mirror of
https://github.com/zhm-real/PathPlanning.git
synced 2026-08-30 00:50:46 +08:00
zhm-real
118 lines
3.9 KiB
Python
118 lines
3.9 KiB
Python
#!/usr/bin/env python3
|
|
# -*- coding: utf-8 -*-
|
|
"""
|
|
@author: huiming zhou
|
|
"""
|
|
|
|
import queue
|
|
import plotting
|
|
import env
|
|
|
|
|
|
class Astar:
|
|
def __init__(self, x_start, x_goal, heuristic_type):
|
|
self.xI, self.xG = x_start, x_goal
|
|
|
|
self.Env = env.Env()
|
|
self.u_set = self.Env.motions # feasible input set
|
|
self.obs = self.Env.obs # position of obstacles
|
|
|
|
[self.path, self.policy, self.visited] = self.searching(self.xI, self.xG, heuristic_type)
|
|
|
|
self.fig_name = "A* Algorithm"
|
|
plotting.animation(self.xI, self.xG, self.obs,
|
|
self.path, self.visited, self.fig_name) # animation generate
|
|
|
|
|
|
def searching(self, xI, xG, heuristic_type):
|
|
"""
|
|
Searching using A_star.
|
|
|
|
:return: planning path, action in each node, visited nodes in the planning process
|
|
"""
|
|
|
|
q_astar = queue.QueuePrior() # priority queue
|
|
q_astar.put(xI, 0)
|
|
parent = {xI: xI} # record parents of nodes
|
|
action = {xI: (0, 0)} # record actions of nodes
|
|
visited = []
|
|
cost = {xI: 0}
|
|
|
|
while not q_astar.empty():
|
|
x_current = q_astar.get()
|
|
if x_current == xG: # stop condition
|
|
break
|
|
visited.append(x_current)
|
|
for u_next in self.u_set: # explore neighborhoods of current node
|
|
x_next = tuple([x_current[i] + u_next[i] for i in range(len(x_current))])
|
|
if x_next not in self.obs:
|
|
new_cost = cost[x_current] + self.get_cost(x_current, u_next)
|
|
if x_next not in cost or new_cost < cost[x_next]: # conditions for updating cost
|
|
cost[x_next] = new_cost
|
|
priority = new_cost + self.Heuristic(x_next, xG, heuristic_type)
|
|
q_astar.put(x_next, priority) # put node into queue using priority "f+h"
|
|
parent[x_next], action[x_next] = x_current, u_next
|
|
|
|
[path, policy] = self.extract_path(xI, xG, parent, action)
|
|
|
|
return path, policy, visited
|
|
|
|
|
|
def extract_path(self, xI, xG, parent, policy):
|
|
"""
|
|
Extract the path based on the relationship of nodes.
|
|
|
|
:param xI: Starting node
|
|
:param xG: Goal node
|
|
:param parent: Relationship between nodes
|
|
:param policy: Action needed for transfer between two nodes
|
|
:return: The planning path
|
|
"""
|
|
|
|
path_back = [xG]
|
|
acts_back = [policy[xG]]
|
|
x_current = xG
|
|
while True:
|
|
x_current = parent[x_current]
|
|
path_back.append(x_current)
|
|
acts_back.append(policy[x_current])
|
|
if x_current == xI: break
|
|
|
|
return list(path_back), list(acts_back)
|
|
|
|
|
|
def get_cost(self, 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, goal, heuristic_type):
|
|
"""
|
|
Calculate heuristic.
|
|
|
|
:param state: current node (state)
|
|
:param goal: goal node (state)
|
|
:param heuristic_type: choosing different heuristic functions
|
|
:return: heuristic
|
|
"""
|
|
|
|
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!")
|
|
|
|
|
|
if __name__ == '__main__':
|
|
x_Start = (5, 5) # Starting node
|
|
x_Goal = (49, 5) # Goal node
|
|
astar = Astar(x_Start, x_Goal, "manhattan") |