def __init__(self, point_free_cb, line_free_cb, dimensions):
"""
Construct a randomized roadmap planner.
'point_free_cb' is a function that accepts a point (two-tuple) and
outputs a boolean value indicating whether the point is in free space.
'line_free_cb' is a function that accepts two points (the start and the
end of a line segment) and outputs a boolen value indicating whether
the line segment is free from obstacles.
'dimensions' is a tuple of tuples that define the x and y dimensions of
the world, e.g. ((-8, 8), (-8, 8)). It should be used when generating
random points.
"""
self.point_free_cb = point_free_cb
self.line_free_cb = line_free_cb
self.dimensions = dimensions
self.search_algorithm = euclidean()
self.number_of_trials = 500
self.search_result = False
self.output_path = None
self.id_generator = 10000
self.graph = graph()
def __init__(self, point_free_cb, line_free_cb, dimensions):
"""
Construct a randomized roadmap planner.
'point_free_cb' is a function that accepts a point (two-tuple) and
outputs a boolean value indicating whether the point is in free space.
'line_free_cb' is a function that accepts two points (the start and the
end of a line segment) and outputs a boolen value indicating whether
the line segment is free from obstacles.
'dimensions' is a tuple of tuples that define the x and y dimensions of
the world, e.g. ((-8, 8), (-8, 8)). It should be used when generating
random points.
"""
self.point_free_cb = point_free_cb
self.line_free_cb = line_free_cb
self.dimensions = dimensions
self.search_algorithm = euclidean()
self.number_of_trials = 500
self.search_result = False
self.output_path = None
self.id_generator = 10000
self.graph = graph()
def __init__(self):
self.date = [0,0,0]
self.dt = 0.0
self.modules = {}
self.unit_types = {}
self.building_types = {}
self.cities = {}
self.areas_id = {}
self.units = []
self.groups = {}
self.players = {}
self.countries = {}
self.fights = []
#graph way search
self.areas_graph = digraph()
self.heuristic = euclidean()
self.countries_colors = {None: 0xe2f0e1}
self.globalClock = ClockObject.getGlobalClock()
self.last_t = self.globalClock.getRealTime()
self.dt = 0
self.t = 0
def __init__(self, point_free_cb, line_free_cb, dimensions):
"""
Construct a randomized roadmap planner.
'point_free_cb' is a function that accepts a point (two-tuple) and
outputs a boolean value indicating whether the point is in free space.
'line_free_cb' is a function that accepts two points (the start and the
end of a line segment) and outputs a boolen value indicating whether
the line segment is free from obstacles.
'dimensions' is a tuple of tuples that define the x and y dimensions of
the world, e.g. ((-8, 8), (-8, 8)). It should be used when generating
random points.
"""
rospy.loginfo('Initializing RandomizedRoadmapPlanner')
self.point_free_cb = point_free_cb
self.line_free_cb = line_free_cb
self.dimensions = dimensions
self.node_list = []
self.graph = graph()
self.heuristic = euclidean()
self.node_ids = []
def test_euclidean(self):
heuristic = euclidean()
heuristic.optimize(self.G)
result = pygraph.algorithms.minmax.heuristic_search(self.G, 'A', 'C', heuristic )
assert result == ['A', 'D', 'C']
def heuristic(self):
heuristic = euclidean()
heuristic.optimize(self.graph)
return heuristic
def heuristic(self): heuristic = euclidean() heuristic.optimize(self.graph) return heuristic
from pygraph.classes.digraph import digraph
from pygraph.algorithms.heuristics.chow import chow
from pygraph.algorithms.heuristics.euclidean import euclidean
from pygraph.algorithms.minmax import *
from moga_taskmapping_v2 import *
g = digraph()
g.add_nodes(['ALU0', 'SE0', 'ALU1', 'SE1'])
g.add_node_attribute('ALU0', ('position', (0, 0)))
g.add_node_attribute('SE0', ('position', (0, 0)))
g.add_node_attribute('ALU1', ('position', (1, 0)))
g.add_node_attribute('SE1', ('position', (1, 0)))
g.add_edge(('ALU0', 'SE0'), wt=1)
g.add_edge(('SE0', 'SE1'), wt=1)
g.add_edge(('SE1', 'SE0'), wt=1)
g.add_edge(('SE0', 'ALU1'), wt=1)
g.add_edge(('ALU1', 'SE1'), wt=1)
h = euclidean()
h.optimize(g)
path = heuristic_search(g, 'ALU0', 'ALU1', h)
print(path)
path_evaluation(g, path)
def test_euclidean(self):
heuristic = euclidean()
heuristic.optimize(self.G)
result = pygraph.algorithms.minmax.heuristic_search(
self.G, 'A', 'C', heuristic)
assert result == ['A', 'D', 'C']
def plan(self, point1, point2):
"""
Plan a path which connects the two given 2D points.
The points are represented by tuples of two numbers (x, y).
Return a list of tuples where each tuple represents a point in the
planned path, the first point is the start point, and the last point is
the end point. If the planning algorithm failed the returned list
should be empty.
"""
path = []
#Add start and end
self.node_counter += 1
start = self.node_counter
self.graph.add_node(self.node_counter,
attrs=[('position', (point1[0], point1[1]))])
self.node_counter += 1
end = self.node_counter
self.graph.add_node(self.node_counter,
attrs=[('position', (point2[0], point2[1]))])
new_node_counter = 0
#Create nodes until a solution is found, limit = self.node_limit
while new_node_counter < self.node_limit:
#Add 5 new random nodes each iteration
for i in range(self.number_nodes):
position_x = random.uniform(self.dimensions_x[0],
self.dimensions_x[1])
position_y = random.uniform(self.dimensions_y[0],
self.dimensions_y[1])
if self.point_free_cb((position_x, position_y)):
self.node_counter += 1
new_node_counter += 1
self.graph.add_node(self.node_counter,
attrs=[('position', (position_x,
position_y))])
#Add edges
for nid1, attr in self.graph.node_attr.iteritems():
position1 = attr[0][1]
for nid2, attr2 in self.graph.node_attr.iteritems():
position2 = attr2[0][1]
distance = self.distance(position1, position2)
#Avoid self edges and repetitivity
if distance > 0.0 and self.graph.has_edge(
(nid1, nid2)) == False:
if self.line_free_cb(position1, position2):
self.graph.add_edge((nid1, nid2), wt=distance)
#A* search
try:
heuristic = euclidean()
heuristic.optimize(self.graph)
id_path = heuristic_search(self.graph, start, end, heuristic)
for member in id_path:
attributes = self.graph.node_attributes(member)
path.append(attributes[0][1])
except NodeUnreachable:
rospy.logwarn("Path unreachable, not enough nodes")
else:
return path
#If node limit is passed, then no path is found
rospy.logwarn("Node limit reached, path is not connected")
return path
# tasks_graph = digraph() # tasks_graph.add_nodes(list(range(8))) # tasks_graph.add_edge((0,1)) # tasks_graph.add_edge((1,2)) # tasks_graph.add_edge((1,3)) # tasks_graph.add_edge((3,4)) # tasks_graph.add_edge((4,5)) # tasks_graph.add_edge((4,6)) # tasks_graph.add_edge((5,7)) # tasks_graph.add_edge((6,7)) # tasks, tasks_graph = af_graph() # test_platform_graph = [[0 for i in range(12)] for j in range(8)] # only size required? platform_graph, SE_graph, ALU_graph = generate_cma_graph(PE_row, PE_col) # CMA graph platform_search_graph = euclidean() SE_search_graph = euclidean() ALU_search_graph = euclidean() platform_search_graph.optimize(platform_graph) SE_search_graph.optimize(SE_graph) ALU_search_graph.optimize(ALU_graph) # mapping1 = random_generator(tasks_graph,test_platform_graph) # mapping2 = random_generator(tasks_graph,test_platform_graph) # print(mapping1) # print(mapping2) # val, totval = objectives_eval(tasks_graph,mapping1) # print(val) # print(totval)
def plan(self, point1, point2):
"""
Plan a path which connects the two given 2D points.
The points are represented by tuples of two numbers (x, y).
Return a list of tuples where each tuple represents a point in the
planned path, the first point is the start point, and the last point is
the end point. If the planning algorithm failed the returned list
should be empty.
"""
print "PLANNING METHOD CALLED"
if not self.point_free_cb(point1):
rospy.logwarn("START POINT UNAVAILIBLE")
return []
if not self.point_free_cb(point2):
rospy.logwarn("END POINT UNAVAILIBLE")
return []
if self.line_free_cb(point1, point2):
print "STRAIGHT LINE ROUTE FOUND"
return [point1, point2]
else:
print "EXPANDING MAP"
start_id = self.add_node_to_graph(point1, force=True)
finish_id = self.add_node_to_graph(point2, force=True)
h = euclidean()
while True:
# No DO..UNTIL loops in python. Condition check below
path = []
try:
h.optimize(self.graph)
path = heuristic_search(self.graph, start_id, finish_id, h)
except NodeUnreachable:
# add another node
new_node_added = False
while not new_node_added:
randomPoint = (uniform(*self.dimensions[0]), uniform(*self.dimensions[1]))
if self.point_free_cb(randomPoint):
if self.add_node_to_graph(randomPoint):
new_node_added = True
# DO..UNTIL emulation this way
if len(path) > 0 or self.nodecount >= RandomizedRoadmapPlanner.MAX_NODES_NUM:
print "Break loop"
break
# print path
if len(path) > 0:
found_path = []
rospy.loginfo("PATH FOUND")
for n in path:
found_path.append(self.graph.node_attr.get(n)[0][1])
return found_path
elif self.nodecount >= RandomizedRoadmapPlanner.MAX_NODES_NUM:
rospy.logwarn("MAX NUM NODES ALLOWED EXCEEDED!")
rospy.logwarn("PATH NOT FOUND!")
return []
