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planner.py
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planner.py
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# coding=utf-8
from __future__ import division
from __future__ import with_statement # for python 2.5
import abc
import sys
import json
import copy
from memoized import memoized
import numpy as np
import openravepy as rave
import trajoptpy
import trajoptpy.math_utils as mu
from trajoptpy import check_traj
from tf import transformations
import addict
import utils
import draw
import bound
__author__ = 'Aijun Bai'
class Planner(object):
__metaclass__ = abc.ABCMeta
def __init__(self, robot, params=None, verbose=False):
self.robot = robot
self.params = params
self.env = self.robot.GetEnv()
self.verbose = verbose
@staticmethod
def four_to_six(v):
if isinstance(v, list):
return [Planner.four_to_six(e) for e in v]
assert v.size == 4
return np.r_[v[0:3], 0.0, 0.0, v[3]]
@staticmethod
def six_to_four(v):
if isinstance(v, list):
return [Planner.six_to_four(e) for e in v]
assert v.size == 6
return np.r_[v[0:3], v[5]]
def name(self):
prefix = '\t' * self.params.depth
return '{}{} - {}'.format(prefix, self.__class__.__name__, self.params.name)
@staticmethod
def copy_params(creator):
def wrapper(robot, params, verbose):
params = copy.deepcopy(params)
if 'depth' in params:
params.depth += 1
else:
params.depth = 0
return creator(robot, params, verbose)
return wrapper
@staticmethod
def filter(plan):
def wrapper(self, start, goal):
bound.set_dof(self.robot, self.params.dof)
if self.params.dof == 4 and (start.size == 6 or goal.size == 6):
start = Planner.six_to_four(start)
goal = Planner.six_to_four(goal)
with self.robot:
self.robot.SetActiveDOFValues(start)
print '{} - planning at depth {} for {} steps...'.format(self.name(), self.params.depth, self.params.n_steps)
traj, cost = plan(self, start, goal)
if traj is not None:
if self.params.dof == 4 and traj[0].size == 4:
traj = Planner.four_to_six([t for t in traj])
if isinstance(traj, np.ndarray):
traj = traj.tolist()
return traj, cost
return None, None
return wrapper
@abc.abstractmethod
def plan(self, start, goal):
pass
def sample_traj(self, traj_obj):
spec = traj_obj.GetConfigurationSpecification()
traj = []
step = traj_obj.GetDuration() / self.params.n_steps
for i in range(self.params.n_steps):
data = traj_obj.Sample(i * step)
T = spec.ExtractTransform(None, data, self.robot)
pose = rave.poseFromMatrix(T) # wxyz, xyz
euler = transformations.euler_from_quaternion(np.r_[pose[1:4], pose[0]])
traj.append(np.r_[pose[4:7], euler[0:3]])
with self.robot:
if check_traj.traj_is_safe(traj, self.robot):
return traj, traj_obj.GetDuration()
return None, None
class TrajoptPlanner(Planner):
def __init__(self, robot, params, verbose=False):
super(TrajoptPlanner, self).__init__(robot, params=params, verbose=verbose)
@staticmethod
def create(multi_initialization):
@Planner.copy_params
def creator(robot, params, verbose):
params.name = 'trajopt+{}'.format(multi_initialization)
params.multi_initialization = multi_initialization
params.int_planner = 'sampling'
return TrajoptPlanner(robot, params=params, verbose=verbose)
return creator
@staticmethod
def make_fullbody_request(end_joints, inittraj, n_steps):
if isinstance(end_joints, np.ndarray):
end_joints = end_joints.tolist()
coll_coeff = 150
dist_pen = 0.05
d = {
"basic_info": {
"n_steps": n_steps,
"manip": "active",
"start_fixed": True
},
"costs": [
{
"type": "joint_vel",
"params": {
"coeffs": [5]
}
},
{
"": "cont_coll",
"type": "collision",
"params": {
"coeffs": [coll_coeff],
"dist_pen": [dist_pen],
"continuous": True
}
},
{
"name": "disc_coll",
"type": "collision",
"params": {
"coeffs": [coll_coeff],
"dist_pen": [dist_pen],
"continuous": False
}
}
],
"constraints": [
{
"type": "joint",
"params": {
"vals": end_joints
}
}
]
}
if inittraj is not None:
d["init_info"] = {
"type": "given_traj",
"data": [row.tolist() for row in inittraj]
}
else:
d["init_info"] = {
"type": "straight_line",
"endpoint": end_joints
}
return d
def plan_with_inittraj(self, start, goal, inittraj=None):
if self.verbose:
draw.draw_trajectory(self.env, inittraj, colors=np.array((0.5, 0.5, 0.5)))
if self.params.n_steps > 2:
with self.robot:
self.robot.SetActiveDOFValues(start)
request = self.make_fullbody_request(goal, inittraj, self.params.n_steps)
prob = trajoptpy.ConstructProblem(json.dumps(request), self.env)
def constraint(dofs):
valid = True
if self.params.dof == 6:
valid &= abs(dofs[3]) < 0.1
valid &= abs(dofs[4]) < 0.1
with self.robot:
self.robot.SetActiveDOFValues(dofs)
valid &= not self.env.CheckCollision(self.robot)
return 0 if valid else 1
for t in range(1, self.params.n_steps):
prob.AddConstraint(
constraint, [(t, j) for j in range(self.params.dof)], "EQ", "constraint%i" % t)
result = trajoptpy.OptimizeProblem(prob)
traj = result.GetTraj()
prob.SetRobotActiveDOFs()
if traj is not None:
if self.verbose:
draw.draw_trajectory(self.env, traj)
if check_traj.traj_is_safe(traj, self.robot):
cost = sum(cost[1] for cost in result.GetCosts())
return traj, cost
elif self.params.n_steps <= 2:
return [start, goal], utils.dist(start, goal)
return None, None
@property
@memoized
def int_planner1(self):
params = copy.deepcopy(self.params)
params.n_steps = self.params.n_steps // 2
return create_planner(self.params.int_planner)(self.robot, params=params, verbose=self.verbose)
@property
@memoized
def int_planner2(self):
params = copy.deepcopy(self.params)
params.n_steps = (self.params.n_steps + 1) // 2
return create_planner(self.params.int_planner)(self.robot, params=params, verbose=self.verbose)
def gen_waypoint(self, bounds, maxiter=100):
for i in range(self.params.multi_initialization):
waypoint = None
for j in range(maxiter):
waypoint = bound.sample(bounds)
with self.robot:
self.robot.SetActiveDOFValues(waypoint)
if not self.env.CheckCollision(self.robot):
break
yield waypoint
@Planner.filter
def plan(self, start, goal):
traj, cost = self.plan_with_inittraj(start, goal)
if traj is not None:
return traj, cost
bounds = bound.get_bounds(self.robot, self.params.dof)
solutions = []
for i, waypoint in enumerate(self.gen_waypoint(bounds)):
print '{} - int_planners try waypoint {} at depth {}...'.format(self.name(), i, self.params.depth)
if self.verbose:
utils.pv('waypoint')
inittraj = []
traj1, _ = self.int_planner1.plan(start, waypoint)
if traj1 is not None:
traj2, _ = self.int_planner2.plan(waypoint, goal)
if traj2 is not None:
print '{} - int_planners find plans at depth {}...'.format(self.name(), self.params.depth)
if self.params.dof == 4:
traj1 = Planner.six_to_four([t for t in traj1])
traj2 = Planner.six_to_four([t for t in traj2])
inittraj.extend(traj1[0:self.int_planner1.params.n_steps] + traj2[-1-self.int_planner2.params.n_steps:])
if inittraj:
traj, cost = self.plan_with_inittraj(start, goal, inittraj=inittraj)
if traj is not None:
solutions.append((traj, cost))
if self.params.first_return:
break
if solutions:
return sorted(solutions, key=lambda x: x[1])[0]
return None, None
class RavePlanner(Planner):
def __init__(self, robot, params, verbose=False):
super(RavePlanner, self).__init__(robot, params=params, verbose=verbose)
self.planner = rave.RaveCreatePlanner(self.env, self.params.rave_planner)
@staticmethod
def create(rave_planner):
@Planner.copy_params
def creator(robot, params, verbose):
params.name = rave_planner
params.rave_planner = rave_planner
return RavePlanner(robot, params=params, verbose=verbose)
return creator
def plan_with_smoother(self, start, goal, smoother):
with self.robot:
self.robot.SetActiveDOFValues(start)
params = rave.Planner.PlannerParameters()
params.SetRobotActiveJoints(self.robot)
params.SetGoalConfig(goal)
if smoother:
params.SetExtraParameters(
"""<_postprocessing planner="{}">
<_nmaxiterations>40</_nmaxiterations>
</_postprocessing>""".format(smoother))
with self.env:
traj_obj = rave.RaveCreateTrajectory(self.env, '')
self.planner.InitPlan(self.robot, params)
self.planner.PlanPath(traj_obj)
return self.sample_traj(traj_obj)
@Planner.filter
def plan(self, start, goal):
smoothers = ['ParabolicSmoother', 'LinearSmoother', None]
for smoother in smoothers:
try:
return self.plan_with_smoother(start, goal, smoother)
except rave.openrave_exception as e:
print e
return None, None
class EnsemblePlanner(Planner):
(SAMPLING, OPTIMIZING) = (1, 2)
def __init__(self, robot, params, verbose=False):
super(EnsemblePlanner, self).__init__(robot, params=params, verbose=verbose)
self.planners = []
if self.params.kclass & EnsemblePlanner.OPTIMIZING:
self.planners.append(create_planner('optimizing')(robot, params=self.params, verbose=verbose))
if self.params.kclass & EnsemblePlanner.SAMPLING:
self.planners.append(create_planner('birrt')(robot, params=self.params, verbose=verbose))
if self.params.kclass & EnsemblePlanner.OPTIMIZING:
self.planners.append(create_planner('optimizing_multi')(robot, params=self.params, verbose=verbose))
@staticmethod
def create(kclass=0):
@Planner.copy_params
def creator(robot, params, verbose):
params.name = ""
if kclass | EnsemblePlanner.OPTIMIZING:
params.name += '{optimizing}'
if kclass | EnsemblePlanner.SAMPLING:
params.name += '{sampling}'
params.kclass = kclass
return EnsemblePlanner(robot, params=params, verbose=verbose)
return creator
@Planner.filter
def plan(self, start, goal):
for planner in self.planners:
traj, cost = planner.plan(start, goal)
if traj is not None:
return traj, cost
return None, None
class PipelinePlanner(Planner):
def __init__(self, robot, params=None, verbose=False):
super(PipelinePlanner, self).__init__(robot, params=params, verbose=verbose)
self.sampling = create_planner('sampling')(robot, self.params, verbose)
self.optimizing = create_planner('optimizing')(robot, self.params, verbose)
self.random_optimizing = create_planner('random_optimizing')(robot, self.params, verbose)
@staticmethod
@Planner.copy_params
def create(robot, params, verbose):
params.name = 'pipeline'
return PipelinePlanner(robot, params=params, verbose=verbose)
@Planner.filter
def plan(self, start, goal):
traj, cost = self.optimizing.plan(start, goal)
if traj is not None:
print '{} - first priority: optimizing'.format(self.name())
return traj, cost
traj, cost = self.sampling.plan(start, goal)
if traj is not None:
inittraj = traj
if self.params.dof == 4:
inittraj = Planner.six_to_four([t for t in inittraj])
traj2, cost2 = self.optimizing.plan_with_inittraj(start, goal, inittraj)
if traj2 is not None:
print '{} - second priority: sampling->optimizing'.format(self.name())
return traj2, cost2
print '{} - third priority: sampling'.format(self.name())
return traj, cost
print '{} - fourth priority: random_optimizing'.format(self.name())
return self.random_optimizing.plan(start, goal)
def create_planner(name, planners=addict.Dict()):
if len(planners) == 0:
planners.birrt = RavePlanner.create('BiRRT')
planners.ompl_rrt = RavePlanner.create('OMPL_RRT')
planners.ompl_rrtstar = RavePlanner.create('OMPL_RRTstar')
planners.ompl_rrtconnect = RavePlanner.create('OMPL_RRTConnect')
planners.rastar = RavePlanner.create('RAStar')
planners.basicrrt = RavePlanner.create('BasicRRT')
planners.sbpl = RavePlanner.create('sbpl')
planners.explorationrrt = RavePlanner.create('ExplorationRRT')
planners.rastar = RavePlanner.create('RAStar')
planners.ompl_rrtconnect = RavePlanner.create('OMPL_RRTConnect')
planners.ompl_prm = RavePlanner.create('OMPL_PRM')
planners.ensembling = EnsemblePlanner.create(
EnsemblePlanner.SAMPLING | EnsemblePlanner.OPTIMIZING)
planners.sampling = EnsemblePlanner.create(EnsemblePlanner.SAMPLING)
planners.optimizing = TrajoptPlanner.create(1)
planners.optimizing_multi = TrajoptPlanner.create(100)
planners.random_optimizing = EnsemblePlanner.create(EnsemblePlanner.OPTIMIZING)
planners.pipeline = PipelinePlanner.create
if name in planners:
return planners[name]
else:
raise KeyError('can not find planner "{}"'.format(name))