def allocatePlanner(si, plannerType):
if plannerType.lower() == "bfmtstar":
return og.BFMT(si)
elif plannerType.lower() == "bitstar":
return og.BITstar(si)
elif plannerType.lower() == "fmtstar":
return og.FMT(si)
elif plannerType.lower() == "informedrrtstar":
return og.InformedRRTstar(si)
elif plannerType.lower() == "prmstar":
return og.PRMstar(si)
elif plannerType.lower() == "rrtstar":
return og.RRTstar(si)
elif plannerType.lower() == "sorrtstar":
return og.SORRTstar(si)
elif plannerType.lower() == "rrtxstatic":
return og.RRTXstatic(si)
elif plannerType.lower() == "rrtsharp":
return og.RRTsharp(si)
# multi-query
elif plannerType.lower() == "lazyprmstar":
return og.LazyPRMstar(si)
# single-query
elif plannerType.lower() == "rrtconnect":
return og.RRTConnect(si)
elif plannerType.lower() == "lbtrrt":
return og.LBTRRT(si)
elif plannerType.lower() == "lazylbtrrt":
return og.LazyLBTRRT(si)
else:
ou.OMPL_ERROR(
"Planner-type is not implemented in allocation function.")
def allocatePlanner(si, plannerType):
if plannerType.lower() == "bitstar":
return og.BITstar(si)
elif plannerType.lower() == "fmtstar":
return og.FMT(si)
elif plannerType.lower() == "prmstar":
return og.PRMstar(si)
elif plannerType.lower() == "rrtstar":
return og.RRTstar(si)
else:
OMPL_ERROR("Planner-type is not implemented in allocation function.")
def plan(samplerIndex):
# construct the state space we are planning in
space = ob.RealVectorStateSpace(3)
# set the bounds
bounds = ob.RealVectorBounds(3)
bounds.setLow(-1)
bounds.setHigh(1)
space.setBounds(bounds)
# define a simple setup class
ss = og.SimpleSetup(space)
# set state validity checking for this space
ss.setStateValidityChecker(ob.StateValidityCheckerFn(isStateValid))
# create a start state
start = ob.State(space)
start[0] = 0
start[1] = 0
start[2] = 0
# create a goal state
goal = ob.State(space)
goal[0] = 0
goal[1] = 0
goal[2] = 1
# set the start and goal states;
ss.setStartAndGoalStates(start, goal)
# set sampler (optional; the default is uniform sampling)
si = ss.getSpaceInformation()
if samplerIndex == 1:
# use obstacle-based sampling
si.setValidStateSamplerAllocator(
ob.ValidStateSamplerAllocator(allocOBValidStateSampler))
elif samplerIndex == 2:
# use my sampler
si.setValidStateSamplerAllocator(
ob.ValidStateSamplerAllocator(allocMyValidStateSampler))
# create a planner for the defined space
planner = og.FMT(si)
ss.setPlanner(planner)
# attempt to solve the problem within ten seconds of planning time
solved = ss.solve(10.0)
if solved:
print("Found solution:")
# print the path to screen
print(ss.getSolutionPath())
else:
print("No solution found")
def plan(samplerIndex, start, goal, ss, space, display_trajectory_publisher):
si = ss.getSpaceInformation()
if samplerIndex == 1:
# use obstacle-based sampling
space.setStateSamplerAllocator(
ob.StateSamplerAllocator(allocSelfCollisionFreeStateSampler))
ss.setStartAndGoalStates(start, goal)
# create a planner for the defined space
planner = og.FMT(si) # change this to FMT;
if samplerIndex == 1:
planner.setVAEFMT(
1) # This flag is for turning on sampling with the VAE generator;
# set parameter;
planner.setExtendedFMT(
False) # do not extend if the planner does not terminate;
planner.setNumSamples(100)
planner.setNearestK(False)
planner.setCacheCC(True)
planner.setHeuristics(True)
# planner.setNearestK(1) # Disable K nearest neighbor implementation;
ss.setPlanner(planner)
start_time = time.time()
solved = ss.solve(40.0)
elapsed_time = time.time() - start_time
if solved:
print("Found solution after %s seconds:" % elapsed_time)
# print the path to screen
path = ss.getSolutionPath()
# ("The solution is: %s" % path)
# Visualization
display_trajectory = DisplayTrajectory()
display_trajectory.trajectory_start = convertStateToRobotState(start)
trajectory = convertPlanToTrajectory(path)
display_trajectory.trajectory.append(trajectory)
display_trajectory_publisher.publish(display_trajectory)
print("Visualizing trajectory...")
sleep(0.5)
else:
print("No solution found after %s seconds: " % elapsed_time)
return elapsed_time, float((int(solved)))
def setPlanner_3d(self):
self.si = self.ss.getSpaceInformation()
if self.plannerType.lower() == "bitstar":
planner = og.BITstar(self.si)
elif self.plannerType.lower() == "fmtstar":
planner = og.FMT(self.si)
elif self.plannerType.lower() == "informedrrtstar":
planner = og.InformedRRTstar(self.si)
elif self.plannerType.lower() == "prmstar":
planner = og.PRMstar(self.si)
elif self.plannerType.lower() == "rrtstar":
planner = og.RRTstar(self.si)
elif self.plannerType.lower() == "sorrtstar":
planner = og.SORRTstar(self.si)
else:
print("Planner-type is not implemented in allocation function.")
planner = og.RRTstar(self.si)
self.ss.setPlanner(planner)
def allocatePlanner(self, si, plannerType):
if plannerType.lower() == "bfmtstar":
return og.BFMT(si)
elif plannerType.lower() == "bitstar":
return og.BITstar(si)
elif plannerType.lower() == "fmtstar":
return og.FMT(si)
elif plannerType.lower() == "informedrrtstar":
return og.InformedRRTstar(si)
elif plannerType.lower() == "prmstar":
return og.PRMstar(si)
elif plannerType.lower() == "rrtstar":
return og.RRTstar(si)
elif plannerType.lower() == "sorrtstar":
return og.SORRTstar(si)
else:
ou.OMPL_ERROR(
"Planner-type is not implemented in allocation function.")
def allocatePlanner(si, plannerType):
if plannerType.lower() == "bfmtstar":
return og.BFMT(si)
elif plannerType.lower() == "bitstar":
planner = og.BITstar(si)
planner.setPruning(False)
planner.setSamplesPerBatch(200)
planner.setRewireFactor(20.)
return planner
elif plannerType.lower() == "fmtstar":
return og.FMT(si)
elif plannerType.lower() == "informedrrtstar":
return og.InformedRRTstar(si)
elif plannerType.lower() == "prmstar":
return og.PRMstar(si)
elif plannerType.lower() == "rrtstar":
return og.RRTstar(si)
elif plannerType.lower() == "sorrtstar":
return og.SORRTstar(si)
elif plannerType.lower() == 'rrtconnect':
return og.RRTConnect(si)
else:
ou.OMPL_ERROR(
"Planner-type is not implemented in allocation function.")
def plan(grid):
#agent and goal are represented by a point(x,y) and radius
global x
global time
x = grid
agent: Agent = grid.agent
goal: Goal = grid.goal
# Construct the robot state space in which we're planning. R2
space = ob.RealVectorStateSpace(2)
# Set the bounds of space to be inside Map
bounds = ob.RealVectorBounds(2)
# projection
pj = ProjectionEvaluator(space)
print('pj=', pj)
pj.setCellSizes(list2vec([1.0, 1.0]))
space.registerDefaultProjection(pj)
# Construct the robot state space in which we're planning.
bounds.setLow(0, 0) #set min x to _ 0
bounds.setHigh(0, grid.size.width) #set max x to _ x width
bounds.setLow(1, 0) #set min y to _ 0
bounds.setHigh(1, grid.size.height) #set max y to _ y height
space.setBounds(bounds)
print("bounds=", bounds.getVolume())
# Construct a space information instance for this state space
si = ob.SpaceInformation(space)
# Set the object used to check which states in the space are valid
si.setStateValidityChecker(ob.StateValidityCheckerFn(isStateValid))
# Set robot's starting state to agent's position (x,y) -> e.g. (0,0)
start = ob.State(space)
start[0] = float(agent.position.x)
start[1] = float(agent.position.y)
print(start[0], start[1])
# Set robot's goal state (x,y) -> e.g. (1.0,0.0)
goal = ob.State(space)
goal[0] = float(grid.goal.position.x)
goal[1] = float(grid.goal.position.y)
# Create a problem instance
pdef = ob.ProblemDefinition(si)
# Set the start and goal states
pdef.setStartAndGoalStates(start, goal)
# Create the optimization objective specified by our command-line argument.
# This helper function is simply a switch statement.
#pdef.setOptimizationObjective(allocateObjective(si, objectiveType))
# ******create a planner for the defined space
planner = og.FMT(si)
# set the problem we are trying to solve for the planner
planner.setProblemDefinition(pdef)
print(planner)
#print('checking projection',planner.getProjectionEvaluator())
print("__________________________________________________________")
# perform setup steps for the planner
planner.setup()
# print the settings for this space
print("space settings\n")
print(si.settings())
print("****************************************************************")
print("problem settings\n")
# print the problem settings
print(pdef)
# attempt to solve the problem within ten second of planning time
solved = planner.solve(time)
# For troubleshooting
if solved:
# get the goal representation from the problem definition (not the same as the goal state)
# and inquire about the found path
path = pdef.getSolutionPath()
print("Found solution:\n%s" % path)
else:
print("No solution found")
#metrics generation and graphing is possible here
#
#return trace for _find_path_internal method
print(
"$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$"
)
x = pdef.getSolutionPath().printAsMatrix()
lst = x.split()
lst = [int(round(float(x), 0)) for x in lst]
print(x)
print(lst)
trace = []
for i in range(0, len(lst), 2):
trace.append(Point(lst[i], lst[i + 1]))
print(trace)
return trace
