def generate_frame_variant(self, frame, options=None):
"""....
Parameters
----------
frame : :class:`compas.geometry.Frame`
TODO
Yield
-----
frame
frame variantion
"""
# overwrite if options is provided in the function call
options = options or self._options
# * half range
delta_roll = is_valid_option(options, 'delta_roll', 0.)
delta_pitch = is_valid_option(options, 'delta_pitch', 0.)
delta_yaw = is_valid_option(options, 'delta_yaw', 0.)
# * discretization
roll_sample_size = is_valid_option(options, 'roll_sample_size', 1)
pitch_sample_size = is_valid_option(options, 'pitch_sample_size', 1)
yaw_sample_size = is_valid_option(options, 'yaw_sample_size', 1)
roll_gen = np.linspace(-delta_roll, delta_roll, num=roll_sample_size)
pitch_gen = np.linspace(-delta_pitch, delta_pitch, num=pitch_sample_size)
yaw_gen = np.linspace(-delta_yaw, delta_yaw, num=yaw_sample_size)
pose = pose_from_frame(frame)
# a finite generator
for roll, pitch, yaw in product(roll_gen, pitch_gen, yaw_gen):
yield frame_from_pose(multiply(pose, Pose(euler=Euler(roll=roll, pitch=pitch, yaw=yaw))))
def plan_motion(self, robot, goal_constraints, start_configuration=None, group=None, options=None):
"""Calculates a motion path.
Parameters
----------
robot : :class:`compas_fab.robots.Robot`
The robot instance for which the motion path is being calculated.
goal_constraints: list of :class:`compas_fab.robots.Constraint`
The goal to be achieved, defined in a set of constraints.
Constraints can be very specific, for example defining value domains
for each joint, such that the goal configuration is included,
or defining a volume in space, to which a specific robot link (e.g.
the end-effector) is required to move to.
start_configuration: :class:`compas_fab.robots.Configuration`, optional
The robot's full configuration, i.e. values for all configurable
joints of the entire robot, at the starting position.
group: str, optional
The name of the group to plan for.
options : dict, optional
Dictionary containing kwargs for arguments specific to
the client being queried.
- ``"avoid_collisions"``: (:obj:`bool`, optional)
Whether or not to avoid collisions. Defaults to ``True``.
- ``"max_step"``: (:obj:`float`, optional) The approximate distance between the
calculated points. (Defined in the robot's units.) Defaults to ``0.01``.
- ``"jump_threshold"``: (:obj:`float`, optional)
The maximum allowed distance of joint positions between consecutive
points. If the distance is found to be above this threshold, the
path computation fails. It must be specified in relation to max_step.
If this threshold is ``0``, 'jumps' might occur, resulting in an invalid
cartesian path. Defaults to :math:`\\pi / 2`.
Returns
-------
:class:`compas_fab.robots.JointTrajectory`
The calculated trajectory.
"""
robot_uid = robot.attributes['pybullet_uid']
# * parse options
verbose = is_valid_option(options, 'verbose', False)
diagnosis = options.get('diagnosis', False)
custom_limits = options.get('custom_limits') or {}
resolutions = options.get('resolutions') or 0.1
weights = options.get('weights') or None
rrt_restarts = options.get('rrt_restarts', 2)
rrt_iterations = options.get('rrt_iterations', 20)
smooth_iterations = options.get('smooth_iterations', 20)
# TODO: auto compute joint weight
# print('plan motion options: ', options)
# * convert link/joint names to pybullet indices
joint_names = robot.get_configurable_joint_names(group=group)
ik_joints = joints_from_names(robot_uid, joint_names)
joint_types = robot.get_joint_types_by_names(joint_names)
pb_custom_limits = get_custom_limits(robot_uid, ik_joints,
custom_limits={joint_from_name(robot_uid, jn) : lims for jn, lims in custom_limits.items()})
# print('pb custom limits: ', list(pb_custom_limits))
with WorldSaver():
if start_configuration is not None:
# * set to start conf
self.client.set_robot_configuration(robot, start_configuration)
sample_fn = get_sample_fn(robot_uid, ik_joints, custom_limits=pb_custom_limits)
distance_fn = get_distance_fn(robot_uid, ik_joints, weights=weights)
extend_fn = get_extend_fn(robot_uid, ik_joints, resolutions=resolutions)
options['robot'] = robot
collision_fn = PyChoreoConfigurationCollisionChecker(self.client)._get_collision_fn(robot, joint_names, options=options)
start_conf = get_joint_positions(robot_uid, ik_joints)
end_conf = self._joint_values_from_joint_constraints(joint_names, goal_constraints)
assert len(ik_joints) == len(end_conf)
if not check_initial_end(start_conf, end_conf, collision_fn, diagnosis=diagnosis):
return None
path = birrt(start_conf, end_conf, distance_fn, sample_fn, extend_fn, collision_fn,
restarts=rrt_restarts, iterations=rrt_iterations, smooth=smooth_iterations)
#return plan_lazy_prm(start_conf, end_conf, sample_fn, extend_fn, collision_fn)
if path is None:
# TODO use LOG
if verbose:
cprint('No free motion found!', 'red')
return None
else:
jt_traj_pts = []
for i, conf in enumerate(path):
# c_conf = Configuration(values=conf, types=joint_types, joint_names=joint_names)
jt_traj_pt = JointTrajectoryPoint(values=conf, types=joint_types, time_from_start=Duration(i*1,0))
# TODO why don't we have a `joint_names` input for JointTrajectoryPoint?
# https://github.com/compas-dev/compas_fab/blob/master/src/compas_fab/robots/trajectory.py#L64
jt_traj_pt.joint_names = joint_names
jt_traj_pts.append(jt_traj_pt)
trajectory = JointTrajectory(trajectory_points=jt_traj_pts,
joint_names=joint_names, start_configuration=jt_traj_pts[0], fraction=1.0)
return trajectory
def inverse_kinematics(self,
robot,
frame_WCF,
start_configuration=None,
group=None,
options=None):
"""Calculate the robot's inverse kinematic for a given frame.
Parameters
----------
robot : :class:`compas_fab.robots.Robot`
The robot instance for which inverse kinematics is being calculated.
frame_WCF: :class:`compas.geometry.Frame`
The frame to calculate the inverse for.
start_configuration: :class:`compas_fab.robots.Configuration`, optional
If passed, the inverse will be calculated such that the calculated
joint positions differ the least from the start_configuration.
Defaults to the init configuration.
group: str, optional
The planning group used for calculation. Defaults to the robot's
main planning group.
options: dict, optional
Dictionary containing the following key-value pairs:
- ``"base_link"``: (:obj:`str`) Name of the base link.
- ``"avoid_collisions"``: (:obj:`bool`, optional) Whether or not to avoid collisions.
Defaults to `True`.
- ``"constraints"``: (:obj:`list` of :class:`compas_fab.robots.Constraint`, optional)
A set of constraints that the request must obey. Defaults to `None`.
- ``"attempts"``: (:obj:`int`, optional) The maximum number of inverse kinematic attempts.
Defaults to `8`.
- ``"attached_collision_meshes"``: (:obj:`list` of :class:`compas_fab.robots.AttachedCollisionMesh`, optional)
Defaults to `None`.
- ``"return_all"``: (bool, optional) return a list of all computed ik solutions
Defaults to False
Raises
------
compas_fab.backends.exceptions.BackendError
If no configuration can be found.
Returns
-------
:class:`compas_fab.robots.Configuration`
The planning group's configuration.
"""
max_iterations = is_valid_option(options, 'attempts', 8)
avoid_collisions = is_valid_option(options, 'avoid_collisions', True)
return_all = is_valid_option(options, 'return_all', False)
custom_limits = options.get('custom_limits') or {}
# return_closest_to_start = is_valid_option(options, 'return_closest_to_start', False)
# cull = is_valid_option(options, 'cull', True)
robot_uid = robot.attributes['pybullet_uid']
ik_joint_names = robot.get_configurable_joint_names(group=group)
ik_joints = joints_from_names(robot_uid, ik_joint_names)
tool_link_name = robot.get_end_effector_link_name(group=group)
tool_link = link_from_name(robot_uid, tool_link_name)
pb_custom_limits = get_custom_limits(
robot_uid,
ik_joints,
custom_limits={
joint_from_name(robot_uid, jn): lims
for jn, lims in custom_limits.items()
})
target_pose = pose_from_frame(frame_WCF)
with WorldSaver():
if start_configuration is not None:
start_conf_vals = start_configuration.values
set_joint_positions(robot_uid, ik_joints, start_conf_vals)
# else:
# sample_fn = get_sample_fn(robot_uid, ik_joints)
# start_conf_vals = sample_fn()
# if group not in self.client.planner.ik_fn_from_group:
# use default ik fn
conf_vals = self._compute_ik(robot_uid,
ik_joints,
tool_link,
target_pose,
max_iterations,
custom_limits=pb_custom_limits)
joint_types = robot.get_joint_types_by_names(ik_joint_names)
configurations = [Configuration(values=conf_val, types=joint_types, joint_names=ik_joint_names) \
for conf_val in conf_vals if conf_val is not None]
# else:
# # qs = client.inverse_kinematics(frame_WCF, group=move_group)
# configurations = self.client.planner.ik_fn_from_group[group](frame_WCF, group=group, options=options)
if avoid_collisions:
configurations = [
conf for conf in configurations
if not self.client.check_collisions(
robot, conf, options=options)
]
if return_all:
return configurations
else:
return configurations[0] if len(configurations) > 0 else None
def plan_cartesian_motion(self,
robot,
frames_WCF,
start_configuration=None,
group=None,
options=None):
"""Calculates a cartesian motion path (linear in tool space).
Parameters
----------
robot : :class:`compas_fab.robots.Robot`
The robot instance for which the motion path is being calculated.
frames_WCF: list of :class:`compas.geometry.Frame`
The frames through which the path is defined.
start_configuration: :class:`Configuration`, optional
The robot's full configuration, i.e. values for all configurable
joints of the entire robot, at the starting position.
group: str, optional
The planning group used for calculation.
options: dict, optional
Dictionary containing kwargs for arguments specific to
the client being queried.
- ``"diagnosis"``: (:obj:`bool`, optional)
. Defaults to ``False``.
- ``"avoid_collisions"``: (:obj:`bool`, optional)
Whether or not to avoid collisions. Defaults to ``True``.
- ``"planner_id"``: (:obj:`str`)
The name of the algorithm used for path planning.
Defaults to ``'IterativeIK'``.
Available planners: ``'IterativeIK', 'LadderGraph'``
- ``"max_step"``: (:obj:`float`, optional) The approximate distance between the
calculated points. (Defined in the robot's units.) Defaults to ``0.01``.
- ``"jump_threshold"``: (:obj:`float`, optional)
The maximum allowed distance of joint positions between consecutive
points. If the distance is found to be above this threshold, the
path computation fails. It must be specified in relation to max_step.
If this threshold is ``0``, 'jumps' might occur, resulting in an invalid
cartesian path.
Defaults to :math:`\\pi / 6` for revolute or continuous joints, 0.1 for prismatic joints.
# TODO: JointConstraint
Returns
-------
:class:`compas_fab.robots.JointTrajectory`
The calculated trajectory.
"""
robot_uid = robot.attributes['pybullet_uid']
# * convert link/joint names to pybullet indices
base_link_name = robot.get_base_link_name(group=group)
tool_link_name = robot.get_end_effector_link_name(group=group)
tool_link = link_from_name(robot_uid, tool_link_name)
base_link = link_from_name(robot_uid, base_link_name)
joint_names = robot.get_configurable_joint_names(group=group)
joint_types = robot.get_joint_types_by_names(joint_names)
ik_joints = joints_from_names(robot_uid, joint_names)
# * parse options
verbose = options.get('verbose', True)
diagnosis = options.get('diagnosis', False)
avoid_collisions = options.get('avoid_collisions', True)
pos_step_size = options.get('max_step', 0.01)
planner_id = is_valid_option(options, 'planner_id', 'IterativeIK')
jump_threshold = is_valid_option(options, 'jump_threshold', {jt_name : math.pi/6 \
if jt_type in [Joint.REVOLUTE, Joint.CONTINUOUS] else 0.1 \
for jt_name, jt_type in zip(joint_names, joint_types)})
jump_threshold_from_joint = {
joint_from_name(robot_uid, jt_name): j_diff
for jt_name, j_diff in jump_threshold.items()
}
# * iterative IK options
pos_tolerance = is_valid_option(options, 'pos_tolerance', 1e-3)
ori_tolerance = is_valid_option(options, 'ori_tolerance', 1e-3 * np.pi)
# * ladder graph options
frame_variant_gen = is_valid_option(options, 'frame_variant_generator',
None)
ik_function = is_valid_option(options, 'ik_function', None)
# * convert to poses and do workspace linear interpolation
given_poses = [pose_from_frame(frame_WCF) for frame_WCF in frames_WCF]
ee_poses = []
for p1, p2 in zip(given_poses[:-1], given_poses[1:]):
c_interp_poses = list(
interpolate_poses(p1, p2, pos_step_size=pos_step_size))
ee_poses.extend(c_interp_poses)
# * build collision fn
attachments = values_as_list(self.client.pychoreo_attachments)
collision_fn = PyChoreoConfigurationCollisionChecker(
self.client)._get_collision_fn(robot, joint_names, options=options)
failure_reason = ''
with WorldSaver():
# set to start conf
if start_configuration is not None:
self.client.set_robot_configuration(robot, start_configuration)
if planner_id == 'IterativeIK':
selected_links = [
link_from_name(robot_uid, l)
for l in robot.get_link_names(group=group)
]
# with HideOutput():
# with redirect_stdout():
path = plan_cartesian_motion_from_links(
robot_uid,
selected_links,
tool_link,
ee_poses,
get_sub_conf=False,
pos_tolerance=pos_tolerance,
ori_tolerance=ori_tolerance)
if path is None:
failure_reason = 'IK plan is not found.'
# collision checking is not included in the default Cartesian planning
if path is not None and avoid_collisions:
for i, conf_val in enumerate(path):
pruned_conf_val = self._prune_configuration(
robot_uid, conf_val, joint_names)
for attachment in attachments:
attachment.assign()
if collision_fn(pruned_conf_val, diagnosis=diagnosis):
failure_reason = 'IK plan is found but collision violated.'
path = None
break
path[i] = pruned_conf_val
# TODO check joint threshold
elif planner_id == 'LadderGraph':
# get ik fn from client
# collision checking is turned off because collision checking is handled inside LadderGraph planner
ik_options = {'avoid_collisions': False, 'return_all': True}
sample_ik_fn = ik_function or self._get_sample_ik_fn(
robot, ik_options)
# convert ee_variant_fn
if frame_variant_gen is not None:
def sample_ee_fn(pose):
for v_frame in frame_variant_gen.generate_frame_variant(
frame_from_pose(pose)):
yield pose_from_frame(v_frame)
else:
sample_ee_fn = None
path, cost = plan_cartesian_motion_lg(robot_uid, ik_joints, ee_poses, sample_ik_fn, collision_fn, \
jump_threshold=jump_threshold_from_joint, sample_ee_fn=sample_ee_fn)
if verbose:
print('Ladder graph cost: {}'.format(cost))
else:
raise ValueError('Cartesian planner {} not implemented!',
planner_id)
if path is None:
if verbose:
cprint(
'No Cartesian motion found, due to {}!'.format(
failure_reason), 'red')
return None
else:
# TODO start_conf might have different number of joints with the given group?
start_traj_pt = None
if start_configuration is not None:
start_traj_pt = JointTrajectoryPoint(
values=start_configuration.values,
types=start_configuration.types)
start_traj_pt.joint_names = start_configuration.joint_names
jt_traj_pts = []
for i, conf in enumerate(path):
jt_traj_pt = JointTrajectoryPoint(values=conf,
types=joint_types)
jt_traj_pt.joint_names = joint_names
if start_traj_pt is not None:
# ! TrajectoryPoint doesn't copy over joint_names...
jtp = start_traj_pt.copy()
jtp.joint_names = start_traj_pt.joint_names
jtp.merge(jt_traj_pt)
jt_traj_pt = jtp
jt_traj_pt.time_from_start = Duration(i * 1, 0)
jt_traj_pts.append(jt_traj_pt)
if start_configuration is not None and not compare_configurations(
start_configuration,
jt_traj_pts[0],
jump_threshold,
verbose=verbose):
# if verbose:
# print()
# cprint('Joint jump from start conf, max diff {}'.format(start_configuration.max_difference(jt_traj_pts[0])), 'red')
# cprint('start conf {}'.format(['{:.4f}'.format(v) for v in start_configuration.values]), 'red')
# cprint('traj pt 0 {}'.format(['{:.4f}'.format(v) for v in jt_traj_pts[0].values]), 'red')
pass
# return None
# TODO check intermediate joint jump
trajectory = JointTrajectory(
trajectory_points=jt_traj_pts,
joint_names=jt_traj_pts[0].joint_names,
start_configuration=jt_traj_pts[0],
fraction=1.0)
return trajectory
def inverse_kinematics(robot, frame_WCF, start_configuration=None, group=None, options=None):
# TODO use input here instead of class attributes
# avoid_collisions=True, constraints=None,
# attempts=8, attached_collision_meshes=None,
# return_full_configuration=False,
# cull=False,
# return_closest_to_start=False,
# return_idxs=None):
avoid_collisions = is_valid_option(options, 'avoid_collisions', True)
constraints = is_valid_option(options, 'constraints', None)
attempts = is_valid_option(options, 'attempts', 8)
attached_collision_meshes = is_valid_option(options, 'attached_collision_meshes', None)
return_full_configuration = is_valid_option(options, 'return_full_configuration', False)
cull = is_valid_option(options, 'cull', False)
return_closest_to_start = is_valid_option(options, 'return_closest_to_start', False)
return_idxs = is_valid_option(options, 'return_idxs', None)
if start_configuration:
base_frame = robot.get_base_frame(self.group, full_configuration=start_configuration)
self.update_base_transformation(base_frame)
# in_collision = robot.client.configuration_in_collision(start_configuration)
# if in_collision:
# raise ValueError("Start configuration already in collision")
# frame_tool0_RCF = self.convert_frame_wcf_to_frame_tool0_rcf(frame_WCF)
frame_tool0_RCF = Frame.from_transformation(self.base_transformation * Transformation.from_frame(frame_WCF) * self.tool_transformation)
# call the ik function
configurations = self.function(frame_tool0_RCF)
# The ik solution for 6 axes industrial robots returns by default 8
# configurations, which are sorted. That means, the if you call ik
# on 2 frames that are close to each other, and compare the 8
# configurations of the first one with the 8 of the second one at
# their respective indices, then these configurations are 'close' to
# each other. That is why for certain use cases, e.g. custom cartesian
# path planning it makes sense to keep the sorting and set the ones
# that are out of joint limits or in collison to `None`.
if return_idxs:
configurations = [configurations[i] for i in return_idxs]
# add joint names to configurations
self.add_joint_names_to_configurations(configurations)
# fit configurations within joint bounds (sets those to `None` that are not working)
self.try_to_fit_configurations_between_bounds(configurations)
# check collisions for all configurations (sets those to `None` that are not working)
# TODO defer collision checking
# if robot.client:
# robot.client.check_configurations_for_collision(configurations)
if return_closest_to_start:
diffs = [c.max_difference(start_configuration) for c in configurations if c is not None]
if len(diffs):
idx = diffs.index(min(diffs))
return configurations[idx] # only one
return None
if cull:
configurations = [c for c in configurations if c is not None]
return configurations