# Reduce computing time of planning method (grid)

Has anyone an idea how to change the planning(self, sx, sy, gx, gy)-method to save computing time? I'm new to NumPy and don't know how to use it effectively yet, but I heard it could also be a good solution.

import math
import matplotlib.pyplot as plt

show_animation = True

class AStarPlanner:

def __init__(self, ox, oy, resolution, rr):
"""
Initialize grid map for a star planning

ox: x position list of Obstacles [m]
oy: y position list of Obstacles [m]
resolution: grid resolution [m]
"""

self.resolution = resolution
self.rr = rr
self.min_x, self.min_y = 0, 0
self.max_x, self.max_y = 0, 0
self.obstacle_map = None
self.x_width, self.y_width = 0, 0
self.motion = self.get_motion_model()
self.calc_obstacle_map(ox, oy)

class Node:
def __init__(self, x, y, cost, parent_index):
self.x = x  # index of grid
self.y = y  # index of grid
self.cost = cost
self.parent_index = parent_index

def __str__(self):
return str(self.x) + "," + str(self.y) + "," + str(
self.cost) + "," + str(self.parent_index)

def planning(self, sx, sy, gx, gy):
"""
A star path search

input:
s_x: start x position [m]
s_y: 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
"""

start_node = self.Node(self.calc_xy_index(sx, self.min_x),
self.calc_xy_index(sy, self.min_y), 0.0, -1)
goal_node = self.Node(self.calc_xy_index(gx, self.min_x),
self.calc_xy_index(gy, self.min_y), 0.0, -1)

open_set, closed_set = dict(), dict()
open_set[self.calc_grid_index(start_node)] = start_node

while 1:
if len(open_set) == 0:
print("Open set is empty..")
break

c_id = min(
open_set,
key=lambda o: open_set[o].cost + self.calc_heuristic(goal_node,
open_set[
o]))
current = open_set[c_id]

if current.x == goal_node.x and current.y == goal_node.y:
print("Found goal")
goal_node.parent_index = current.parent_index
goal_node.cost = current.cost
break

# Remove the item from the open set
del open_set[c_id]

# Add it to the closed set
closed_set[c_id] = current

# 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)
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  # discovered a new node
else:
if open_set[n_id].cost > node.cost:
# This path is the best until now. record it
open_set[n_id] = node

rx, ry = self.calc_final_path(goal_node, closed_set)

return rx, ry

def calc_final_path(self, goal_node, closed_set):
# generate final course
rx, ry = [self.calc_grid_position(goal_node.x, self.min_x)], [
self.calc_grid_position(goal_node.y, self.min_y)]
parent_index = goal_node.parent_index
while parent_index != -1:
n = closed_set[parent_index]
rx.append(self.calc_grid_position(n.x, self.min_x))
ry.append(self.calc_grid_position(n.y, self.min_y))
parent_index = n.parent_index

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, min_position):
"""
calc grid position

:param index:
:param min_position:
:return:
"""
pos = index * self.resolution + min_position
return int(pos)

def decalc_grid_position(self, pos, min_position):
index = (pos - min_position) / self.resolution

return int(index)

def calc_xy_index(self, position, min_pos):
return round((position - min_pos) / self.resolution)

def calc_grid_index(self, node):
return (node.y - self.min_y) * self.x_width + (node.x - self.min_x)

def verify_node(self, node):
px = self.calc_grid_position(node.x, self.min_x)
py = self.calc_grid_position(node.y, self.min_y)

if px < self.min_x:
return False
elif py < self.min_y:
return False
elif px >= self.max_x:
return False
elif py >= self.max_y:
return False

# collision check
if self.obstacle_map[node.x][node.y]:
return False

return True

""" Compute and store table of offsets of cells within a robot's radius.
"""
for x in range(int(-self.rr), int(self.rr) + 1):
for y in range(int(-self.rr), int(self.rr) + 1):
if math.hypot(x, y) < self.rr:

def calc_obstacle_map(self, ox, oy):

self.min_x = round(min(ox))
self.min_y = round(min(oy))
self.max_x = round(max(ox))
self.max_y = round(max(oy))
print("min_x:", self.min_x)
print("min_y:", self.min_y)
print("max_x:", self.max_x)
print("max_y:", self.max_y)

self.x_width = round((self.max_x - self.min_x) / self.resolution)
self.y_width = round((self.max_y - self.min_y) / self.resolution)
print("x_width:", self.x_width)
print("y_width:", self.y_width)

self.obstacle_map = [[False for _ in range(len(oy))] for _ in range(len(ox))]

obstacles = zip(ox, oy)
for ox, oy in obstacles:
ox = self.decalc_grid_position(ox, self.min_x)
oy = self.decalc_grid_position(oy, self.min_y)
self.obstacle_map[int(ox + dx)][int(oy + dy)] = True

@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 = 55.0  # [m]
gy = -5.0  # [m]
grid_size = 2.0  # [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")

a_star = AStarPlanner(ox, oy, grid_size, robot_radius)
rx, ry = a_star.planning(sx, sy, gx, gy)

if show_animation:  # pragma: no cover
plt.plot(rx, ry, "-r")
plt.pause(0.001)
plt.show()

if __name__ == '__main__':
main()