def __init__(self, controlSpace, objective, metric, edgeChecker, **params):
"""Given a ControlSpace controlSpace, a metric, and an edge checker"""
TreePlanner.__init__(self)
self.controlSpace = controlSpace
if not isinstance(controlSpace, ControlSpace):
print "Warning, controlSpace is not a ControlSpace"
if not isinstance(edgeChecker, EdgeChecker):
print "Warning, edgeChecker is not an EdgeChecker"
self.cspace = controlSpace.configurationSpace()
self.metric = metric
self.objective = objective
self.edgeChecker = edgeChecker
self.controlSelector = RandomControlSelector(controlSpace, self.metric,
1)
self.goal = None
self.goalSampler = None
self.pChooseGoal = popdefault(params, 'pChooseGoal', 0.1)
self.goalNodes = []
self.selectionRadius = popdefault(params, 'selectionRadius', 0.1)
self.witnessRadius = popdefault(params, 'witnessRadius', 0.03)
self.witnessSet = []
self.configurationSampler = Sampler(
self.controlSpace.configurationSpace())
nnmethod = popdefault(params, 'nearestNeighborMethod', 'kdtree')
self.nearestNeighbors = NearestNeighbors(self.metric, nnmethod)
self.nearestWitness = NearestNeighbors(self.metric, nnmethod)
self.stats = Profiler()
self.numIters = self.stats.count('numIters')
self.bestPathCost = infty
self.bestPath = None
if len(params) != 0:
print "Warning, unused params", params
class StableSparseRRTStar:
def __init__(self,controlSpace,objective,metric,edgeChecker,
**params):
self.selectionRadius0 = popdefault(params,'selectionRadius',0.2)
self.witnessRadius0 = popdefault(params,'witnessRadius',0.2)
self.numIters0 = popdefault(params,'numSSTIters',300)
self.shrinkage = popdefault(params,'shrinkage',0.8)
self.ssrrt = StableSparseRRT(controlSpace,objective,metric,
edgeChecker,selectionRadius=self.selectionRadius0,witnessRadius=self.witnessRadius0,**params)
self.itersleft = self.numIters0
self.stats = Profiler()
self.stats.items['ssrrt'] = self.ssrrt.stats
self.numIters = self.stats.count('numIters')
self.restartCount = self.stats.count('restartCount')
self.bestPath = None
self.bestPathCost = infty
if len(params) != 0:
print "Warning, unused params",params
def setBoundaryConditions(self,start,goal):
self.ssrrt.setBoundaryConditions(start,goal)
def setConfigurationSampler(self,sampler):
self.ssrrt.setConfigurationSampler(sampler)
def setControlSelector(self,selector):
self.ssrrt.setControlSelector(selector)
def reset(self):
self.ssrrt.reset()
self.ssrrt.selectionRadius = self.selectionRadius0
self.ssrrt.witnessRadius = self.witnessRadius0
self.itersleft = self.numIters0
self.numIters.set(0)
self.restartCount.set(0)
self.bestPath = None
self.bestPathCost = infty
def planMore(self,iters):
res = False
for n in xrange(iters):
self.numIters += 1
if self.ssrrt.planMore(1):
res = True
if self.ssrrt.bestPathCost < self.bestPathCost:
self.bestPathCost = self.ssrrt.bestPathCost
self.bestPath = self.ssrrt.bestPath
self.itersleft -= 1
if self.itersleft <= 0:
#go to next iteration of SS-RRT
self.restartCount += 1
self.ssrrt.reset()
self.ssrrt.selectionRadius *= self.shrinkage
self.ssrrt.witnessRadius *= self.shrinkage
#formula from Algorithm 9
self.itersleft = (1.0+math.log(self.restartCount.count))*pow(self.shrinkage,-(len(self.ssrrt.root.x)+1)*self.restartCount.count)*self.numIters0
return res
def getPath(self):
return self.bestPath
def getRoadmap(self):
return self.ssrrt.getRoadmap()
class StableSparseRRTStar:
def __init__(self,controlSpace,objective,metric,edgeChecker,
selectionRadius,witnessRadius,numIters,shrinkage,
**params):
self.ssrrt = StableSparseRRT(controlSpace,objective,metric,
edgeChecker,selectionRadius=selectionRadius,witnessRadius=witnessRadius,**params)
self.selectionRadius0 = selectionRadius
self.witnessRadius0 = witnessRadius
self.numIters0 = numIters
self.shrinkage = shrinkage
self.itersleft = numIters
self.restartCount = 0
self.stats = Profiler()
self.stats.items['ssrrt'] = self.ssrrt.stats
self.numIters = self.stats.count('numIters')
self.stats.count('restartCount')
self.bestPath = None
self.bestPathCost = infty
def setBoundaryConditions(self,start,goal):
self.ssrrt.setBoundaryConditions(start,goal)
def setConfigurationSampler(self,sampler):
self.ssrrt.setConfigurationSampler(sampler)
def setControlSelector(self,selector):
self.ssrrt.setControlSelector(selector)
def reset(self):
self.ssrrt.reset()
self.ssrrt.selectionRadius = self.selectionRadius0
self.ssrrt.witnessRadius = self.witnessRadius0
self.itersleft = self.numIters0
self.numIters.set(0)
self.restartCount = 0
self.bestPath = None
self.bestPathCost = infty
def planMore(self,iters):
res = False
for n in xrange(iters):
self.numIters += 1
if self.ssrrt.planMore(1):
res = True
if self.ssrrt.bestPathCost < self.bestPathCost:
self.bestPathCost = self.ssrrt.bestPathCost
self.bestPath = self.ssrrt.bestPath
self.itersleft -= 1
if self.itersleft <= 0:
#go to next iteration of SS-RRT
self.restartCount += 1
self.ssrrt.reset()
self.ssrrt.selectionRadius *= self.shrinkage
self.ssrrt.witnessRadius *= self.shrinkage
#formula from Algorithm 9
self.itersleft = (1.0+math.log(self.restartCount))*pow(self.shrinkage,-(len(self.ssrrt.root.x)+1)*self.restartCount)*self.numIters0
return res
def getPath(self):
return self.bestPath
def getRoadmap(self):
return self.ssrrt.getRoadmap()
def __init__(self,controlSpace,objective,metric,edgeChecker,
**params):
"""Given a ControlSpace controlSpace, a metric, and an edge checker"""
TreePlanner.__init__(self)
self.controlSpace = controlSpace
if not isinstance(controlSpace,ControlSpace):
print "Warning, controlSpace is not a ControlSpace"
if not isinstance(edgeChecker,EdgeChecker):
print "Warning, edgeChecker is not an EdgeChecker"
self.cspace = controlSpace.configurationSpace()
self.metric = metric
self.objective = objective
self.edgeChecker = edgeChecker
self.controlSelector = RandomControlSelector(controlSpace,self.metric,1)
self.goal = None
self.goalSampler = None
self.pChooseGoal = popdefault(params,'pChooseGoal',0.1)
self.goalNodes = []
self.selectionRadius = popdefault(params,'selectionRadius',0.1)
self.witnessRadius = popdefault(params,'witnessRadius',0.03)
self.witnessSet = []
self.configurationSampler = Sampler(self.controlSpace.configurationSpace())
nnmethod = popdefault(params,'nearestNeighborMethod','kdtree')
self.nearestNeighbors = NearestNeighbors(self.metric,nnmethod)
self.nearestWitness = NearestNeighbors(self.metric,nnmethod)
self.stats = Profiler()
self.numIters = self.stats.count('numIters')
self.bestPathCost = infty
self.bestPath = None
if len(params) != 0:
print "Warning, unused params",params
def __init__(self,controlSpace,objective,metric,edgeChecker,
selectionRadius,witnessRadius,numIters,shrinkage,
**params):
self.ssrrt = StableSparseRRT(controlSpace,objective,metric,
edgeChecker,selectionRadius=selectionRadius,witnessRadius=witnessRadius,**params)
self.selectionRadius0 = selectionRadius
self.witnessRadius0 = witnessRadius
self.numIters0 = numIters
self.shrinkage = shrinkage
self.itersleft = numIters
self.restartCount = 0
self.stats = Profiler()
self.stats.items['ssrrt'] = self.ssrrt.stats
self.numIters = self.stats.count('numIters')
self.stats.count('restartCount')
self.bestPath = None
self.bestPathCost = infty
def __init__(self, controlSpace, objective, metric, edgeChecker, **params):
self.selectionRadius0 = popdefault(params, 'selectionRadius', 0.2)
self.witnessRadius0 = popdefault(params, 'witnessRadius', 0.2)
self.numIters0 = popdefault(params, 'numSSTIters', 300)
self.shrinkage = popdefault(params, 'shrinkage', 0.8)
self.ssrrt = StableSparseRRT(controlSpace,
objective,
metric,
edgeChecker,
selectionRadius=self.selectionRadius0,
witnessRadius=self.witnessRadius0,
**params)
self.itersleft = self.numIters0
self.stats = Profiler()
self.stats.items['ssrrt'] = self.ssrrt.stats
self.numIters = self.stats.count('numIters')
self.restartCount = self.stats.count('restartCount')
self.bestPath = None
self.bestPathCost = infty
if len(params) != 0:
print "Warning, unused params", params
def __init__(self,controlSpace,objective,metric,edgeChecker,
**params):
self.selectionRadius0 = popdefault(params,'selectionRadius',0.2)
self.witnessRadius0 = popdefault(params,'witnessRadius',0.2)
self.numIters0 = popdefault(params,'numSSTIters',300)
self.shrinkage = popdefault(params,'shrinkage',0.8)
self.ssrrt = StableSparseRRT(controlSpace,objective,metric,
edgeChecker,selectionRadius=self.selectionRadius0,witnessRadius=self.witnessRadius0,**params)
self.itersleft = self.numIters0
self.stats = Profiler()
self.stats.items['ssrrt'] = self.ssrrt.stats
self.numIters = self.stats.count('numIters')
self.restartCount = self.stats.count('restartCount')
self.bestPath = None
self.bestPathCost = infty
if len(params) != 0:
print "Warning, unused params",params
class StableSparseRRT(TreePlanner):
"""An implementation of Littlefield et al 2014 Stable Sparse RRT.
Nodes get a cost 'c' and an active flag 'active'.
The witness set consists of pairs [point,rep].
"""
def __init__(self, controlSpace, objective, metric, edgeChecker, **params):
"""Given a ControlSpace controlSpace, a metric, and an edge checker"""
TreePlanner.__init__(self)
self.controlSpace = controlSpace
if not isinstance(controlSpace, ControlSpace):
print "Warning, controlSpace is not a ControlSpace"
if not isinstance(edgeChecker, EdgeChecker):
print "Warning, edgeChecker is not an EdgeChecker"
self.cspace = controlSpace.configurationSpace()
self.metric = metric
self.objective = objective
self.edgeChecker = edgeChecker
self.controlSelector = RandomControlSelector(controlSpace, self.metric,
1)
self.goal = None
self.goalSampler = None
self.pChooseGoal = popdefault(params, 'pChooseGoal', 0.1)
self.goalNodes = []
self.selectionRadius = popdefault(params, 'selectionRadius', 0.1)
self.witnessRadius = popdefault(params, 'witnessRadius', 0.03)
self.witnessSet = []
self.configurationSampler = Sampler(
self.controlSpace.configurationSpace())
nnmethod = popdefault(params, 'nearestNeighborMethod', 'kdtree')
self.nearestNeighbors = NearestNeighbors(self.metric, nnmethod)
self.nearestWitness = NearestNeighbors(self.metric, nnmethod)
self.stats = Profiler()
self.numIters = self.stats.count('numIters')
self.bestPathCost = infty
self.bestPath = None
if len(params) != 0:
print "Warning, unused params", params
def destroy(self):
TreePlanner.destroy(self)
self.goalNodes = []
self.witnessSet = []
def reset(self):
"""Re-initializes the RRT* to the same start / goal, clears the
planning tree."""
x0 = self.root.x
goal = self.goal
self.bestPathCost = infty
self.bestPath = None
self.destroy()
self.setBoundaryConditions(x0, goal)
self.numIters.set(0)
def setBoundaryConditions(self, x0, goal):
"""Initializes the tree from a start state x0 and a goal
ConfigurationSubset.
goal can be set to None to just explore.
"""
self.setRoot(x0)
self.root.c = 0
self.root.active = True
self.witnessSet = [[x0, self.root]]
self.goal = goal
if goal != None:
if isinstance(goal, (list, tuple)):
self.goal = SingletonSubset(self.cspace, goal)
self.goalSampler = SubsetSampler(self.cspace, self.goal)
self.nearestNeighbors.reset()
self.nearestNeighbors.add(x0, self.root)
self.nearestWitness.reset()
self.nearestWitness.add(x0, self.witnessSet[0])
def setConfigurationSampler(self, sampler):
self.configurationSampler = sampler
def setControlSelector(self, selector):
self.controlSelector = selector
def planMore(self, iters):
for n in xrange(iters):
self.numIters += 1
n = self.expand()
if n != None and self.goal != None:
if self.goal.contains(n.x):
self.goalNodes.append(n)
if n.c + self.objective.terminal(n.x) < self.bestPathCost:
self.bestPathCost = n.c + self.objective.terminal(n.x)
print "New goal node with cost", self.bestPathCost
self.bestPath = TreePlanner.getPath(self, n)
if self.bestPath == None:
print "Uh... no path to goal?"
return True
return False
def expand(self):
"""Expands the tree via the Sparse-Stable-RRT technique.
Returns the new node or None otherwise."""
if self.goalSampler and random.uniform(0.0, 1.0) < self.pChooseGoal:
xrand = self.goalSampler.sample()
else:
xrand = self.configurationSampler.sample()
#self.stats.stopwatch('pickNode').begin()
nnear = self.pickNode(xrand)
#self.stats.stopwatch('pickNode').end()
if nnear == None:
return None
#self.stats.stopwatch('selectControl').begin()
u = self.controlSelector.select(nnear.x, xrand)
#self.stats.stopwatch('selectControl').end()
#self.stats.stopwatch('edgeCheck').begin()
edge = self.controlSpace.interpolator(nnear.x, u)
if not self.edgeChecker.feasible(edge):
#self.stats.stopwatch('edgeCheck').end()
#self.stats.count('numInfeasible').add(1)
return None
#self.stats.stopwatch('edgeCheck').end()
newcost = nnear.c + self.objective.incremental(nnear.x, u)
#feasible edge, add it
nnew = self.addEdge(nnear, u, edge)
nnew.c = newcost
nnew.active = True
#self.stats.stopwatch('sst-domination-check').begin()
localbest = self.nodeLocallyBest(nnew)
#self.stats.stopwatch('sst-domination-check').end()
if localbest == False:
#dominated by some other node
#self.stats.count('numDominated').add(1)
assert nnew == self.nodes[-1]
nnew.destroy()
self.nodes.pop(-1)
return None
self.nearestNeighbors.add(nnew.x, nnew)
#self.doPruning(nnew,localbest[1])
#self.stats.stopwatch('sst-domination-pruning').begin()
for s in localbest[1]:
self.doPruning(nnew, s)
#self.stats.stopwatch('sst-domination-pruning').end()
return nnew
def nodeLocallyBest(self, n):
"""Tests the node n against nearby witnesses. If no witnesses
are nearby, n gets added as a witness. Algorithm 7"""
res = self.nearestWitness.nearest(n.x)
assert res != None
s = res[1]
if self.metric(s[0], n.x) > self.witnessRadius:
#self.stats.count('numWitnesses').add(1)
self.witnessSet.append([n.x, None])
self.nearestWitness.add(n.x, self.witnessSet[-1])
return True, self.witnessSet[-1]
wlist = self.nearestWitness.neighbors(n.x, self.witnessRadius)
better = []
for res in wlist:
s = res[1]
if s[1] == None: better.append(s)
if n.c < s[1].c: better.append(s)
if len(better) == 0: return False
return True, better
#if s[1] == None: return True,s
#if n.c < s[1].c: return True,s
return False
def doPruning(self, n, snearest=None):
"""Algorithm 8"""
if snearest != None:
res = self.nearestWitness.nearest(n.x)
assert res != None
snearest = res[1]
npeer = snearest[1]
if npeer == n: return
snearest[1] = n
if npeer != None:
#self.stats.count('numPruned').add(1)
npeer.active = False
#print "Remove",c
while npeer != None and len(
npeer.children) == 0 and not npeer.active:
#self.stats.count('numDeleted').add(1)
p = npeer.parent
#remove from NN data structure?
cnt = self.nearestNeighbors.remove(npeer.x, npeer)
#print "Remove",c
npeer.destroy()
npeer = p
def pickNode(self, xrand):
"""Picks a within distance selectionRadius of xrand with the lowest
cost. Algorithm 6"""
res = self.nearestNeighbors.neighbors(xrand, self.selectionRadius)
activenear = []
for pt, n in res:
if n.active: activenear.append(n)
if len(activenear) == 0:
#return nearest of active neighbors
res = self.nearestNeighbors.nearest(xrand,
lambda pt, n: not n.active)
if res == None: return None
return res[1]
else:
#return node with minimum distance
cmin = infty
res = None
for n in activenear:
if n.c < cmin:
res = n
cmin = n.c
return res
def getPath(self):
return self.bestPath
class RRTStar(TreePlanner):
"""The RRT* planner with a kinematic space
Stores a tree of Nodes. Expands the tree at random using the RRT strategy
with a goal bias of probability pChooseGoal.
"""
def __init__(self, cspace, metric, edgeChecker, **params):
"""Given a ControlSpace controlSpace, a metric, and an edge checker"""
TreePlanner.__init__(self)
if not isinstance(cspace, ConfigurationSpace):
print "Warning, cspace is not a ConfigurationSpace"
if not isinstance(edgeChecker, EdgeChecker):
print "Warning, edgeChecker is not an EdgeChecker"
self.cspace = cspace
self.metric = metric
self.edgeChecker = edgeChecker
self.goal = None
self.goalSampler = None
self.pChooseGoal = popdefault(params, 'pChooseGoal', 0.1)
self.goalNodes = []
self.configurationSampler = Sampler(self.cspace)
nnmethod = popdefault(params, 'nearestNeighborMethod', 'kdtree')
self.nearestNeighbors = NearestNeighbors(self.metric, nnmethod)
self.bestPathCost = infty
self.bestPath = None
if len(params) != 0:
print "Warning, unused params", params
self.stats = Profiler()
self.numIters = self.stats.count('numIters')
def destroy(self):
TreePlanner.destroy(self)
self.nearestNeighbors.reset()
self.goalNodes = []
def reset(self):
"""Re-initializes the RRT* to the same start / goal, clears the
planning tree."""
x0 = self.root.x
goal = self.goal
self.bestPathCost = infty
self.bestPath = None
self.destroy()
self.setBoundaryConditions(x0, goal)
self.numIters.set(0)
def setBoundaryConditions(self, x0, goal):
"""Initializes the tree from a start state x0 and a goal
ConfigurationSubset.
goal can be set to None to just explore.
"""
self.setRoot(x0)
self.root.c = 0
self.goal = goal
if goal != None:
if isinstance(goal, (list, tuple)):
self.goal = SingletonSubset(self.cspace, goal)
self.goalSampler = SubsetSampler(self.cspace, self.goal)
self.nearestNeighbors.add(x0, self.root)
def setConfigurationSampler(self, sampler):
self.configurationSampler = sampler
def planMore(self, iters):
for n in xrange(iters):
self.numIters.add(1)
n = self.expand()
if n != None and self.goal != None:
if self.goal.contains(n.x):
self.goalNodes.append(n)
if n.c < self.bestPathCost:
self.bestPathCost = n.c
self.bestPath = self.getPath(n)
return True
return False
def expand(self):
"""Expands the tree via the RRT* technique. Returns the new node
or None otherwise."""
if self.goalSampler and random.uniform(0.0, 1.0) < self.pChooseGoal:
xrand = self.goalSampler.sample()
else:
xrand = self.configurationSampler.sample()
nnear = self.pickNode(xrand)
if nnear == None:
return None
if nnear.c > self.bestPathCost:
return None
#do rrt* connection
N = float(self.numIters.count + 1)
rad = 1.414 * (math.log(N) / N)**(1.0 / len(nnear.x))
k = int(((1.0 + 1.0 / len(xrand)) * math.e) * math.log(N))
if k <= 0: k = 1
u = min(1.0, rad / self.metric(nnear.x, xrand))
xend = self.cspace.interpolate(nnear.x, xrand, u)
edge = self.cspace.interpolator(nnear.x, xend)
if not self.edgeChecker.feasible(edge):
return None
#feasible edge, add it
nnew = self.addEdge(nnear, xend, edge)
nnew.c = nnear.c + edge.length()
self.nearestNeighbors.add(nnew.x, nnew)
self.rewire(nnew, k=k, rad=None, first=True, recursive=False)
return nnew
def rewire(self, n, k=None, rad=None, first=True, recursive=False):
#gather incoming cost from parent
changed = False
if n.parent != None:
d = n.parent.c + self.metric(n.x, n.parent.x)
if d != n.c:
#print "initial cost improvement",n.c,"to",d
n.c = d
changed = True
if changed:
#self.stats.count('numCostImprovements').add(1)
pass
if first or recursive:
if rad != None:
neighbors = self.nearestNeighbors.neighbors(n.x, rad)
elif k != None:
neighbors = self.nearestNeighbors.knearest(n.x, k)
else:
raise ValueError(
"Rewire: must have either k or rad arguments provided")
#sort the neighbors by accumulated cost
dneighbors = []
#print "Node",n.x,"cost",n.c
for x, nn in neighbors:
if nn == n: continue
if nn == n.parent: continue
d = self.metric(n.x, nn.x) + nn.c
#print nn.x,d,self.metric(n.x,nn.x)
dneighbors.append((d, nn))
dneighbors = sorted(dneighbors, key=lambda x: x[0])
#print len(dneighbors),"neighbors, cost range",dneighbors[0][0],"to",dneighbors[-1][0]
#add incoming edges from neighbors
for (d, nn) in dneighbors:
if d > n.c: break
edge = self.cspace.interpolator(nn.x, n.x)
if self.edgeChecker.feasible(edge):
#print "rrt* cost improvement",n.c,"to",d
n.setParent(nn, n.x, edge)
n.c = d
changed = True
#optimization: break on first added neighbor, since they're
#sorted by increasing cost
if changed:
#self.stats.count('numIncomingRewirings').add(1)
pass
break
if n.c > self.bestPathCost:
return
if first or changed:
for c in n.children:
self.rewire(c, k=k, rad=rad, first=False, recursive=recursive)
if first or recursive:
#add outgoing edges from n
for (d, nn) in dneighbors:
newcost = n.c + self.metric(n.x, nn.x)
if newcost < nn.c:
#print "Outgoing improvement from",nn.c,"to",newcost
#potential improvement, check edge and rewire if necessary
edge = self.cspace.interpolator(n.x, nn.x)
if self.edgeChecker.feasible(edge):
#self.stats.count('numOutgoingRewirings').add(1)
nn.setParent(n, nn.x, edge)
#nn.c = newcost
self.rewire(nn,
k=k,
rad=rad,
first=False,
recursive=recursive)
def pickNode(self, xrand):
"""Picks a node closest to xrand. If dynamicDomain is True,
uses the radius associated with the node"""
res = self.nearestNeighbors.nearest(xrand)
if res == None: return None
return res[1]
def getPath(self, n=None):
if n == None:
return self.bestPath
return TreePlanner.getPath(self, n)
class StableSparseRRT(TreePlanner):
"""An implementation of Littlefield et al 2014 Stable Sparse RRT.
Nodes get a cost 'c' and an active flag 'active'.
The witness set consists of pairs [point,rep].
"""
def __init__(self,controlSpace,objective,metric,edgeChecker,
**params):
"""Given a ControlSpace controlSpace, a metric, and an edge checker"""
TreePlanner.__init__(self)
self.controlSpace = controlSpace
if not isinstance(controlSpace,ControlSpace):
print "Warning, controlSpace is not a ControlSpace"
if not isinstance(edgeChecker,EdgeChecker):
print "Warning, edgeChecker is not an EdgeChecker"
self.cspace = controlSpace.configurationSpace()
self.metric = metric
self.objective = objective
self.edgeChecker = edgeChecker
self.controlSelector = RandomControlSelector(controlSpace,self.metric,1)
self.goal = None
self.goalSampler = None
self.pChooseGoal = popdefault(params,'pChooseGoal',0.1)
self.goalNodes = []
self.selectionRadius = popdefault(params,'selectionRadius',0.1)
self.witnessRadius = popdefault(params,'witnessRadius',0.03)
self.witnessSet = []
self.configurationSampler = Sampler(self.controlSpace.configurationSpace())
nnmethod = popdefault(params,'nearestNeighborMethod','kdtree')
self.nearestNeighbors = NearestNeighbors(self.metric,nnmethod)
self.nearestWitness = NearestNeighbors(self.metric,nnmethod)
self.stats = Profiler()
self.numIters = self.stats.count('numIters')
self.bestPathCost = infty
self.bestPath = None
if len(params) != 0:
print "Warning, unused params",params
def destroy(self):
TreePlanner.destroy(self)
self.goalNodes = []
self.witnessSet = []
def reset(self):
"""Re-initializes the RRT* to the same start / goal, clears the
planning tree."""
x0 = self.root.x
goal = self.goal
self.bestPathCost = infty
self.bestPath = None
self.destroy()
self.setBoundaryConditions(x0,goal)
self.numIters.set(0)
def setBoundaryConditions(self,x0,goal):
"""Initializes the tree from a start state x0 and a goal
ConfigurationSubset.
goal can be set to None to just explore.
"""
self.setRoot(x0)
self.root.c = 0
self.root.active = True
self.witnessSet = [[x0,self.root]]
self.goal = goal
if goal != None:
if isinstance(goal,(list,tuple)):
self.goal = SingletonSubset(self.cspace,goal)
self.goalSampler = SubsetSampler(self.cspace,self.goal)
self.nearestNeighbors.reset()
self.nearestNeighbors.add(x0,self.root)
self.nearestWitness.reset()
self.nearestWitness.add(x0,self.witnessSet[0])
def setConfigurationSampler(self,sampler):
self.configurationSampler = sampler
def setControlSelector(self,selector):
self.controlSelector = selector
def planMore(self,iters):
for n in xrange(iters):
self.numIters += 1
n = self.expand()
if n != None and self.goal != None:
if self.goal.contains(n.x):
self.goalNodes.append(n)
if n.c + self.objective.terminal(n.x) < self.bestPathCost:
self.bestPathCost = n.c + self.objective.terminal(n.x)
print "New goal node with cost",self.bestPathCost
self.bestPath = TreePlanner.getPath(self,n)
if self.bestPath == None:
print "Uh... no path to goal?"
return True
return False
def expand(self):
"""Expands the tree via the Sparse-Stable-RRT technique.
Returns the new node or None otherwise."""
if self.goalSampler and random.uniform(0.0,1.0) < self.pChooseGoal:
xrand = self.goalSampler.sample()
else:
xrand = self.configurationSampler.sample()
#self.stats.stopwatch('pickNode').begin()
nnear = self.pickNode(xrand)
#self.stats.stopwatch('pickNode').end()
if nnear == None:
return None
#self.stats.stopwatch('selectControl').begin()
u = self.controlSelector.select(nnear.x,xrand)
#self.stats.stopwatch('selectControl').end()
#self.stats.stopwatch('edgeCheck').begin()
edge = self.controlSpace.interpolator(nnear.x,u)
if not self.edgeChecker.feasible(edge):
#self.stats.stopwatch('edgeCheck').end()
#self.stats.count('numInfeasible').add(1)
return None
#self.stats.stopwatch('edgeCheck').end()
newcost = nnear.c + self.objective.incremental(nnear.x,u)
#feasible edge, add it
nnew = self.addEdge(nnear,u,edge)
nnew.c = newcost
nnew.active = True
#self.stats.stopwatch('sst-domination-check').begin()
localbest = self.nodeLocallyBest(nnew)
#self.stats.stopwatch('sst-domination-check').end()
if localbest == False:
#dominated by some other node
#self.stats.count('numDominated').add(1)
assert nnew == self.nodes[-1]
nnew.destroy()
self.nodes.pop(-1)
return None
self.nearestNeighbors.add(nnew.x,nnew)
#self.doPruning(nnew,localbest[1])
#self.stats.stopwatch('sst-domination-pruning').begin()
for s in localbest[1]:
self.doPruning(nnew,s)
#self.stats.stopwatch('sst-domination-pruning').end()
return nnew
def nodeLocallyBest(self,n):
"""Tests the node n against nearby witnesses. If no witnesses
are nearby, n gets added as a witness. Algorithm 7"""
res = self.nearestWitness.nearest(n.x)
assert res != None
s = res[1]
if self.metric(s[0],n.x) > self.witnessRadius:
#self.stats.count('numWitnesses').add(1)
self.witnessSet.append([n.x,None])
self.nearestWitness.add(n.x,self.witnessSet[-1])
return True,self.witnessSet[-1]
wlist = self.nearestWitness.neighbors(n.x,self.witnessRadius)
better = []
for res in wlist:
s = res[1]
if s[1] == None: better.append(s)
if n.c < s[1].c: better.append(s)
if len(better)==0: return False
return True,better
#if s[1] == None: return True,s
#if n.c < s[1].c: return True,s
return False
def doPruning(self,n,snearest = None):
"""Algorithm 8"""
if snearest != None:
res = self.nearestWitness.nearest(n.x)
assert res != None
snearest = res[1]
npeer = snearest[1]
if npeer == n: return
snearest[1] = n
if npeer != None:
#self.stats.count('numPruned').add(1)
npeer.active = False
#print "Remove",c
while npeer != None and len(npeer.children)==0 and not npeer.active:
#self.stats.count('numDeleted').add(1)
p = npeer.parent
#remove from NN data structure?
cnt = self.nearestNeighbors.remove(npeer.x,npeer)
#print "Remove",c
npeer.destroy()
npeer = p
def pickNode(self,xrand):
"""Picks a within distance selectionRadius of xrand with the lowest
cost. Algorithm 6"""
res = self.nearestNeighbors.neighbors(xrand,self.selectionRadius)
activenear = []
for pt,n in res:
if n.active: activenear.append(n)
if len(activenear) == 0:
#return nearest of active neighbors
res = self.nearestNeighbors.nearest(xrand,lambda pt,n:not n.active)
if res ==None: return None
return res[1]
else:
#return node with minimum distance
cmin = infty
res = None
for n in activenear:
if n.c < cmin:
res = n
cmin = n.c
return res
def getPath(self):
return self.bestPath
class RRTStar(TreePlanner):
"""The RRT* planner with a kinematic space
Stores a tree of Nodes. Expands the tree at random using the RRT strategy
with a goal bias of probability pChooseGoal.
"""
def __init__(self,cspace,metric,edgeChecker,
**params):
"""Given a ControlSpace controlSpace, a metric, and an edge checker"""
TreePlanner.__init__(self)
if not isinstance(cspace,ConfigurationSpace):
print "Warning, cspace is not a ConfigurationSpace"
if not isinstance(edgeChecker,EdgeChecker):
print "Warning, edgeChecker is not an EdgeChecker"
self.cspace = cspace
self.metric = metric
self.edgeChecker = edgeChecker
self.goal = None
self.goalSampler = None
self.pChooseGoal = popdefault(params,'pChooseGoal',0.1)
self.goalNodes = []
self.configurationSampler = Sampler(self.cspace)
nnmethod = popdefault(params,'nearestNeighborMethod','kdtree')
self.nearestNeighbors = NearestNeighbors(self.metric,nnmethod)
self.bestPathCost = infty
self.bestPath = None
if len(params) != 0:
print "Warning, unused params",params
self.stats = Profiler()
self.numIters = self.stats.count('numIters')
def destroy(self):
TreePlanner.destroy(self)
self.nearestNeighbors.reset()
self.goalNodes = []
def reset(self):
"""Re-initializes the RRT* to the same start / goal, clears the
planning tree."""
x0 = self.root.x
goal = self.goal
self.bestPathCost = infty
self.bestPath = None
self.destroy()
self.setBoundaryConditions(x0,goal)
self.numIters.set(0)
def setBoundaryConditions(self,x0,goal):
"""Initializes the tree from a start state x0 and a goal
ConfigurationSubset.
goal can be set to None to just explore.
"""
self.setRoot(x0)
self.root.c = 0
self.goal = goal
if goal != None:
if isinstance(goal,(list,tuple)):
self.goal = SingletonSubset(self.cspace,goal)
self.goalSampler = SubsetSampler(self.cspace,self.goal)
self.nearestNeighbors.add(x0,self.root)
def setConfigurationSampler(self,sampler):
self.configurationSampler = sampler
def planMore(self,iters):
for n in xrange(iters):
self.numIters.add(1)
n = self.expand()
if n != None and self.goal != None:
if self.goal.contains(n.x):
self.goalNodes.append(n)
if n.c < self.bestPathCost:
self.bestPathCost = n.c
self.bestPath = self.getPath(n)
return True
return False
def expand(self):
"""Expands the tree via the RRT* technique. Returns the new node
or None otherwise."""
if self.goalSampler and random.uniform(0.0,1.0) < self.pChooseGoal:
xrand = self.goalSampler.sample()
else:
xrand = self.configurationSampler.sample()
nnear = self.pickNode(xrand)
if nnear == None:
return None
if nnear.c > self.bestPathCost:
return None
#do rrt* connection
N = float(self.numIters.count+1)
rad = 1.414*(math.log(N)/N) ** (1.0/len(nnear.x));
k = int(((1.0+1.0/len(xrand))*math.e)*math.log(N))
if k <=0: k=1
u = min(1.0,rad / self.metric(nnear.x,xrand))
xend = self.cspace.interpolate(nnear.x,xrand,u)
edge = self.cspace.interpolator(nnear.x,xend)
if not self.edgeChecker.feasible(edge):
return None
#feasible edge, add it
nnew = self.addEdge(nnear,xend,edge)
nnew.c = nnear.c + edge.length()
self.nearestNeighbors.add(nnew.x,nnew)
self.rewire(nnew,k=k,rad=None,first=True,recursive=False)
return nnew
def rewire(self,n,k=None,rad=None,first=True,recursive=False):
#gather incoming cost from parent
changed = False
if n.parent != None:
d = n.parent.c + self.metric(n.x,n.parent.x)
if d != n.c:
#print "initial cost improvement",n.c,"to",d
n.c = d
changed = True
if changed:
#self.stats.count('numCostImprovements').add(1)
pass
if first or recursive:
if rad != None:
neighbors = self.nearestNeighbors.neighbors(n.x,rad)
elif k != None:
neighbors = self.nearestNeighbors.knearest(n.x,k)
else:
raise ValueError("Rewire: must have either k or rad arguments provided")
#sort the neighbors by accumulated cost
dneighbors = []
#print "Node",n.x,"cost",n.c
for x,nn in neighbors:
if nn == n: continue
if nn == n.parent: continue
d = self.metric(n.x,nn.x) + nn.c
#print nn.x,d,self.metric(n.x,nn.x)
dneighbors.append((d,nn))
dneighbors = sorted(dneighbors,key=lambda x:x[0])
#print len(dneighbors),"neighbors, cost range",dneighbors[0][0],"to",dneighbors[-1][0]
#add incoming edges from neighbors
for (d,nn) in dneighbors:
if d > n.c: break
edge = self.cspace.interpolator(nn.x,n.x)
if self.edgeChecker.feasible(edge):
#print "rrt* cost improvement",n.c,"to",d
n.setParent(nn,n.x,edge)
n.c = d
changed = True
#optimization: break on first added neighbor, since they're
#sorted by increasing cost
if changed:
#self.stats.count('numIncomingRewirings').add(1)
pass
break
if n.c > self.bestPathCost:
return
if first or changed:
for c in n.children:
self.rewire(c,k=k,rad=rad,first=False,recursive=recursive)
if first or recursive:
#add outgoing edges from n
for (d,nn) in dneighbors:
newcost = n.c + self.metric(n.x,nn.x)
if newcost < nn.c:
#print "Outgoing improvement from",nn.c,"to",newcost
#potential improvement, check edge and rewire if necessary
edge = self.cspace.interpolator(n.x,nn.x)
if self.edgeChecker.feasible(edge):
#self.stats.count('numOutgoingRewirings').add(1)
nn.setParent(n,nn.x,edge)
#nn.c = newcost
self.rewire(nn,k=k,rad=rad,first=False,recursive=recursive)
def pickNode(self,xrand):
"""Picks a node closest to xrand. If dynamicDomain is True,
uses the radius associated with the node"""
res = self.nearestNeighbors.nearest(xrand)
if res==None: return None
return res[1]
def getPath(self,n=None):
if n == None:
return self.bestPath
return TreePlanner.getPath(self,n)