def adaptive_resample(x, t = None, max_err = np.inf, max_dx = np.inf, max_dt = np.inf, normfunc = None):
"""
SLOW VERSION
"""
#return np.arange(0, len(x), 100)
LOG.info("resampling a path of length %i", len(x))
x = np.asarray(x)
if x.ndim == 1: x = x[:,None]
else: assert x.ndim == 2
if normfunc is None: normfunc = np.linalg.norm
if t is None: t = np.arange(len(x))
else: t = np.asarray(t)
assert(t.ndim == 1)
n = len(t)
assert len(x) == n
import networkx as nx
g = nx.DiGraph()
g.add_nodes_from(xrange(n))
for i_src in xrange(n):
for i_targ in xrange(i_src+1, n):
normdx = normfunc(x[i_targ] - x[i_src])
dt = t[i_targ] - t[i_src]
errs = lerp(x[i_src], x[i_targ], (t[i_src:i_targ+1] - t[i_src])/dt) - x[i_src:i_targ+1]
interp_err = errs.__abs__().max()#np.array([normfunc(err) for err in errs]).max()
if normdx > max_dx or dt > max_dt or interp_err > max_err: break
g.add_edge(i_src, i_targ)
resample_inds = nx.shortest_path(g, 0, n-1)
return resample_inds
def make_joint_traj(xyzs, quats, manip, ref_frame, targ_frame, filter_options = 0):
"do ik and then fill in the points where ik failed"
n = len(xyzs)
assert len(quats) == n
robot = manip.GetRobot()
joint_inds = manip.GetArmIndices()
robot.SetActiveDOFs(joint_inds)
orig_joint = robot.GetActiveDOFValues()
joints = []
inds = []
for i in xrange(0,n):
mat4 = conv.trans_rot_to_hmat(xyzs[i], quats[i])
joint = PR2.cart_to_joint(manip, mat4, ref_frame, targ_frame, filter_options)
if joint is not None:
joints.append(joint)
inds.append(i)
robot.SetActiveDOFValues(joint)
robot.SetActiveDOFValues(orig_joint)
LOG.info("found ik soln for %i of %i points",len(inds), n)
if len(inds) > 2:
joints2 = mu.interp2d(np.arange(n), inds, joints)
return joints2, inds
else:
return np.zeros((n, len(joints))), []
def adaptive_resample(x,
t=None,
max_err=np.inf,
max_dx=np.inf,
max_dt=np.inf,
normfunc=None):
"""
SLOW VERSION
"""
#return np.arange(0, len(x), 100)
LOG.info("resampling a path of length %i", len(x))
x = np.asarray(x)
if x.ndim == 1: x = x[:, None]
else: assert x.ndim == 2
if normfunc is None: normfunc = np.linalg.norm
if t is None: t = np.arange(len(x))
else: t = np.asarray(t)
assert (t.ndim == 1)
n = len(t)
assert len(x) == n
import networkx as nx
g = nx.DiGraph()
g.add_nodes_from(xrange(n))
for i_src in xrange(n):
for i_targ in xrange(i_src + 1, n):
normdx = normfunc(x[i_targ] - x[i_src])
dt = t[i_targ] - t[i_src]
errs = lerp(
x[i_src], x[i_targ],
(t[i_src:i_targ + 1] - t[i_src]) / dt) - x[i_src:i_targ + 1]
interp_err = errs.__abs__().max(
) #np.array([normfunc(err) for err in errs]).max()
if normdx > max_dx or dt > max_dt or interp_err > max_err: break
g.add_edge(i_src, i_targ)
resample_inds = nx.shortest_path(g, 0, n - 1)
return resample_inds
def follow_body_traj(pr2, bodypart2traj, wait=True, base_frame = "/base_footprint", speed_factor=1):
LOG.info("following trajectory with bodyparts %s", " ".join(bodypart2traj.keys()))
name2part = {"lgrip":pr2.lgrip,
"rgrip":pr2.rgrip,
"larm":pr2.larm,
"rarm":pr2.rarm,
"base":pr2.base}
for partname in bodypart2traj:
if partname not in name2part:
raise Exception("invalid part name %s"%partname)
#### Go to initial positions #######
for (name, part) in name2part.items():
if name in bodypart2traj:
part_traj = bodypart2traj[name]
if name == "lgrip" or name == "rgrip":
part.set_angle(np.squeeze(part_traj)[0])
elif name == "larm" or name == "rarm":
part.goto_joint_positions(part_traj[0])
elif name == "base":
part.goto_pose(part_traj[0], base_frame)
pr2.join_all()
#### Construct total trajectory so we can retime it #######
n_dof = 0
trajectories = []
vel_limits = []
acc_limits = []
bodypart2inds = {}
for (name, part) in name2part.items():
if name in bodypart2traj:
traj = bodypart2traj[name]
if traj.ndim == 1: traj = traj.reshape(-1,1)
trajectories.append(traj)
vel_limits.extend(part.vel_limits)
acc_limits.extend(part.acc_limits)
bodypart2inds[name] = range(n_dof, n_dof+part.n_joints)
n_dof += part.n_joints
trajectories = np.concatenate(trajectories, 1)
vel_limits = np.array(vel_limits)*speed_factor
times = retiming.retime_with_vel_limits(trajectories, vel_limits)
times_up = np.linspace(0, times[-1], int(np.ceil(times[-1]/.1)))
traj_up = mu.interp2d(times_up, times, trajectories)
#### Send all part trajectories ###########
for (name, part) in name2part.items():
if name in bodypart2traj:
part_traj = traj_up[:,bodypart2inds[name]]
if name == "lgrip" or name == "rgrip":
part.follow_timed_trajectory(times_up, part_traj.flatten())
elif name == "larm" or name == "rarm":
vels = resampling.get_velocities(part_traj, times_up, .001)
part.follow_timed_joint_trajectory(part_traj, vels, times_up)
elif name == "base":
part.follow_timed_trajectory(times_up, part_traj, base_frame)
if wait: pr2.join_all()
return True
