def setup_ompl(self):
print "set space"
self.space = ob.RealVectorStateSpace(dim=self.space_dimension)
bounds = ob.RealVectorBounds(self.space_dimension)
print "set bounds"
for i in xrange(self.space_dimension):
bounds.setLow(i, self.joint_constraints[0])
bounds.setHigh(i, self.joint_constraints[1])
print "set bounds 2"
self.space.setBounds(bounds)
print "set si"
self.si = ob.SpaceInformation(self.space)
print "set motion validator"
self.motion_validator = MotionValidator(self.si)
self.motion_validator.set_link_dimensions(self.link_dimensions)
self.motion_validator.set_workspace_dimension(self.workspace_dimension)
self.motion_validator.set_max_distance(self.max_velocity, self.delta_t)
print "set state validiy checker"
self.si.setStateValidityChecker(
ob.StateValidityCheckerFn(self.motion_validator.isValid))
print "set motion validator"
self.si.setMotionValidator(self.motion_validator)
print "si.setup"
self.si.setup()
self.problem_definition = ob.ProblemDefinition(self.si)
def setup_ompl(self):
self.space = ob.RealVectorStateSpace(dim=self.space_dimension)
bounds = ob.RealVectorBounds(self.space_dimension)
for i in xrange(self.space_dimension):
bounds.setLow(i, self.joint_constraints[0])
bounds.setHigh(i, self.joint_constraints[1])
self.space.setBounds(bounds)
self.si = ob.SpaceInformation(self.space)
self.motion_validator = MotionValidator(self.si)
self.motion_validator.set_link_dimensions(self.link_dimensions)
self.motion_validator.set_workspace_dimension(self.workspace_dimension)
self.motion_validator.set_max_distance(self.max_velocity, self.delta_t)
#self.si.setStateValidityChecker(self.motion_validator.isValid)
self.si.setMotionValidator(self.motion_validator)
self.si.setup()
self.problem_definition = ob.ProblemDefinition(self.si)
class PathPlanner:
def __init__(self):
pass
def set_params(self,
link_dimensions,
workspace_dimension,
max_velocity,
delta_t,
use_linear_path,
sim_run,
joint_constraints):
self.link_dimensions = link_dimensions
self.space_dimension = len(link_dimensions)
self.workspace_dimension = workspace_dimension
self.max_velocity = max_velocity
self.delta_t = delta_t
self.use_linear_path = use_linear_path
self.sim_run = sim_run
self.joint_constraints = joint_constraints
if logging.getLogger().getEffectiveLevel() == logging.WARN:
noOutputHandler()
def set_start_state(self, start_state):
self.start_state = ob.State(self.si.getStateSpace())
for i in xrange(self.si.getStateSpace().getDimension()):
self.start_state[i] = start_state[i]
def set_goal_state(self, goal):
self.problem_definition.clearGoal()
try:
iterator = iter(goal)
except TypeError:
"""
Only one goal state
"""
if self.motion_validator.isValid(goal):
self.problem_definition.setGoal(GoalRegion(self.si, goal))
else:
"""
A goal area defined by several goal states
"""
gs = []
for g in goal:
if self.motion_validator.isValid(g):
gs.append(g)
self.problem_definition.setGoal(GoalRegion(self.si, gs))
def plan_path(self):
self.problem_definition.clearSolutionPaths()
path_collides = True
if (not self.motion_validator.isValid(self.start_state) or
not self.problem_definition.getGoal().canSample()):
return [], [], []
if self.use_linear_path:
path = self.linear_path(self.start_state, self.problem_definition.getGoal().getRandomGoalState())
path_collides = self.path_collides(path)
if path_collides:
self.problem_definition.addStartState(self.start_state)
self.planner = og.RRTConnect(self.si)
self.planner.setRange(np.sqrt(self.si.getStateSpace().getDimension() * np.square(self.delta_t * self.max_velocity)))
self.planner.setProblemDefinition(self.problem_definition)
self.planner.setup()
start_time = time.time()
while not self.problem_definition.hasSolution():
self.planner.solve(1.0)
delta = time.time() - start_time
if delta > 3.0:
#logging.warn("PathPlanner: Maximum allowed planning time exceeded. Aborting")
xs = []
us = []
zs = []
return xs, us, zs
path = []
if self.problem_definition.hasSolution():
logging.info("PathPlanner: Solution found after " +str(time.time() - start_time) + " s")
solution_path = self.problem_definition.getSolutionPath()
states = solution_path.getStates()
path = [np.array([state[i] for i in xrange(self.space.getDimension())]) for state in states]
return self._augment_path(path)
def path_collides(self, path):
for i in xrange(1, len(path)):
if self.motion_validator._in_collision(path[i-1], path[i]):
return True
return False
def linear_path(self, start, goal):
path = []
max_dist = np.sqrt(self.si.getStateSpace().getDimension() * np.square(self.delta_t * self.max_velocity))
s = []
g = []
for i in xrange(self.space.getDimension()):
s.append(start[i])
g.append(goal[i])
start = np.array(s)
path.append(start)
goal = np.array(g)
vec = goal - start
vec_length = np.linalg.norm(vec)
vec_norm = vec / vec_length
steps = vec_length / max_dist
steps_full = np.floor(steps)
steps_half = steps - steps_full
for i in xrange(int(steps_full)):
new_state = path[-1] + max_dist * vec_norm
path.append(new_state)
if steps_half > 1.0e-10:
path.append(goal)
return path
def setup_ompl(self):
print "set space"
self.space = ob.RealVectorStateSpace(dim=self.space_dimension)
bounds = ob.RealVectorBounds(self.space_dimension)
print "set bounds"
for i in xrange(self.space_dimension):
bounds.setLow(i, self.joint_constraints[0])
bounds.setHigh(i, self.joint_constraints[1])
print "set bounds 2"
self.space.setBounds(bounds)
print "set si"
self.si = ob.SpaceInformation(self.space)
print "set motion validator"
self.motion_validator = MotionValidator(self.si)
self.motion_validator.set_link_dimensions(self.link_dimensions)
self.motion_validator.set_workspace_dimension(self.workspace_dimension)
self.motion_validator.set_max_distance(self.max_velocity, self.delta_t)
print "set state validiy checker"
self.si.setStateValidityChecker(ob.StateValidityCheckerFn(self.motion_validator.isValid))
print "set motion validator"
self.si.setMotionValidator(self.motion_validator)
print "si.setup"
self.si.setup()
self.problem_definition = ob.ProblemDefinition(self.si)
def set_obstacles(self, obstacles):
self.motion_validator.set_obstacles(obstacles)
def _augment_path(self, path):
"""
Augments the path with controls and observations
"""
new_path = []
for i in xrange(len(path) - 1):
u = (np.array(path[i + 1]) - np.array(path[i])) / self.delta_t
#new_path.append([path[i], [u[j] for j in xrange(len(u))], path[i]])
new_path.append([path[i], u, path[i]])
new_path.append([path[-1], np.array([0.0 for i in xrange(self.si.getStateSpace().getDimension())]), path[-1]])
xs = [new_path[i][0] for i in xrange(len(path))]
us = [new_path[i][1] for i in xrange(len(path))]
zs = [new_path[i][2] for i in xrange(len(path))]
return xs, us, zs
class PathPlanner:
def __init__(self):
pass
def set_params(self, link_dimensions, workspace_dimension, max_velocity,
delta_t, use_linear_path, sim_run, joint_constraints,
verbose_rrt):
self.link_dimensions = link_dimensions
self.space_dimension = len(link_dimensions)
self.workspace_dimension = workspace_dimension
self.max_velocity = max_velocity
self.delta_t = delta_t
self.use_linear_path = use_linear_path
self.sim_run = sim_run
self.joint_constraints = joint_constraints
self.verbose_rrt = verbose_rrt
if not self.verbose_rrt:
noOutputHandler()
def set_start_state(self, start_state):
self.start_state = ob.State(self.si.getStateSpace())
for i in xrange(self.si.getStateSpace().getDimension()):
self.start_state[i] = start_state[i]
def set_goal_state(self, goal_state):
self.goal_state = ob.State(self.si.getStateSpace())
for i in xrange(self.si.getStateSpace().getDimension()):
self.goal_state[i] = goal_state[i]
def plan_path(self):
self.problem_definition.clearSolutionPaths()
path_collides = True
if self.use_linear_path:
path = self.linear_path(self.start_state, self.goal_state)
path_collides = self.path_collides(path)
if path_collides:
if not self.motion_validator.isValid(
self.start_state) or not self.motion_validator.isValid(
self.goal_state):
logging.warn(
"PathPlanner: Start or goal state not valid. Skipping")
return [], [], []
self.problem_definition.addStartState(self.start_state)
self.problem_definition.setStartAndGoalStates(
self.start_state, self.goal_state)
self.planner = og.RRTConnect(self.si)
self.planner.setRange(
np.sqrt(self.si.getStateSpace().getDimension() *
np.square(self.delta_t * self.max_velocity)))
self.planner.setProblemDefinition(self.problem_definition)
self.planner.setup()
start_time = time.time()
while not self.problem_definition.hasSolution():
self.planner.solve(1.0)
delta = time.time() - start_time
logging.info("PathPlanner: Time spent on planning so far: " +
str(delta))
if delta > 3.0:
xs = []
us = []
zs = []
return xs, us, zs
path = []
if self.problem_definition.hasSolution():
logging.info("PathPlanner: Solution found after " +
str(time.time() - start_time) + " seconds")
solution_path = self.problem_definition.getSolutionPath()
states = solution_path.getStates()
path = [
np.array(
[state[i] for i in xrange(self.space.getDimension())])
for state in states
]
#print "path " + str(path)
else:
print "no solution"
return self._augment_path(path)
def path_collides(self, path):
for i in xrange(1, len(path)):
if self.motion_validator._in_collision(path[i - 1], path[i]):
return True
return False
def linear_path(self, start, goal):
path = []
max_dist = np.sqrt(self.si.getStateSpace().getDimension() *
np.square(self.delta_t * self.max_velocity))
s = []
g = []
for i in xrange(self.space.getDimension()):
s.append(start[i])
g.append(goal[i])
start = np.array(s)
path.append(start)
goal = np.array(g)
vec = goal - start
vec_length = np.linalg.norm(vec)
vec_norm = vec / vec_length
steps = vec_length / max_dist
steps_full = np.floor(steps)
steps_half = steps - steps_full
for i in xrange(int(steps_full)):
new_state = path[-1] + max_dist * vec_norm
path.append(new_state)
if steps_half > 1.0e-10:
path.append(goal)
return path
def setup_ompl(self):
self.space = ob.RealVectorStateSpace(dim=self.space_dimension)
bounds = ob.RealVectorBounds(self.space_dimension)
for i in xrange(self.space_dimension):
bounds.setLow(i, self.joint_constraints[0])
bounds.setHigh(i, self.joint_constraints[1])
self.space.setBounds(bounds)
self.si = ob.SpaceInformation(self.space)
self.motion_validator = MotionValidator(self.si)
self.motion_validator.set_link_dimensions(self.link_dimensions)
self.motion_validator.set_workspace_dimension(self.workspace_dimension)
self.motion_validator.set_max_distance(self.max_velocity, self.delta_t)
#self.si.setStateValidityChecker(self.motion_validator.isValid)
self.si.setMotionValidator(self.motion_validator)
self.si.setup()
self.problem_definition = ob.ProblemDefinition(self.si)
def set_obstacles(self, obstacles):
self.motion_validator.set_obstacles(obstacles)
def _augment_path(self, path):
"""
Augments the path with controls and observations
"""
new_path = []
for i in xrange(len(path) - 1):
u = (np.array(path[i + 1]) - np.array(path[i])) / self.delta_t
#new_path.append([path[i], [u[j] for j in xrange(len(u))], path[i]])
new_path.append([path[i], u, path[i]])
new_path.append([
path[-1],
np.array(
[0.0 for i in xrange(self.si.getStateSpace().getDimension())]),
path[-1]
])
xs = [new_path[i][0] for i in xrange(len(path))]
us = [new_path[i][1] for i in xrange(len(path))]
zs = [new_path[i][2] for i in xrange(len(path))]
#print "xs" + str(xs)
return xs, us, zs
class PathPlanner:
def __init__(self):
pass
def set_params(self, link_dimensions, workspace_dimension, max_velocity,
delta_t, use_linear_path, sim_run, joint_constraints):
self.link_dimensions = link_dimensions
self.space_dimension = len(link_dimensions)
self.workspace_dimension = workspace_dimension
self.max_velocity = max_velocity
self.delta_t = delta_t
self.use_linear_path = use_linear_path
self.sim_run = sim_run
self.joint_constraints = joint_constraints
if logging.getLogger().getEffectiveLevel() == logging.WARN:
noOutputHandler()
def set_start_state(self, start_state):
self.start_state = ob.State(self.si.getStateSpace())
for i in xrange(self.si.getStateSpace().getDimension()):
self.start_state[i] = start_state[i]
def set_goal_state(self, goal):
self.problem_definition.clearGoal()
try:
iterator = iter(goal)
except TypeError:
"""
Only one goal state
"""
if self.motion_validator.isValid(goal):
self.problem_definition.setGoal(GoalRegion(self.si, goal))
else:
"""
A goal area defined by several goal states
"""
gs = []
for g in goal:
if self.motion_validator.isValid(g):
gs.append(g)
self.problem_definition.setGoal(GoalRegion(self.si, gs))
def plan_path(self):
self.problem_definition.clearSolutionPaths()
path_collides = True
if (not self.motion_validator.isValid(self.start_state)
or not self.problem_definition.getGoal().canSample()):
return [], [], []
if self.use_linear_path:
path = self.linear_path(
self.start_state,
self.problem_definition.getGoal().getRandomGoalState())
path_collides = self.path_collides(path)
if path_collides:
self.problem_definition.addStartState(self.start_state)
self.planner = og.RRTConnect(self.si)
self.planner.setRange(
np.sqrt(self.si.getStateSpace().getDimension() *
np.square(self.delta_t * self.max_velocity)))
self.planner.setProblemDefinition(self.problem_definition)
self.planner.setup()
start_time = time.time()
while not self.problem_definition.hasSolution():
self.planner.solve(1.0)
delta = time.time() - start_time
if delta > 3.0:
#logging.warn("PathPlanner: Maximum allowed planning time exceeded. Aborting")
xs = []
us = []
zs = []
return xs, us, zs
path = []
if self.problem_definition.hasSolution():
logging.info("PathPlanner: Solution found after " +
str(time.time() - start_time) + " s")
solution_path = self.problem_definition.getSolutionPath()
states = solution_path.getStates()
path = [
np.array(
[state[i] for i in xrange(self.space.getDimension())])
for state in states
]
return self._augment_path(path)
def path_collides(self, path):
for i in xrange(1, len(path)):
if self.motion_validator._in_collision(path[i - 1], path[i]):
return True
return False
def linear_path(self, start, goal):
path = []
max_dist = np.sqrt(self.si.getStateSpace().getDimension() *
np.square(self.delta_t * self.max_velocity))
s = []
g = []
for i in xrange(self.space.getDimension()):
s.append(start[i])
g.append(goal[i])
start = np.array(s)
path.append(start)
goal = np.array(g)
vec = goal - start
vec_length = np.linalg.norm(vec)
vec_norm = vec / vec_length
steps = vec_length / max_dist
steps_full = np.floor(steps)
steps_half = steps - steps_full
for i in xrange(int(steps_full)):
new_state = path[-1] + max_dist * vec_norm
path.append(new_state)
if steps_half > 1.0e-10:
path.append(goal)
return path
def setup_ompl(self):
print "set space"
self.space = ob.RealVectorStateSpace(dim=self.space_dimension)
bounds = ob.RealVectorBounds(self.space_dimension)
print "set bounds"
for i in xrange(self.space_dimension):
bounds.setLow(i, self.joint_constraints[0])
bounds.setHigh(i, self.joint_constraints[1])
print "set bounds 2"
self.space.setBounds(bounds)
print "set si"
self.si = ob.SpaceInformation(self.space)
print "set motion validator"
self.motion_validator = MotionValidator(self.si)
self.motion_validator.set_link_dimensions(self.link_dimensions)
self.motion_validator.set_workspace_dimension(self.workspace_dimension)
self.motion_validator.set_max_distance(self.max_velocity, self.delta_t)
print "set state validiy checker"
self.si.setStateValidityChecker(
ob.StateValidityCheckerFn(self.motion_validator.isValid))
print "set motion validator"
self.si.setMotionValidator(self.motion_validator)
print "si.setup"
self.si.setup()
self.problem_definition = ob.ProblemDefinition(self.si)
def set_obstacles(self, obstacles):
self.motion_validator.set_obstacles(obstacles)
def _augment_path(self, path):
"""
Augments the path with controls and observations
"""
new_path = []
for i in xrange(len(path) - 1):
u = (np.array(path[i + 1]) - np.array(path[i])) / self.delta_t
#new_path.append([path[i], [u[j] for j in xrange(len(u))], path[i]])
new_path.append([path[i], u, path[i]])
new_path.append([
path[-1],
np.array(
[0.0 for i in xrange(self.si.getStateSpace().getDimension())]),
path[-1]
])
xs = [new_path[i][0] for i in xrange(len(path))]
us = [new_path[i][1] for i in xrange(len(path))]
zs = [new_path[i][2] for i in xrange(len(path))]
return xs, us, zs
class PathPlanner:
def __init__(self):
pass
def set_params(self,
link_dimensions,
workspace_dimension,
max_velocity,
delta_t,
use_linear_path,
sim_run,
joint_constraints,
verbose_rrt):
self.link_dimensions = link_dimensions
self.space_dimension = len(link_dimensions)
self.workspace_dimension = workspace_dimension
self.max_velocity = max_velocity
self.delta_t = delta_t
self.use_linear_path = use_linear_path
self.sim_run = sim_run
self.joint_constraints = joint_constraints
self.verbose_rrt = verbose_rrt
if not self.verbose_rrt:
noOutputHandler()
def set_start_state(self, start_state):
self.start_state = ob.State(self.si.getStateSpace())
for i in xrange(self.si.getStateSpace().getDimension()):
self.start_state[i] = start_state[i]
def set_goal_state(self, goal_state):
self.goal_state = ob.State(self.si.getStateSpace())
for i in xrange(self.si.getStateSpace().getDimension()):
self.goal_state[i] = goal_state[i]
def plan_path(self):
self.problem_definition.clearSolutionPaths()
path_collides = True
if self.use_linear_path:
path = self.linear_path(self.start_state, self.goal_state)
path_collides = self.path_collides(path)
if path_collides:
if not self.motion_validator.isValid(self.start_state) or not self.motion_validator.isValid(self.goal_state):
logging.warn("PathPlanner: Start or goal state not valid. Skipping")
return [], [], []
self.problem_definition.addStartState(self.start_state)
self.problem_definition.setStartAndGoalStates(self.start_state, self.goal_state)
self.planner = og.RRTConnect(self.si)
self.planner.setRange(np.sqrt(self.si.getStateSpace().getDimension() * np.square(self.delta_t * self.max_velocity)))
self.planner.setProblemDefinition(self.problem_definition)
self.planner.setup()
start_time = time.time()
while not self.problem_definition.hasSolution():
self.planner.solve(1.0)
delta = time.time() - start_time
logging.info("PathPlanner: Time spent on planning so far: " + str(delta))
if delta > 3.0:
xs = []
us = []
zs = []
return xs, us, zs
path = []
if self.problem_definition.hasSolution():
logging.info("PathPlanner: Solution found after " + str(time.time() - start_time) + " seconds")
solution_path = self.problem_definition.getSolutionPath()
states = solution_path.getStates()
path = [np.array([state[i] for i in xrange(self.space.getDimension())]) for state in states]
#print "path " + str(path)
else:
print "no solution"
return self._augment_path(path)
def path_collides(self, path):
for i in xrange(1, len(path)):
if self.motion_validator._in_collision(path[i-1], path[i]):
return True
return False
def linear_path(self, start, goal):
path = []
max_dist = np.sqrt(self.si.getStateSpace().getDimension() * np.square(self.delta_t * self.max_velocity))
s = []
g = []
for i in xrange(self.space.getDimension()):
s.append(start[i])
g.append(goal[i])
start = np.array(s)
path.append(start)
goal = np.array(g)
vec = goal - start
vec_length = np.linalg.norm(vec)
vec_norm = vec / vec_length
steps = vec_length / max_dist
steps_full = np.floor(steps)
steps_half = steps - steps_full
for i in xrange(int(steps_full)):
new_state = path[-1] + max_dist * vec_norm
path.append(new_state)
if steps_half > 1.0e-10:
path.append(goal)
return path
def setup_ompl(self):
self.space = ob.RealVectorStateSpace(dim=self.space_dimension)
bounds = ob.RealVectorBounds(self.space_dimension)
for i in xrange(self.space_dimension):
bounds.setLow(i, self.joint_constraints[0])
bounds.setHigh(i, self.joint_constraints[1])
self.space.setBounds(bounds)
self.si = ob.SpaceInformation(self.space)
self.motion_validator = MotionValidator(self.si)
self.motion_validator.set_link_dimensions(self.link_dimensions)
self.motion_validator.set_workspace_dimension(self.workspace_dimension)
self.motion_validator.set_max_distance(self.max_velocity, self.delta_t)
#self.si.setStateValidityChecker(self.motion_validator.isValid)
self.si.setMotionValidator(self.motion_validator)
self.si.setup()
self.problem_definition = ob.ProblemDefinition(self.si)
def set_obstacles(self, obstacles):
self.motion_validator.set_obstacles(obstacles)
def _augment_path(self, path):
"""
Augments the path with controls and observations
"""
new_path = []
for i in xrange(len(path) - 1):
u = (np.array(path[i + 1]) - np.array(path[i])) / self.delta_t
#new_path.append([path[i], [u[j] for j in xrange(len(u))], path[i]])
new_path.append([path[i], u, path[i]])
new_path.append([path[-1], np.array([0.0 for i in xrange(self.si.getStateSpace().getDimension())]), path[-1]])
xs = [new_path[i][0] for i in xrange(len(path))]
us = [new_path[i][1] for i in xrange(len(path))]
zs = [new_path[i][2] for i in xrange(len(path))]
#print "xs" + str(xs)
return xs, us, zs