from .cspace import CSpace
from .. import robotsim
from ..model import collide
from .cspaceutils import EmbeddedCSpace
import math
import random
[docs]class RobotCSpace(CSpace):
"""A basic robot cspace that allows collision free motion.
Args:
robot (RobotModel): the robot that's moving.
collider (:class:`WorldCollider`, optional): a collide.WorldCollider
instance instantiated with the world in which the robot lives.
Any ignored collisions in the collider will be respected in the
feasibility tests of this CSpace.
If this is not provided, then only self-collisions will be checked.
.. warning::
If your robot has non-standard joints, like a free-
floating base or continuously rotating (spin) joints, you may need to
overload the :meth:`sample` method. The default implementation
assumes that everything with unbounded limits is a rotational joint.
"""
def __init__(self,robot,collider=None):
CSpace.__init__(self)
self.robot = robot
self.setBounds(list(zip(*robot.getJointLimits())))
self.collider = collider
self.addFeasibilityTest((lambda x: self.inJointLimits(x)),"joint limits")
def setconfig(x):
self.robot.setConfig(x)
return True
if collider:
bb0 = ([float('inf')]*3,[float('-inf')]*3)
bb = [bb0[0],bb0[1]]
def calcbb(x):
bb[0] = bb0[0]
bb[1] = bb0[1]
for i in range(self.robot.numLinks()):
g = self.robot.link(i).geometry()
if not g.empty():
bbi = g.getBB()
bb[0] = [min(a,b) for (a,b) in zip(bb[0],bbi[0])]
bb[1] = [max(a,b) for (a,b) in zip(bb[1],bbi[1])]
return True
def objCollide(o):
obb = self.collider.world.rigidObject(o).geometry().getBB()
if not collide.bb_intersect(obb,bb): return False
return any(True for _ in self.collider.robotObjectCollisions(self.robot.index,o))
def terrCollide(o):
obb = self.collider.world.terrain(o).geometry().getBB()
if not collide.bb_intersect(obb,bb): return False
return any(True for _ in self.collider.robotTerrainCollisions(self.robot.index,o))
self.addFeasibilityTest(setconfig,"setconfig")
self.addFeasibilityTest(calcbb,"calcbb",dependencies="setconfig")
self.addFeasibilityTest((lambda x: not self.selfCollision()),"self collision",dependencies="setconfig")
#self.addFeasibilityTest((lambda x: not self.envCollision()),"env collision")
for o in range(self.collider.world.numRigidObjects()):
self.addFeasibilityTest((lambda x,o=o: not objCollide(o)),"obj collision "+str(o)+" "+self.collider.world.rigidObject(o).getName(),dependencies="calcbb")
for o in range(self.collider.world.numTerrains()):
self.addFeasibilityTest((lambda x,o=o: not terrCollide(o)),"terrain collision "+str(o)+" "+self.collider.world.terrain(o).getName(),dependencies="calcbb")
else:
self.addFeasibilityTest(setconfig,"setconfig")
self.addFeasibilityTest((lambda x: not self.selfCollision()),"self collision",dependencies="setconfig")
self.joint_limit_failures = [0]*len(self.bound)
self.properties['geodesic'] = 1
[docs] def addConstraint(self,checker,name=None):
self.addFeasibilityTest(checker,name)
[docs] def sample(self):
"""Overload this to implement custom sampling strategies or to handle
non-standard joints. This one will handle spin joints and
rotational axes of floating bases."""
res = CSpace.sample(self)
for i,x in enumerate(res):
if math.isnan(x):
res[i] = random.uniform(0,math.pi*2.0)
return res
[docs] def inJointLimits(self,x):
"""Checks joint limits of the configuration x"""
for i,(xi,bi) in enumerate(zip(x,self.bound)):
if xi < bi[0] or xi > bi[1]:
self.joint_limit_failures[i] += 1
return False
return True
[docs] def selfCollision(self,x=None):
"""Checks whether the robot at its current configuration is in
self collision"""
#This should be faster than going through the collider...
if x is not None: self.robot.setConfig(x)
return self.robot.selfCollides()
#if not self.collider: return False
#return any(self.collider.robotSelfCollisions(self.robot.index))
[docs] def envCollision(self,x=None):
"""Checks whether the robot at its current configuration is in
collision with the environment."""
if not self.collider: return False
if x is not None: self.robot.setConfig(x)
for o in range(self.collider.world.numRigidObjects()):
if any(self.collider.robotObjectCollisions(self.robot.index,o)):
return True
for o in range(self.collider.world.numTerrains()):
if any(self.collider.robotTerrainCollisions(self.robot.index,o)):
return True
return False
[docs] def interpolate(self,a,b,u):
return self.robot.interpolate(a,b,u)
[docs] def distance(self,a,b):
return self.robot.distance(a,b)
[docs] def sendPathToController(self,path,controller):
"""Given a planned CSpace path 'path' and a SimRobotController 'controller',
sends the path so that it is executed correctly by the controller (this assumes
a fully actuated robot)."""
controller.setMilestone(path[0])
for q in path[1:]:
controller.appendMilestoneLinear(q)
[docs]class ClosedLoopRobotCSpace(RobotCSpace):
"""A closed loop cspace. Allows one or more IK constraints to be
maintained during the robot's motion.
Attributes:
solver (IKSolver): the solver containing all IK constraints
maxIters (int): the maximum number of iterations for numerical IK
solver
tol (float): how closely the IK constraint must be met, in meters and/
or radians
To satisfy the IK constraint, the motion planner ensures that configuration
samples are projected to the manifold of closed-loop IK solutions. To
create edges between samples a and b, the straight line path a and b is
projected to the manifold via an IK solve.
"""
def __init__(self,robot,iks,collider=None):
RobotCSpace.__init__(self,robot,collider)
self.solver = robotsim.IKSolver(robot)
if hasattr(iks,'__iter__'):
for ik in iks:
self.solver.add(ik)
else:
self.solver.add(iks)
#root finding iterations
self.maxIters = 100
self.tol = 1e-3
self.addFeasibilityTest((lambda x: self.closedLoop(x)),'closed loop constraint')
[docs] def setIKActiveDofs(self,activeSet):
"""Marks that only a subset of the DOFs of the robot are to be used for
solving the IK constraint.
Args:
activeSet (list of int): the robot DOF indices that should be active.
"""
self.solver.setActiveDofs(activeSet)
[docs] def sample(self):
"""Samples directly on the contact manifold. The basic method samples
arbitrarily in the configuration space and then solves IK constraints.
Note that this may be an ineffective method especially for floating-base
robots, since the floating joints may be sampled arbitrarily. To maximize
performance, better problem-speciifc sampling distributions should be
implemented by a subclass, if possible.
"""
x = RobotCSpace.sample(self)
res = self.solveConstraints(x)
return res
[docs] def sampleneighborhood(self,c,r):
"""Samples a neighborhood in ambient space and then projects onto the
contact manifold.
"""
x = RobotCSpace.sampleneighborhood(self,c,r)
return self.solveConstraints(x)
[docs] def solveConstraints(self,x):
"""Given an initial configuration of the robot x, attempts to solve the
IK constraints given in this space. Return value is the best
configuration found via local optimization.
"""
self.robot.setConfig(x)
self.solver.setMaxIters(self.maxIters)
self.solver.setTolerance(self.tol)
res = self.solver.solve()
return self.robot.getConfig()
[docs] def closedLoop(self,config=None,tol=None):
"""Returns true if the closed loop constraint has been met at config,
or if config==None, the robot's current configuration."""
if config is not None: self.robot.setConfig(config)
e = self.solver.getResidual()
if tol==None: tol = self.tol
return max(abs(ei) for ei in e) <= tol
[docs] def interpolate(self,a,b,u):
"""Interpolates on the manifold. Used by edge collision checking"""
x = RobotCSpace.interpolate(self,a,b,u)
return self.solveConstraints(x)
[docs] def interpolationPath(self,a,b,epsilon=1e-2):
"""Creates a discretized path on the contact manifold between the
points a and b, with resolution epsilon.
"""
d = self.distance(a,b)
nsegs = int(math.ceil(d/epsilon))
if nsegs <= 1: return [a,b]
res = [a]
for i in range(nsegs-1):
u = float(i+1)/float(nsegs)
res.append(self.interpolate(a,b,u))
res.append(b)
return res
[docs] def discretizePath(self,path,epsilon=1e-2):
"""Given a :class:`CSpace` path ``path``, generates a path that
satisfies closed-loop constraints up to the given distance between
milestones.
"""
if path is None: return None
if len(path)==0: return []
respath = [path[0]]
for a,b in zip(path[:-1],path[1:]):
respath += self.interpolationPath(a,b,epsilon)[1:]
return respath
[docs] def sendPathToController(self,path,controller,epsilon=1e-2):
"""Given a :class:`CSpace` path ``path``, sends the path to be executed
to the :class:`SimRobotController` ``controller``.
This discretizes the path and sends it as a piecewise linear curve,
limited in speed by the robot's maximum velocity.
.. note::
This isn't the best thing to do for robots with slow acceleration
limits and/or high inertias because it ignores acceleration. A
better solution can be found in the MInTOS package or the C++ code
in Klampt/Cpp/Planning/RobotTimeScaling.h.
"""
dpath = self.discretizePath(path,epsilon)
vmax = controller.model().getVelocityLimits()
assert len(dpath[0]) == len(vmax)
controller.setMilestone(dpath[0])
for a,b in zip(dpath[:-1],dpath[1:]):
dt = 0.0
for i in range(len(a)):
if vmax[i] == 0:
if a[i] != b[i]: print("ClosedLoopRobotCSpace.sendPathToController(): Warning, path moves on DOF %d with maximum velocity 0"%(i,))
else:
dt = max(dt,abs(a[i]-b[i])/vmax[i])
#this does a piecewise lienar interpolation
controller.appendLinear(dt,b)
[docs]class ImplicitManifoldRobotCSpace(RobotCSpace):
"""A closed loop cspace with an arbitrary numerical manifold f(q)=0
to constrain the robot's motion. The argument implicitConstraint
should be a function f(q) returning a list of values that should be
equal to 0 up to the given tolerance. Essentially this is a
ClosedLoopRobotCSpace except with a user-provided function.
See :class:`ClosedLoopRobotCSpace`.
"""
def __init__(self,robot,implicitConstraint,collider=None):
RobotCSpace.__init__self(robot,collider)
self.implicitConstraint = implicitConstraint
#root finding iterations
self.maxIters = 100
self.tol = 1e-3
self.addFeasibilityTest((lambda x: self.onManifold(x)),'implicit manifold constraint')
[docs] def sample(self):
"""Samples directly on the contact manifold"""
x = RobotCSpace.sample()
return self.solveManifold(x)
[docs] def onManifold(self,x,tol=None):
"""Returns true if the manifold constraint has been met at x."""
e = self.implicitConstraint.eval(x)
if tol==None: tol = self.tol
return max(abs(ei) for ei in e) <= tol
[docs] def solveManifold(self,x,tol=None,maxIters=None):
"""Solves the manifold constraint starting from x, to the given
tolerance and with the given maximum iteration count. Default
uses the values set as attributes of this class.
"""
if tol==None: tol = self.tol
if maxIters==None: maxIters = self.maxIters
from klampt import rootfind
rootfind.setXTolerance(1e-8)
rootfind.setFTolerance(tol)
rootfind.setVectorField(self.implicitConstraint)
(res,x,val) = rootfind.findRootsBounded(x,self.bound)
return x
[docs] def interpolate(self,a,b,u):
"""Interpolates on the manifold. Used by edge collision checking"""
x = RobotCSpace.interpolate(self,a,b,u)
return self.solveManifold(x)
[docs]class EmbeddedRobotCSpace(EmbeddedCSpace):
"""A basic robot cspace that allows collision free motion of a *subset*
of joints. The subset is given by the indices in the list "subset"
provided to the constructor. The configuration space is R^k where k
is the number of DOFs in the subset.
Args:
ambientspace (RobotCSpace): a RobotCSpace, ClosedLoopRobotCSpace, etc.
subset (list of ints): the indices of moving DOFs
xinit (configuration, optional): the reference configuration, or None
to use the robot's current configuration as the reference.
"""
def __init__(self,ambientspace,subset,xinit=None):
self.robot = ambientspace.robot
if xinit is None:
xinit = self.robot.getConfig()
EmbeddedCSpace.__init__(self,ambientspace,subset,xinit)
#do monkey-patching needed to make the sampler work properly for closed-loop spaces
if isinstance(ambientspace,ImplicitManifoldRobotCSpace):
from klampt import rootfind
def subsetImplicitConstraint(x):
return self.ambientSpace.implicitConstraint(self.lift(x))
def solveManifold(x):
rootfind.setXTolerance(1e-8)
rootfind.setFTolerance(self.ambientspace.tol)
rootfind.setVectorField(subsetImplicitConstraint)
(res,x,val) = rootfind.findRootsBounded(x,self.bound)
return x
def sample():
return solveManifold(self.lift(CSpace.sample(self)))
def sampleneighborhood(c,r):
return solveManifold(self.lift(CSpace.sampleneighborhood(self,c,r)))
self.sample = sample
self.sampleneighborhood = sampleneighborhood
if isinstance(ambientspace,ClosedLoopRobotCSpace):
#sanity check
activedofs = ambientspace.solver.getActiveDofs()
if len(activedofs) > len(subset):
raise ValueError("ClosedLoopRobotCSpace IK solver must be configured with moving dofs that are within the subset of embedded dofs")
elif activedofs != subset:
ssubset = set(subset)
for i in activedofs:
if i not in ssubset:
raise ValueError("ClosedLoopRobotCSpace IK solver must be configured with moving dofs that are within the subset of embedded dofs")
def sample():
xseed = self.lift(CSpace.sample(self))
return self.project(self.ambientspace.solveConstraints(xseed))
def sampleneighborhood(c,r):
xseed = self.lift(CSpace.sampleneighborhood(self,c,r))
return self.project(self.ambientspace.solveConstraints(xseed))
self.sample = sample
self.sampleneighborhood = sampleneighborhood
[docs] def disableInactiveCollisions(self):
"""This modifies the collider in ambientspace to only check collisions
between moving pairs. Should be called before `setup()` in most cases.
"""
robot = self.robot
collider = self.ambientspace.collider
subset = self.mapping
active = [False]*robot.numLinks()
for i in subset:
active[i] = True
for i in range(robot.numLinks()):
if active[robot.link(i).getParent()]:
active[i] = True
inactive = []
for i in range(robot.numLinks()):
if not active[i]:
inactive.append(i)
#disable self-collisions for inactive objects
for i in inactive:
rindices = collider.robots[robot.index]
rindex = rindices[i]
if rindex < 0:
continue
newmask = set()
for j in range(robot.numLinks()):
if rindices[j] in collider.mask[rindex] and active[j]:
newmask.add(rindices[j])
collider.mask[rindex] = newmask
[docs] def discretizePath(self,path,epsilon=1e-2):
"""Only useful for ClosedLoopRobotCSpace"""
if hasattr(self.ambientspace,'discretizePath'):
return self.ambientspace.discretizePath(self.liftPath(path),epsilon)
else:
return self.liftPath(path)
[docs] def sendPathToController(self,path,controller):
"""Sends a planned path so that it is executed correctly by the
controller (assumes a fully actuated robot).
Args:
path (list of Configs): a path in the embedded space or the
ambient space, as returned by a planner.
controller (SimRobotController): the robot's controller
"""
if len(path[0]) == len(self.mapping):
path = self.liftPath(path)
if hasattr(self.ambientspace,'discretizePath'):
path = self.ambientspace.discretizePath(path)
self.ambientspace.sendPathToController(path,controller)
[docs]class RobotSubsetCSpace(EmbeddedCSpace):
"""A basic robot cspace that allows collision free motion of a *subset*
of joints. The subset is given by the indices in the list "subset"
provided to the constructor. The configuration space is R^k where k
is the number of DOFs in the subset.
This class will automatically disable all collisions for inactive robot
links in the collider.
.. note::
To convert from start/goal robot configurations to the CSpace, call
the `project(qrobot)` method for the start and goal.
(see :meth:`EmbeddedCSpace.project`)
.. note::
To convert from a planned path back to the robot's full configuration
space, you will need to call the `lift(q)` method for all
configurations q in the planned path. (see :meth:`EmbeddedCSpace.lift`)
.. warning::
If your robot has non-standard joints, like a free-floating base or
continuously rotating (spin) joints, you will need to overload the
:meth:`sample` method.
.. deprecated:: 0.8.6
Deprecated this in favor of EmbeddedRobotCSpace, which is adaptable
to ClosedLoopRobotCSpace and ImplicitManifoldRobotCSpace. To convert
code to EmbeddedRobotCSpace, convert
``space = RobotSubsetCSpace(robot,subset,collider)`` to
``space = EmbeddedCSpace(RobotCSpace(robot,collider),subset);``
``space.disableInactiveCollisions()``
"""
def __init__(self,robot,subset,collider=None):
EmbeddedCSpace.__init__(self,RobotCSpace(robot,collider),subset,xinit=robot.getConfig())
self.collider = collider
if self.collider:
#determine moving objects, which includes all links in the subset and descendants
moving = [False]*robot.numLinks()
for i in range(robot.numLinks()):
if i in subset: moving[i] = True
else:
p = robot.link(i).getParent()
if p >= 0 and moving[p]: moving[i]=True
#disable self-collisions for non moving objects
for i,mv in enumerate(moving):
if not mv:
rindices = self.collider.robots[robot.index]
rindex = rindices[i]
if rindex < 0:
continue
newmask = set()
for j in range(robot.numLinks()):
if rindices[j] in self.collider.mask[rindex] and moving[j]:
newmask.add(rindices[j])
self.collider.mask[rindex] = newmask
[docs] def sendPathToController(self,path,controller):
"""Given a planned :class:`CSpace` path ``path`` and a
:class:`SimRobotController` ``controller``, sends the path so that it
is executed correctly by the controller.
.. note:
This assumes a fully actuated robot. It won't work for robots with
free-floating bases.
"""
lpath = self.liftPath(path)
controller.setMilestone(lpath[0])
for q in lpath[1:]:
controller.appendMilestoneLinear(q)