')
joint_symbols = {
j.position
for j in kitchen.joints.values() if hasattr(j, 'position')
}
joint_symbols |= {
j.position
for j in pr2.joints.values() if hasattr(j, 'position')
}
coll_world = km.get_active_geometry(joint_symbols)
robot_parts = {
n: o
for n, o in coll_world.named_objects.items() if n[:4] == 'pr2/'
}
batch = {o: 2.0 for o in robot_parts.values()}
print(
'Benchmarking by querying distances for {} objects. Total object count: {}.\n'
.format(len(batch), len(coll_world.names)))
dur_update = []
dur_distances = []
# print('Mesh files:\n{}'.format('\n'.join(sorted({pb.pb.get_shape_filename(s) for s in sum([o.collision_shape.child_shapes for o in coll_world.collision_objects], [])}))))
# print('Objects:\n{}'.format('\n'.join(['{}:\n {}\n {} {}'.format(n,
# o.transform.rotation,
# o.transform.origin,
# pb.pb.get_shape_filename(o.collision_shape.get_child(0)))
# for n, o in coll_world.named_objects.items()])))
for x in tqdm(range(100)):
start = Time.now()
coll_world.update_world({
s: v
for s, v in zip(joint_symbols, np.random.rand(len(joint_symbols)))
})
dur_update.append(Time.now() - start)
start = Time.now()
distances = coll_world.closest_distances(batch)
dur_distances.append(Time.now() - start)
dur_update_mean = sum([d.to_sec() for d in dur_update]) / len(dur_update)
dur_distances_mean = sum([d.to_sec()
for d in dur_distances]) / len(dur_distances)
print('Update mean: {}\nUpdate max: {}\n'
'Distances mean: {}\nDistances max: {}\n'.format(
dur_update_mean,
max(dur_update).to_sec(),
dur_distances_mean,
max(dur_distances).to_sec(),
))
km = GeometryModel()
robot_type = rospy.get_param('~robot', 'pr2')
if robot_type.lower() == 'pr2':
robot_urdf = load_urdf_file('package://iai_pr2_description/robots/pr2_calibrated_with_ft2.xml')
elif robot_type.lower() == 'hsrb':
robot_urdf = load_urdf_file('package://hsr_description/robots/hsrb4s.obj.urdf')
elif robot_type.lower() == 'fetch':
robot_urdf = load_urdf_file('package://fetch_description/robots/fetch.urdf')
else:
print(f'Unknown robot {robot_type}')
exit(1)
robot_name = robot_urdf.name
robot_path = Path(robot_name)
load_urdf(km, robot_path, robot_urdf, Path('world'))
km.clean_structure()
model_name = load_model(km,
rospy.get_param('~model', 'nobilia'),
'world',
1.0, 0, 0.7, rospy.get_param('~yaw', 0.57))
km.clean_structure()
km.dispatch_events()
print('\n'.join(km.timeline_tags.keys()))
if rospy.get_param('~use_base', False):
if robot_type.lower() == 'fetch':
insert_diff_base(km,
if __name__ == '__main__':
rospy.init_node('iiwa_kinematic_sim')
rosparam_description = rospy.get_param('/robot_description', None)
if rosparam_description is None:
with open(
res_pkg_path(
'package://kineverse_experiment_world/urdf/iiwa_wsg_50.urdf'
), 'r') as f:
rosparam_description = f.read()
urdf = load_urdf_str(rosparam_description)
km = GeometryModel()
load_urdf(km, Path('iiwa'), urdf)
km.clean_structure()
km.dispatch_events()
sim = KineverseKinematicSim(km,
Path('iiwa'),
state_topic='/iiwa/joint_states')
sorted_names = sorted(sim.state_info.keys())
js_msg = JointStateMsg()
js_msg.name = sorted_names
def cb_pos_array_command(msg):
js_msg.header.stamp = rospy.Time.now()
js_msg.position = msg.data
rospy.get_param('/robot_description'))
radius = float(sys.argv[1])
distance = float(sys.argv[2])
else:
urdf_model = URDF.from_xml_file(res_pkg_path(sys.argv[1]))
radius = float(sys.argv[2])
distance = float(sys.argv[3])
# Fill collision geometry if demanded
if len(sys.argv) >= 5 and (sys.argv[4].lower() == 'true'
or sys.argv[4] == '1'):
urdf_model = urdf_filler(urdf_model)
op_client = OperationsClient(EventModel, True)
load_urdf(op_client, urdf_model.name, urdf_model)
op_client.apply_operation_before(
'create world', 'create {}'.format(urdf_model.name),
CreateComplexObject(Path('world'), Frame('')))
r_limit = 1.0 / (pi * radius * 2)
drive_op = create_diff_drive_joint_with_symbols(
Path('world/pose'),
Path('{}/links/{}/pose'.format(urdf_model.name,
urdf_model.get_root())),
Path('{}/joints/to_world'.format(urdf_model.name)), radius, distance,
r_limit, Path(urdf_model.name))
op_client.apply_operation_after(
'connect world {}'.format(urdf_model.get_root()),
'create {}/{}'.format(urdf_model.name,
JointTrajectoryMsg,
queue_size=1)
tf_broadcaster = tf.TransformBroadcaster()
urdf_model = URDF.from_xml_string(urdf_str)
# ROBOT STATE PUBLISHER
# sp_p = subprocess.Popen([pub_path,
# '__name:={}_state_publisher'.format(urdf_model.name),
# 'robot_description:=/robot_description',
# '_tf_prefix:={}'.format(urdf_model.name),
# 'joint_states:=/joint_states'])
# KINEMATIC MODEL
km = ArticulationModel()
load_urdf(km, Path('fetch'), urdf_model)
km.clean_structure()
km.apply_operation_before('create world', 'create fetch',
CreateComplexObject(Path('world'), Frame('')))
roomba_op = create_roomba_joint_with_symbols(
Path('world/pose'), Path('fetch/links/base_link/pose'),
Path('fetch/joints/to_world'), vector3(0, 0, 1), vector3(1, 0, 0), 1.0,
0.6, Path('fetch'))
km.apply_operation_after('connect world base_link',
'create fetch/base_link', roomba_op)
km.clean_structure()
with open(res_pkg_path('package://kineverse/test/fetch.json'),
'w') as fetch_json:
urdf_model = URDF.from_xml_string(urdf_str)
kitchen_model = urdf_filler(URDF.from_xml_string(urdf_kitchen_str))
traj_pup = rospy.Publisher('/{}/commands/joint_trajectory'.format(
urdf_model.name),
JointTrajectoryMsg,
queue_size=1)
kitchen_traj_pup = rospy.Publisher('/{}/commands/joint_trajectory'.format(
kitchen_model.name),
JointTrajectoryMsg,
queue_size=1)
# KINEMATIC MODEL
km = GeometryModel()
load_urdf(km, Path('fetch'), urdf_model)
load_urdf(km, Path('kitchen'), kitchen_model)
km.clean_structure()
km.apply_operation_before('create world', 'create fetch',
CreateComplexObject(Path('world'), Frame('')))
if use_omni:
base_op = create_omnibase_joint_with_symbols(
Path('world/pose'), Path('fetch/links/base_link/pose'),
Path('fetch/joints/to_world'), vector3(0, 0, 1), 1.0, 0.6,
Path('fetch'))
else:
base_op = create_roomba_joint_with_symbols(
Path('world/pose'), Path('fetch/links/base_link/pose'),
Path('fetch/joints/to_world'), vector3(0, 0, 1), vector3(1, 0, 0),
