mirror of
https://github.com/zhm-real/PathPlanning.git
synced 2026-08-30 00:50:46 +08:00
zhm-real
150 lines
5.0 KiB
Python
150 lines
5.0 KiB
Python
"""
|
|
Bidirectional_a_star 2D
|
|
@author: huiming zhou
|
|
"""
|
|
|
|
import os
|
|
import sys
|
|
|
|
sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
|
|
"/../../Search-based Planning/")
|
|
|
|
from Search_2D import queue
|
|
from Search_2D import plotting
|
|
from Search_2D import env
|
|
|
|
|
|
class BidirectionalAstar:
|
|
def __init__(self, x_start, x_goal, heuristic_type):
|
|
self.xI, self.xG = x_start, x_goal
|
|
self.heuristic_type = heuristic_type
|
|
|
|
self.Env = env.Env() # class Env
|
|
|
|
self.u_set = self.Env.motions # feasible input set
|
|
self.obs = self.Env.obs # position of obstacles
|
|
|
|
self.g_fore = {self.xI: 0, self.xG: float("inf")} # cost to come: from x_start
|
|
self.g_back = {self.xG: 0, self.xI: float("inf")} # cost to come: form x_goal
|
|
|
|
self.OPEN_fore = queue.QueuePrior() # OPEN set for foreward searching
|
|
self.OPEN_fore.put(self.xI, self.g_fore[self.xI] + self.h(self.xI, self.xG))
|
|
self.OPEN_back = queue.QueuePrior() # OPEN set for backward searching
|
|
self.OPEN_back.put(self.xG, self.g_back[self.xG] + self.h(self.xG, self.xI))
|
|
|
|
self.CLOSED_fore = [] # CLOSED set for foreward
|
|
self.CLOSED_back = [] # CLOSED set for backward
|
|
|
|
self.PARENT_fore = {self.xI: self.xI}
|
|
self.PARENT_back = {self.xG: self.xG}
|
|
|
|
def searching(self):
|
|
s_meet = self.xI
|
|
|
|
while not self.OPEN_fore.empty() and not self.OPEN_back.empty():
|
|
# solve foreward-search
|
|
s_fore = self.OPEN_fore.get()
|
|
if s_fore in self.PARENT_back:
|
|
s_meet = s_fore
|
|
break
|
|
self.CLOSED_fore.append(s_fore)
|
|
|
|
for u in self.u_set:
|
|
s_next = tuple([s_fore[i] + u[i] for i in range(2)])
|
|
if s_next not in self.obs:
|
|
new_cost = self.g_fore[s_fore] + self.get_cost(s_fore, u)
|
|
if s_next not in self.g_fore:
|
|
self.g_fore[s_next] = float("inf")
|
|
if new_cost < self.g_fore[s_next]:
|
|
self.g_fore[s_next] = new_cost
|
|
self.PARENT_fore[s_next] = s_fore
|
|
self.OPEN_fore.put(s_next, new_cost + self.h(s_next, self.xG))
|
|
|
|
# solve backward-search
|
|
s_back = self.OPEN_back.get()
|
|
if s_back in self.PARENT_fore:
|
|
s_meet = s_back
|
|
break
|
|
self.CLOSED_back.append(s_back)
|
|
|
|
for u in self.u_set:
|
|
s_next = tuple([s_back[i] + u[i] for i in range(len(s_back))])
|
|
if s_next not in self.obs:
|
|
new_cost = self.g_back[s_back] + self.get_cost(s_back, u)
|
|
if s_next not in self.g_back:
|
|
self.g_back[s_next] = float("inf")
|
|
if new_cost < self.g_back[s_next]:
|
|
self.g_back[s_next] = new_cost
|
|
self.PARENT_back[s_next] = s_back
|
|
self.OPEN_back.put(s_next, new_cost + self.h(s_next, self.xI))
|
|
|
|
return self.extract_path(s_meet), self.CLOSED_fore, self.CLOSED_back
|
|
|
|
def extract_path(self, s_meet):
|
|
# extract path for foreward part
|
|
path_fore = [s_meet]
|
|
s = s_meet
|
|
|
|
while True:
|
|
s = self.PARENT_fore[s]
|
|
path_fore.append(s)
|
|
if s == self.xI:
|
|
break
|
|
|
|
# extract path for backward part
|
|
path_back = []
|
|
s = s_meet
|
|
|
|
while True:
|
|
s = self.PARENT_back[s]
|
|
path_back.append(s)
|
|
if s == self.xG:
|
|
break
|
|
|
|
return list(reversed(path_fore)) + list(path_back)
|
|
|
|
def h(self, s, goal):
|
|
"""
|
|
Calculate heuristic value.
|
|
:param s: current node (state)
|
|
:param goal: goal node (state)
|
|
:return: heuristic value
|
|
"""
|
|
|
|
heuristic_type = self.heuristic_type
|
|
|
|
if heuristic_type == "manhattan":
|
|
return abs(goal[0] - s[0]) + abs(goal[1] - s[1])
|
|
elif heuristic_type == "euclidean":
|
|
return ((goal[0] - s[0]) ** 2 + (goal[1] - s[1]) ** 2) ** (1 / 2)
|
|
else:
|
|
print("Please choose right heuristic type!")
|
|
|
|
@staticmethod
|
|
def get_cost(x, u):
|
|
"""
|
|
Calculate cost for this motion
|
|
:param x: current node
|
|
:param u: input
|
|
:return: cost for this motion
|
|
:note: cost function could be more complicate!
|
|
"""
|
|
|
|
return 1
|
|
|
|
|
|
def main():
|
|
x_start = (5, 5)
|
|
x_goal = (45, 25)
|
|
|
|
bastar = BidirectionalAstar(x_start, x_goal, "euclidean")
|
|
plot = plotting.Plotting(x_start, x_goal)
|
|
fig_name = "Bidirectional-A*"
|
|
|
|
path, visited_fore, visited_back = bastar.searching()
|
|
plot.animation_bi_astar(path, visited_fore, visited_back, fig_name) # animation
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|