def __init__(self,
space,
x_bound=1.0,
y_bound=1.0,
milestones=(0, 0),
initial_points=4000):
self.space = space
#PRM planner
MotionPlan.setOptions(type="prm",
knn=10,
connectionThreshold=0.05,
ignoreConnectedComponents=True)
self.optimizingPlanner = False
# we first plan a bit without goals just to have a good skeleton
self.initial_points = initial_points
self.x_bound = x_bound
self.y_bound = y_bound
self.milestones = milestones
self.times = 0
self.planner = MotionPlan(space)
print('planning initial roadmap with {} points'.format(initial_points))
self.planner.planMore(self.initial_points)
self.connected = False
# we now add each of the chosen points as a milestone:
self.G = self.planner.getRoadmap()
self.start_milestones = len(self.G[0])
for milestone in self.milestones:
self.planner.addMilestone(milestone)
self.components = int(self.planner.getStats()['numComponents'])
print(self.components)
self.path = []
self.G = None
def makePlanner(space, start, goal):
"""Creates a MotionPlan object for the given space, start, and goal.
Returns (planner,optimizing) where optimizing is True if the planner should
continue be run after the first solution path has been found"""
#TODO: In lab3c, you should tune these parameters
#
#This sets a Probabilistic Road Map (PRM) planner that connects
#a random point to its 10 nearest neighbors. If knn is set to 0,
#the points are connected as long as they lie
#within distance 0.1 of one another
# MotionPlan.setOptions(type="prm",knn=10,connectionThreshold=0.1)
#This line sets a Rapidly-exploring Random Tree (RRT) planner that
#repeatedly extends the tree toward a random point at maximum
#distance 0.25. It uses the bidirectional=True option, which grows
#trees from both the start and the goal
#MotionPlan.setOptions(type="rrt",connectionThreshold=0.1,perturbationRadius=0.25,bidirectional=True)
optimizing = False
#Optimizing planners, for use in Lab3C, part 2. Make sure to uncomment optimizing = True below.
#This sets the PRM algorithm with shortcutting
#MotionPlan.setOptions(type="prm",knn=10,connectionThreshold=0.1,shortcut=True)
#This sets the RRT* algorithm
#MotionPlan.setOptions(type="rrt*",connectionThreshold=0.1,perturbationRadius=0.25)
#This sets a fast-marching method algorithm
#MotionPlan.setOptions(type="fmm*")
#This sets a random-restart + shortcutting RRT
MotionPlan.setOptions(type="rrt",connectionThreshold=0.1,perturbationRadius=0.25,bidirectional=True,restart=True,shortcut=True)
optimizing = True
#create the planner and return it along with the termination criterion
planner = MotionPlan(space)
return planner,optimizing
def makePlanner(space, start, goal):
"""Creates a MotionPlan object for the given space, start, and goal.
Returns (planner,optimizing) where optimizing is True if the planner should
continue be run after the first solution path has been found"""
#This sets a Probabilistic Road Map (PRM) planner that connects
#a random point to its 10 nearest neighbors. If knn is set to 0,
#the points are connected as long as they lie
#within distance 0.1 of one another
# MotionPlan.setOptions(type="prm",knn=10,connectionThreshold=1)
#This line sets a Rapidly-exploring Random Tree (RRT) planner that
#repeatedly extends the tree toward a random point at maximum
#distance 0.25. It uses the bidirectional=True option, which grows
#trees from both the start and the goal
#MotionPlan.setOptions(type="rrt",connectionThreshold=2.0,perturbationRadius=2.5,bidirectional=True)
#MotionPlan.setOptions(type="sbl",connectionThreshold=5.0,gridResolution=1.0,perturbationRadius=1.5,bidirectional=True)
optimizing = False
#Optimizing planners. Make sure to uncomment optimizing = True below.
#This sets the PRM algorithm with shortcutting
# MotionPlan.setOptions(type="prm",knn=10,connectionThreshold=1.0,shortcut=True)
#This sets the RRT* algorithm
MotionPlan.setOptions(type="rrt*",
connectionThreshold=2.0,
perturbationRadius=2.5)
#This sets a fast-marching method algorithm (Note: this does not work properly with rotations)
#MotionPlan.setOptions(type="fmm*")
#This sets a random-restart + shortcutting RRT
# MotionPlan.setOptions(type="rrt",connectionThreshold=2.0,perturbationRadius=2.5,bidirectional=True,restart=True,shortcut=True,restartTermCond="{foundSolution:1,maxIters:1000}")
optimizing = True
#create the planner, and return it along with the termination criterion
planner = MotionPlan(space)
return planner, optimizing
def planTransit(world, objectIndex, hand):
globals = Globals(world)
cspace = TransitCSpace(globals, hand)
obj = world.rigidObject(objectIndex)
robot = world.robot(0)
qmin, qmax = robot.getJointLimits()
#get the start config
q0 = robot.getConfig()
q0arm = [q0[i] for i in hand.armIndices]
if not cspace.feasible(q0arm):
print "Warning, arm start configuration is infeasible"
#get the pregrasp config -- TODO: what if the ik solver doesn't work?
qpregrasp = None
qpregrasparm = None
solver = hand.ikSolver(robot, obj.getTransform()[1], [0, 0, 1])
print "Trying to find pregrasp config..."
solver.setMaxIters(100)
solver.setTolerance(1e-3)
res = solver.solve()
if res:
qpregrasp = robot.getConfig()
qpregrasparm = [qpregrasp[i] for i in hand.armIndices]
if not cspace.feasible(qpregrasparm):
print "Pregrasp config infeasible"
cspace.close()
return None
if qpregrasp == None:
print "Pregrasp solve failed"
cspace.close()
return None
print "Planning transit motion to pregrasp config..."
MotionPlan.setOptions(connectionThreshold=5.0, perturbationRadius=0.5)
planner = MotionPlan(cspace, 'sbl')
planner.setEndpoints(q0arm, qpregrasparm)
iters = 0
step = 10
while planner.getPath() == None and iters < 1000:
planner.planMore(step)
iters += step
cspace.close()
if planner.getPath() == None:
print "Failed finding transit path"
return None
print "Success, found path with", len(planner.getPath()), "milestones"
#lift arm path to whole configuration space path
path = []
for qarm in planner.getPath():
path.append(q0[:])
for qi, i in zip(qarm, hand.armIndices):
path[-1][i] = qi
#add a path to the grasp configuration
return path + [hand.open(path[-1], 0)]
def init():
space, s, g = spawnFunc()
if PLANNER_TYPE == 'prm_neighborhood':
plan = MotionPlan(space,
type='prm',
connectionThreshold=0.1,
ignoreConnectedComponents=True)
elif PLANNER_TYPE == 'prm_knn':
plan = MotionPlan(space,
type='prm',
knn=5,
ignoreConnectedComponents=True)
elif PLANNER_TYPE == 'prmstar':
plan = MotionPlan(space, type='prm*')
elif PLANNER_TYPE == 'rrtstar':
plan = MotionPlan(space, type='rrt*', bidirectional=False)
else:
raise ValueError("Invalid PLANNER_TYPE")
plan.setEndpoints(s, g)
def planTransfer(world, objectIndex, hand, shift):
"""Plan a transfer path for the robot given in world, which is currently
holding the object indexed by objectIndex in the hand hand.
The desired motion should translate the object by shift without rotating
the object.
"""
globals = Globals(world)
obj = world.rigidObject(objectIndex)
cspace = TransferCSpace(globals, hand, obj)
robot = world.robot(0)
qmin, qmax = robot.getJointLimits()
#get the start config
q0 = robot.getConfig()
q0arm = [q0[i] for i in hand.armIndices]
if not cspace.feasible(q0arm):
print "Warning, arm start configuration is infeasible"
print "TODO: Complete 2.a to bypass this error"
raw_input()
cspace.close()
return None
#TODO: get the ungrasp config using an IK solver
qungrasp = None
qungrasparm = None
print "TODO: Complete 2.b to find a feasible ungrasp config"
raw_input()
solver = hand.ikSolver(robot, vectorops.add(obj.getTransform()[1], shift),
(0, 0, 1))
#plan the transfer path between q0arm and qungrasparm
print "Planning transfer motion to ungrasp config..."
MotionPlan.setOptions(connectionThreshold=5.0, perturbationRadius=0.5)
planner = MotionPlan(cspace, 'sbl')
planner.setEndpoints(q0arm, qungrasparm)
#TODO: do the planning
print "TODO: Complete 2.c to find a feasible transfer path"
raw_input()
cspace.close()
#lift arm path to whole configuration space path
path = []
for qarm in planner.getPath():
path.append(q0[:])
for qi, i in zip(qarm, hand.armIndices):
path[-1][i] = qi
qpostungrasp = hand.open(qungrasp, 1.0)
return path + [qpostungrasp]
def __init__(self, space, start, goal, initial_points=1000):
GLProgram.__init__(self)
self.space = space
#PRM planner
MotionPlan.setOptions(
type="prm*", knn=10, connectionThreshold=0.2
) # Change type based on what planner you want to use
self.optimizingPlanner = True
self.planner = MotionPlan(space)
self.start = start
self.goal = goal
self.planner.addMilestone(start)
self.planner.addMilestone(goal)
self.components = int(self.planner.getStats()['numComponents'])
print(self.components)
self.path = []
self.G = None
def planFree(world, hand, qtarget):
"""Plans a free-space motion for the robot's arm from the current
configuration to the destination qtarget"""
globals = Globals(world)
cspace = TransitCSpace(globals, hand)
robot = world.robot(0)
qmin, qmax = robot.getJointLimits()
#get the start/goal config
q0 = robot.getConfig()
q0arm = [q0[i] for i in hand.armIndices]
qtargetarm = [qtarget[i] for i in hand.armIndices]
if not cspace.feasible(q0arm):
print "Warning, arm start configuration is infeasible"
if not cspace.feasible(qtargetarm):
print "Warning, arm goal configuration is infeasible"
print "Planning transit motion to target config..."
MotionPlan.setOptions(connectionThreshold=5.0, perturbationRadius=0.5)
planner = MotionPlan(cspace, 'sbl')
planner.setEndpoints(q0arm, qtargetarm)
iters = 0
step = 10
while planner.getPath() == None and iters < 1000:
planner.planMore(step)
iters += step
cspace.close()
if planner.getPath() == None:
print "Failed finding transit path"
return None
print "Success"
#lift arm path to whole configuration space path
path = []
for qarm in planner.getPath():
path.append(q0[:])
for qi, i in zip(qarm, hand.armIndices):
path[-1][i] = qi
return path
def testPlannerSuccessRate(N=100,
duration=10,
spawnFunc=lambda: kinkTest(0.0025, False)):
import time
import matplotlib.pyplot as plt
finished = []
for run in range(N):
space, s, g = spawnFunc()
space.eps = 1e-3
if run == 0 and False: #show space
space.drawObstaclesMPL(plt.axes())
plt.scatter([s[0], g[0]], [s[1], g[1]])
plt.show()
plan = MotionPlan(space, type='prm', knn=5)
plan.setEndpoints(s, g)
t0 = time.time()
finished.append(None)
while time.time() - t0 < duration:
plan.planMore(5)
if plan.getPath():
finished[-1] = time.time() - t0
print("Found path with", len(plan.getPath()), "milestones in",
time.time() - t0, "s")
break
if finished[-1] is None:
print("Failed to find path in", duration, "s")
import numpy as np
finished = [v for v in finished if v != None]
hist, edges = np.histogram(finished, 20, (0, duration))
print(hist, edges)
hist = hist * 100 / N
chist = np.cumsum(hist)
plt.bar(edges[:-1], 100 - chist, duration / 20)
plt.xlabel('Time (s)')
plt.ylabel('% failed')
plt.xlim(0, duration)
plt.ylim(0, 100)
plt.savefig('histogram.png')
plt.show()
def __init__(self, space, start=(0.1, 0.5), goal=(0.9, 0.5)):
GLProgram.__init__(self)
self.space = space
#PRM planner
MotionPlan.setOptions(type="prm", knn=10, connectionThreshold=0.1)
self.optimizingPlanner = False
#FMM* planner
#MotionPlan.setOptions(type="fmm*")
#self.optimizingPlanner = True
#RRT planner
#MotionPlan.setOptions(type="rrt",perturbationRadius=0.25,bidirectional=True)
#self.optimizingPlanner = False
#RRT* planner
#MotionPlan.setOptions(type="rrt*")
#self.optimizingPlanner = True
#random-restart RRT planner
#MotionPlan.setOptions(type="rrt",perturbationRadius=0.25,bidirectional=True,shortcut=True,restart=True,restartTermCond="{foundSolution:1,maxIters:1000}")
#self.optimizingPlanner = True
#OMPL planners:
#Tested to work fine with OMPL's prm, lazyprm, prm*, lazyprm*, rrt, rrt*, rrtconnect, lazyrrt, lbtrrt, sbl, bitstar.
#Note that lbtrrt doesn't seem to continue after first iteration.
#Note that stride, pdst, and fmt do not work properly...
#MotionPlan.setOptions(type="ompl:rrt",suboptimalityFactor=0.1,knn=10,connectionThreshold=0.1)
#self.optimizingPlanner = True
self.planner = MotionPlan(space)
self.start = start
self.goal = goal
self.planner.setEndpoints(start, goal)
self.path = []
self.G = None
def feasible_plan(world, robot, qtarget):
"""Plans for some number of iterations from the robot's current configuration to
configuration qtarget. Returns the first path found.
Returns None if no path was found, otherwise returns the plan.
"""
t0 = time.time()
moving_joints = [1, 2, 3, 4, 5, 6, 7]
space = robotplanning.makeSpace(world=world,
robot=robot,
edgeCheckResolution=1e-2,
movingSubset=moving_joints)
plan = MotionPlan(space, type='prm')
#TODO: maybe you should use planToConfig?
numIters = 0
t1 = time.time()
print("Planning time,", numIters, "iterations", t1 - t0)
#to be nice to the C++ module, do this to free up memory
plan.space.close()
plan.close()
return path
def __init__(self,
space,
milestones=(0, 0),
initial_points=4000,
steps=100):
self.space = space
#PRM planner
MotionPlan.setOptions(type="prm",
knn=20,
connectionThreshold=0.5,
ignoreConnectedComponents=True)
self.optimizingPlanner = False
# we first plan a bit without goals just to have a good skeleton
self.initial_points = initial_points
self.steps = steps
self.milestones = milestones
self.times = 0
self.planner = MotionPlan(space)
for milestone in self.milestones:
self.planner.addMilestone(milestone.tolist())
self.components = int(self.planner.getStats()['numComponents'])
# print(self.components)
print('planning initial roadmap with {} points'.format(initial_points))
self.planner.planMore(self.initial_points)
self.connected = False
# we now add each of the chosen points as a milestone:
self.G = self.planner.getRoadmap()
self.start_milestones = len(self.G[0])
self.path = []
self.milestone_2 = 1
self.G = None
self.count = 0
self.connected_list = {0}
self.total_milestones = set(list(range(len(self.milestones))))
def __init__(self,
space,
start=(0.1, 0.5),
goal=(0.9, 0.5),
x_bound=1.0,
y_bound=1.0,
milestones=(0, 0),
initial_points=1000):
GLProgram.__init__(self)
self.space = space
#PRM planner
MotionPlan.setOptions(type="prm",
knn=10,
connectionThreshold=1,
ignoreConnectedComponents=True)
self.optimizingPlanner = False
# we first plan a bit without goals just to have a good skeleton
self.initial_points = initial_points
self.x_bound = x_bound
self.y_bound = y_bound
self.milestones = milestones
self.times = 0
self.planner = MotionPlan(space)
print('planning initial roadmap with {} points'.format(initial_points))
self.planner.planMore(self.initial_points)
self.connected = False
#FMM* planner
#MotionPlan.setOptions(type="fmm*")
#self.optimizingPlanner = True
#RRT planner
#MotionPlan.setOptions(type="rrt",perturbationRadius=0.25,bidirectional=True)
#self.optimizingPlanner = False
# RRT* planner
# MotionPlan.setOptions(type="rrt*")
# self.optimizingPlanner = True
#random-restart RRT planner
#MotionPlan.setOptions(type="rrt",perturbationRadius=0.25,bidirectional=True,shortcut=True,restart=True,restartTermCond="{foundSolution:1,maxIters:1000}")
#self.optimizingPlanner = True
#OMPL planners:
#Tested to work fine with OMPL's prm, lazyprm, prm*, lazyprm*, rrt, rrt*, rrtconnect, lazyrrt, lbtrrt, sbl, bitstar.
#Note that lbtrrt doesn't seem to continue after first iteration.
#Note that stride, pdst, and fmt do not work properly...
#MotionPlan.setOptions(type="ompl:rrt",suboptimalityFactor=0.1,knn=10,connectionThreshold=0.1)
#self.optimizingPlanner = True
# we then add start, goal and milestones
self.start = start
self.goal = goal
# self.planner.setEndpoints(start,goal)
# we now add each of the chosen points as a milestone:
self.G = self.planner.getRoadmap()
self.start_milestones = len(self.G[0])
print(self.start_milestones)
for milestone in self.milestones:
self.planner.addMilestone(milestone)
self.components = int(self.planner.getStats()['numComponents'])
print(self.components)
self.G = self.planner.getRoadmap()
self.end_milestones = len(self.G[0])
print(self.end_milestones)
# self.planner.addMilestone(self.start)
# self.planner.addMilestone(self.goal)
self.path = []
self.G = None