Files
PathPlanning/Search-based Planning/bfs.py
T
zhm-real e67c39290f update
2020-06-20 13:04:36 -07:00

82 lines
2.7 KiB
Python

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: huiming zhou
"""
import queue
import plotting
import env
class BFS:
def __init__(self, x_start, x_goal):
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)
self.fig_name = "Breadth-first Searching"
plotting.animation(self.xI, self.xG, self.obs,
self.path, self.visited, self.fig_name) # animation generate
def searching(self, xI, xG):
"""
Searching using BFS.
:return: planning path, action in each node, visited nodes in the planning process
"""
q_bfs = queue.QueueFIFO() # first-in-first-out queue
q_bfs.put(xI)
parent = {xI: xI} # record parents of nodes
action = {xI: (0, 0)} # record actions of nodes
visited = []
while not q_bfs.empty():
x_current = q_bfs.get()
if x_current == xG:
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 parent and x_next not in self.obs: # node not visited and not in obstacles
q_bfs.put(x_next)
parent[x_next], action[x_next] = x_current, u_next
[path, policy] = self.extract_path(xI, xG, parent, action) # extract path
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)
if __name__ == '__main__':
x_Start = (5, 5) # Starting node
x_Goal = (49, 5) # Goal node
bfs = BFS(x_Start, x_Goal)