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PathPlanning/Stochastic Shortest Path/tools.py
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2020-06-18 14:57:03 -07:00
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: huiming zhou
"""
import matplotlib.pyplot as plt
import environment
def obs_detect(x, u, obs_map):
"""
Detect if the next state is in obstacles using this input.
:param x: current state
:param u: input
:param obs_map: map of obstacles
:return: in obstacles: True / not in obstacles: False
"""
x_next = [x[0] + u[0], x[1] + u[1]] # next state using input 'u'
if u not in environment.motions or \
obs_map[x_next[0]][x_next[1]] == 1: # if 'u' is feasible and next state is not in obstacles
return True
return False
def extract_path(xI, xG, parent, actions):
"""
Extract the path based on the relationship of nodes.
:param xI: Starting node
:param xG: Goal node
:param parent: Relationship between nodes
:param actions: Action needed for transfer between two nodes
:return: The planning path
"""
path_back = [xG]
acts_back = [actions[xG]]
x_current = xG
while True:
x_current = parent[x_current]
path_back.append(x_current)
acts_back.append(actions[x_current])
if x_current == xI: break
return list(reversed(path_back)), list(reversed(acts_back))
def showPath(xI, xG, path, visited, name):
"""
Plot the path.
:param xI: Starting node
:param xG: Goal node
:param path: Planning path
:param visited: Visited nodes
:param name: Name of this figure
:return: A plot
"""
background = environment.obstacles()
fig, ax = plt.subplots()
for k in range(len(visited)):
background[visited[k][1]][visited[k][0]] = [.5, .5, .5] # visited nodes: gray color
for k in range(len(path)):
background[path[k][1]][path[k][0]] = [1., 0., 0.] # path: red color
background[xI[1]][xI[0]] = [0., 0., 1.] # starting node: blue color
background[xG[1]][xG[0]] = [0., 1., .5] # goal node: green color
ax.imshow(background)
ax.invert_yaxis() # put origin of coordinate to left-bottom
plt.title(name, fontdict=None)
plt.show()