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Code Issues Packages Projects Releases Wiki Activity

Greedy Best-First Search (#315)

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This commit is contained in:
Erwin Lejeune
2020-05-05 07:06:01 +02:00
committed by GitHub
parent ab49acb1ac
commit 734f3aed20
5 changed files with 326 additions and 46 deletions
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2
PathPlanning/AStar/a_star.py
View File

@@ -237,7 +237,7 @@ def main():
grid_size = 2.0 # [m]
robot_radius = 1.0 # [m]
# set obstable positions
# set obstacle positions
ox, oy = [], []
for i in range(-10, 60):
ox.append(i)

78
PathPlanning/BidirectionalAStar/bidirectional_a_star.py
View File

@@ -100,8 +100,9 @@ class BidirectionalAStarPlanner:
self.calc_grid_position(current_B.y, self.miny), "xc")
# for stopping simulation with the esc key.
plt.gcf().canvas.mpl_connect('key_release_event',
lambda event: [exit(
0) if event.key == 'escape' else None])
lambda event:
[exit(0) if event.key == 'escape'
else None])
if len(closed_set_A.keys()) % 10 == 0:
plt.pause(0.001)
@@ -121,61 +122,50 @@ class BidirectionalAStarPlanner:
# expand_grid search grid based on motion model
for i, _ in enumerate(self.motion):
continue_A = False
continue_B = False
child_node_A = self.Node(current_A.x + self.motion[i][0],
current_A.y + self.motion[i][1],
current_A.cost + self.motion[i][2],
c_id_A)
c_nodes = [self.Node(current_A.x + self.motion[i][0],
current_A.y + self.motion[i][1],
current_A.cost + self.motion[i][2],
c_id_A),
self.Node(current_B.x + self.motion[i][0],
current_B.y + self.motion[i][1],
current_B.cost + self.motion[i][2],
c_id_B)]
child_node_B = self.Node(current_B.x + self.motion[i][0],
current_B.y + self.motion[i][1],
current_B.cost + self.motion[i][2],
c_id_B)
n_id_A = self.calc_grid_index(child_node_A)
n_id_B = self.calc_grid_index(child_node_B)
n_ids = [self.calc_grid_index(c_nodes[0]),
self.calc_grid_index(c_nodes[1])]
# If the node is not safe, do nothing
if not self.verify_node(child_node_A):
continue_A = True
continue_ = self.check_nodes_and_sets(c_nodes, closed_set_A,
closed_set_B, n_ids)
if not self.verify_node(child_node_B):
continue_B = True
if n_id_A in closed_set_A:
continue_A = True
if n_id_B in closed_set_B:
continue_B = True
if not continue_A:
if n_id_A not in open_set_A:
if not continue_[0]:
if n_ids[0] not in open_set_A:
# discovered a new node
open_set_A[n_id_A] = child_node_A
open_set_A[n_ids[0]] = c_nodes[0]
else:
if open_set_A[n_id_A].cost > child_node_A.cost:
if open_set_A[n_ids[0]].cost > c_nodes[0].cost:
# This path is the best until now. record it
open_set_A[n_id_A] = child_node_A
open_set_A[n_ids[0]] = c_nodes[0]
if not continue_B:
if n_id_B not in open_set_B:
if not continue_[1]:
if n_ids[1] not in open_set_B:
# discovered a new node
open_set_B[n_id_B] = child_node_B
open_set_B[n_ids[1]] = c_nodes[1]
else:
if open_set_B[n_id_B].cost > child_node_B.cost:
if open_set_B[n_ids[1]].cost > c_nodes[1].cost:
# This path is the best until now. record it
open_set_B[n_id_B] = child_node_B
open_set_B[n_ids[1]] = c_nodes[1]
rx, ry = self.calc_final_bidirectional_path(
meetpointA, meetpointB, closed_set_A, closed_set_B)
return rx, ry
def calc_final_bidirectional_path(self, meetnode_A, meetnode_B, closed_set_A, closed_set_B):
rx_A, ry_A = self.calc_final_path(meetnode_A, closed_set_A)
rx_B, ry_B = self.calc_final_path(meetnode_B, closed_set_B)
# takes two sets and two meeting nodes and return the optimal path
def calc_final_bidirectional_path(self, n1, n2, setA, setB):
rx_A, ry_A = self.calc_final_path(n1, setA)
rx_B, ry_B = self.calc_final_path(n2, setB)
rx_A.reverse()
ry_A.reverse()
@@ -198,6 +188,16 @@ class BidirectionalAStarPlanner:
return rx, ry
def check_nodes_and_sets(self, c_nodes, closedSet_A, closedSet_B, n_ids):
continue_ = [False, False]
if not self.verify_node(c_nodes[0]) or n_ids[0] in closedSet_A:
continue_[0] = True
if not self.verify_node(c_nodes[1]) or n_ids[1] in closedSet_B:
continue_[1] = True
return continue_
@staticmethod
def calc_heuristic(n1, n2):
w = 1.0 # weight of heuristic

7
PathPlanning/BreadthFirstSearch/breadth_first_search.py
View File

@@ -84,8 +84,9 @@ class BreadthFirstSearchPlanner:
self.calc_grid_position(current.y, self.miny), "xc")
# for stopping simulation with the esc key.
plt.gcf().canvas.mpl_connect('key_release_event',
lambda event: [exit(
0) if event.key == 'escape' else None])
lambda event:
[exit(0) if event.key == 'escape'
else None])
if len(closed_set.keys()) % 10 == 0:
plt.pause(0.001)
@@ -216,7 +217,7 @@ def main():
grid_size = 2.0 # [m]
robot_radius = 1.0 # [m]
# set obstable positions
# set obstacle positions
ox, oy = [], []
for i in range(-10, 60):
ox.append(i)

7
PathPlanning/DepthFirstSearch/depth_first_search.py
View File

@@ -81,8 +81,9 @@ class DepthFirstSearchPlanner:
self.calc_grid_position(current.y, self.miny), "xc")
# for stopping simulation with the esc key.
plt.gcf().canvas.mpl_connect('key_release_event',
lambda event: [exit(
0) if event.key == 'escape' else None])
lambda event:
[exit(0) if event.key == 'escape'
else None])
plt.pause(0.01)
if current.x == ngoal.x and current.y == ngoal.y:
@@ -213,7 +214,7 @@ def main():
grid_size = 2.0 # [m]
robot_radius = 1.0 # [m]
# set obstable positions
# set obstacle positions
ox, oy = [], []
for i in range(-10, 60):
ox.append(i)

278
PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py Normal file
View File

@@ -0,0 +1,278 @@
"""
Greedy Best-First grid planning
author: Erwin Lejeune (@spida_rwin)
See Wikipedia article (https://en.wikipedia.org/wiki/Best-first_search)
"""
import math
import matplotlib.pyplot as plt
show_animation = True
class BestFirstSearchPlanner:
def __init__(self, ox, oy, reso, rr):
"""
Initialize grid map for greedy best-first planning
ox: x position list of Obstacles [m]
oy: y position list of Obstacles [m]
reso: grid resolution [m]
rr: robot radius[m]
"""
self.reso = reso
self.rr = rr
self.calc_obstacle_map(ox, oy)
self.motion = self.get_motion_model()
class Node:
def __init__(self, x, y, cost, pind, parent):
self.x = x # index of grid
self.y = y # index of grid
self.cost = cost
self.pind = pind
self.parent = parent
def __str__(self):
return str(self.x) + "," + str(self.y) + "," + str(
self.cost) + "," + str(self.pind)
def planning(self, sx, sy, gx, gy):
"""
Greedy Best-First search
input:
sx: start x position [m]
sy: start y position [m]
gx: goal x position [m]
gy: goal y position [m]
output:
rx: x position list of the final path
ry: y position list of the final path
"""
nstart = self.Node(self.calc_xyindex(sx, self.minx),
self.calc_xyindex(sy, self.miny), 0.0, -1, None)
ngoal = self.Node(self.calc_xyindex(gx, self.minx),
self.calc_xyindex(gy, self.miny), 0.0, -1, None)
open_set, closed_set = dict(), dict()
open_set[self.calc_grid_index(nstart)] = nstart
while 1:
if len(open_set) == 0:
print("Open set is empty..")
break
c_id = min(
open_set,
key=lambda o: self.calc_heuristic(ngoal, open_set[o]))
current = open_set[c_id]
# show graph
if show_animation: # pragma: no cover
plt.plot(self.calc_grid_position(current.x, self.minx),
self.calc_grid_position(current.y, self.miny), "xc")
# for stopping simulation with the esc key.
plt.gcf().canvas.mpl_connect('key_release_event',
lambda event:
[exit(0)
if event.key == 'escape'
else None])
if len(closed_set.keys()) % 10 == 0:
plt.pause(0.001)
# Remove the item from the open set
del open_set[c_id]
# Add it to the closed set
closed_set[c_id] = current
if current.x == ngoal.x and current.y == ngoal.y:
print("Found goal")
ngoal.pind = current.pind
ngoal.cost = current.cost
break
# expand_grid search grid based on motion model
for i, _ in enumerate(self.motion):
node = self.Node(current.x + self.motion[i][0],
current.y + self.motion[i][1],
current.cost + self.motion[i][2],
c_id, current)
n_id = self.calc_grid_index(node)
# If the node is not safe, do nothing
if not self.verify_node(node):
continue
if n_id in closed_set:
continue
if n_id not in open_set:
open_set[n_id] = node
else:
if open_set[n_id].cost > node.cost:
open_set[n_id] = node
closed_set[ngoal.pind] = current
rx, ry = self.calc_final_path(ngoal, closed_set)
return rx, ry
def calc_final_path(self, ngoal, closedset):
# generate final course
rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [
self.calc_grid_position(ngoal.y, self.miny)]
n = closedset[ngoal.pind]
while n is not None:
rx.append(self.calc_grid_position(n.x, self.minx))
ry.append(self.calc_grid_position(n.y, self.miny))
n = n.parent
return rx, ry
@staticmethod
def calc_heuristic(n1, n2):
w = 1.0 # weight of heuristic
d = w * math.hypot(n1.x - n2.x, n1.y - n2.y)
return d
def calc_grid_position(self, index, minp):
"""
calc grid position
:param index:
:param minp:
:return:
"""
pos = index * self.reso + minp
return pos
def calc_xyindex(self, position, min_pos):
return round((position - min_pos) / self.reso)
def calc_grid_index(self, node):
return (node.y - self.miny) * self.xwidth + (node.x - self.minx)
def verify_node(self, node):
px = self.calc_grid_position(node.x, self.minx)
py = self.calc_grid_position(node.y, self.miny)
if px < self.minx:
return False
elif py < self.miny:
return False
elif px >= self.maxx:
return False
elif py >= self.maxy:
return False
# collision check
if self.obmap[node.x][node.y]:
return False
return True
def calc_obstacle_map(self, ox, oy):
self.minx = round(min(ox))
self.miny = round(min(oy))
self.maxx = round(max(ox))
self.maxy = round(max(oy))
print("minx:", self.minx)
print("miny:", self.miny)
print("maxx:", self.maxx)
print("maxy:", self.maxy)
self.xwidth = round((self.maxx - self.minx) / self.reso)
self.ywidth = round((self.maxy - self.miny) / self.reso)
print("xwidth:", self.xwidth)
print("ywidth:", self.ywidth)
# obstacle map generation
self.obmap = [[False for _ in range(self.ywidth)]
for _ in range(self.xwidth)]
for ix in range(self.xwidth):
x = self.calc_grid_position(ix, self.minx)
for iy in range(self.ywidth):
y = self.calc_grid_position(iy, self.miny)
for iox, ioy in zip(ox, oy):
d = math.hypot(iox - x, ioy - y)
if d <= self.rr:
self.obmap[ix][iy] = True
break
@staticmethod
def get_motion_model():
# dx, dy, cost
motion = [[1, 0, 1],
[0, 1, 1],
[-1, 0, 1],
[0, -1, 1],
[-1, -1, math.sqrt(2)],
[-1, 1, math.sqrt(2)],
[1, -1, math.sqrt(2)],
[1, 1, math.sqrt(2)]]
return motion
def main():
print(__file__ + " start!!")
# start and goal position
sx = 10.0 # [m]
sy = 10.0 # [m]
gx = 50.0 # [m]
gy = 50.0 # [m]
grid_size = 2.0 # [m]
robot_radius = 1.0 # [m]
# set obstacle positions
ox, oy = [], []
for i in range(-10, 60):
ox.append(i)
oy.append(-10.0)
for i in range(-10, 60):
ox.append(60.0)
oy.append(i)
for i in range(-10, 61):
ox.append(i)
oy.append(60.0)
for i in range(-10, 61):
ox.append(-10.0)
oy.append(i)
for i in range(-10, 40):
ox.append(20.0)
oy.append(i)
for i in range(0, 40):
ox.append(40.0)
oy.append(60.0 - i)
if show_animation: # pragma: no cover
plt.plot(ox, oy, ".k")
plt.plot(sx, sy, "og")
plt.plot(gx, gy, "xb")
plt.grid(True)
plt.axis("equal")
greedybestfirst = BestFirstSearchPlanner(ox, oy, grid_size, robot_radius)
rx, ry = greedybestfirst.planning(sx, sy, gx, gy)
if show_animation: # pragma: no cover
plt.plot(rx, ry, "-r")
plt.pause(0.01)
plt.show()
if __name__ == '__main__':
main()