mirror of
https://github.com/zhm-real/PathPlanning.git
synced 2026-08-30 00:50:46 +08:00
zhm-real
121 lines
3.3 KiB
Python
121 lines
3.3 KiB
Python
import queue
|
|
import plotting
|
|
import env
|
|
|
|
|
|
class Astar:
|
|
def __init__(self, x_start, x_goal, e, heuristic_type):
|
|
self.xI, self.xG = x_start, x_goal
|
|
self.heuristic_type = heuristic_type
|
|
|
|
self.Env = env.Env() # class Env
|
|
|
|
self.e = e
|
|
self.u_set = self.Env.motions # feasible input set
|
|
self.obs = self.Env.obs # position of obstacles
|
|
|
|
self.g = {self.xI: 0, self.xG: float("inf")}
|
|
self.fig_name = "A* Algorithm"
|
|
|
|
self.OPEN = queue.QueuePrior() # priority queue / OPEN
|
|
self.OPEN.put(self.xI, self.fvalue(self.xI))
|
|
self.parent = {self.xI: self.xI}
|
|
|
|
def searching(self):
|
|
"""
|
|
Searching using A_star.
|
|
|
|
:return: planning path, action in each node, visited nodes in the planning process
|
|
"""
|
|
|
|
visited = []
|
|
|
|
while not self.OPEN.empty():
|
|
s = self.OPEN.get()
|
|
if s == self.xG: # stop condition
|
|
break
|
|
visited.append(s)
|
|
for u_next in self.u_set: # explore neighborhoods of current node
|
|
s_next = tuple([s[i] + u_next[i] for i in range(len(s))])
|
|
if s_next not in self.obs:
|
|
new_cost = self.g[s] + self.get_cost(s, u_next)
|
|
if s_next not in self.g or new_cost < self.g[s_next]: # conditions for updating cost
|
|
self.g[s_next] = new_cost
|
|
self.parent[s_next] = s
|
|
self.OPEN.put(s_next, self.fvalue(s_next))
|
|
|
|
return self.extract_path(), visited
|
|
|
|
def fvalue(self, x):
|
|
h = self.e * self.Heuristic(x)
|
|
return self.g[x] + h
|
|
|
|
def extract_path(self):
|
|
"""
|
|
Extract the path based on the relationship of nodes.
|
|
|
|
:return: The planning path
|
|
"""
|
|
|
|
path_back = [self.xG]
|
|
x_current = self.xG
|
|
|
|
while True:
|
|
x_current = self.parent[x_current]
|
|
path_back.append(x_current)
|
|
|
|
if x_current == self.xI:
|
|
break
|
|
|
|
return list(path_back)
|
|
|
|
@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 Heuristic(self, state):
|
|
"""
|
|
Calculate heuristic.
|
|
|
|
:param state: current node (state)
|
|
:param goal: goal node (state)
|
|
:param heuristic_type: choosing different heuristic functions
|
|
:return: heuristic
|
|
"""
|
|
|
|
heuristic_type = self.heuristic_type
|
|
goal = self.xG
|
|
|
|
if heuristic_type == "manhattan":
|
|
return abs(goal[0] - state[0]) + abs(goal[1] - state[1])
|
|
elif heuristic_type == "euclidean":
|
|
return ((goal[0] - state[0]) ** 2 + (goal[1] - state[1]) ** 2) ** (1 / 2)
|
|
else:
|
|
print("Please choose right heuristic type!")
|
|
|
|
|
|
def main():
|
|
x_start = (5, 5) # Starting node
|
|
x_goal = (49, 5) # Goal node
|
|
|
|
astar = Astar(x_start, x_goal, 1, "manhattan")
|
|
plot = plotting.Plotting(x_start, x_goal) # class Plotting
|
|
|
|
fig_name = "A* Algorithm"
|
|
path, visited = astar.searching()
|
|
|
|
plot.animation(path, visited, fig_name) # animation generate
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|