图论DFS(Depth First Search)Algorithm深度优先搜索遍历空间平面图选择路径,networkx,Python
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图论DFS(Depth First Search)Algorithm深度优先搜索遍历空间平面图选择路径,networkx,Python
程序初始代码是模式0,即随机生成最多20个障碍物点,验证算法的效果。打红色X点表示此路不通。出发点是(1,1),终点是(5,7)。最终红色的点即为选出的路径,并用红色的粗线连起来。
import random
import networkx as nx
import matplotlib.pyplot as plt
WALKABLE = 'walkable'
PARENT = 'parent'
VISITED = 'visited'
def my_graph():
M = 7
N = 9
G = nx.grid_2d_graph(m=M, n=N)
pos = nx.spring_layout(G, iterations=100)
nx.draw_networkx(G, pos=pos,
# labels=labels, #labels = dict(((i, j), 'Phil') for i, j in G.nodes())
font_size=8,
font_color='white',
node_color='green',
node_size=500,
width=1)
START = (1, 1)
GOAL = (M - 1 - 1, N - 1 - 1)
# 0,随机生成障碍物点
# 1,精心挑选的障碍物构成陷阱
obstacle_mode = 0
road_closed_nodes = []
if obstacle_mode == 0:
obstacle = 20 # 障碍物(断点、不可达点)数量
road_closed_nodes = obstacle_nodes(G, START, GOAL, obstacle, M, N)
elif obstacle_mode == 1:
road_closed_nodes = dummy_nodes(G)
nx.draw_networkx_nodes(
G, pos,
nodelist=road_closed_nodes,
node_size=500,
node_color="red",
node_shape="x",
# alpha=0.3,
label='x'
)
dfs(G, START, GOAL)
path = find_path_by_parent(G, START, GOAL)
print('path', path)
nx.draw_networkx_nodes(
G, pos,
nodelist=path,
node_size=400,
node_color="red",
node_shape='o',
# alpha=0.3,
# label='NO'
)
# nx.draw_networkx_nodes(
# G, pos,
# nodelist=path,
# node_size=100,
# node_color="yellow",
# node_shape='*',
# # alpha=0.3,
# # label='NO'
# )
path_edges = []
for i in range(len(path)):
if (i + 1) == len(path):
break
path_edges.append((path[i], path[i + 1]))
print('path_edges', path_edges)
# 把path着色加粗重新描边
nx.draw_networkx_edges(G, pos,
edgelist=path_edges,
width=8,
alpha=0.5,
edge_color="r")
plt.axis('off')
plt.show()
# 基于栈实深度优先遍历搜索
def dfs(G, START, GOAL):
for n in G.nodes():
G.nodes[n]['visited'] = False
stack = [] # 用列表当作一个栈,只在栈顶操作(数组的第1个位置)
stack.append(START)
close_list = []
while True:
if len(stack) == 0:
break
print('-----')
print('stack-', stack)
visit_node = stack[0]
G.nodes[visit_node]['visited'] = True
print('访问', visit_node)
if visit_node == GOAL:
break
close_list.append(visit_node)
count = 0
neighbors = nx.neighbors(G, visit_node)
for node in neighbors:
visited = G.nodes[node][VISITED]
try:
walkable = G.nodes[node][WALKABLE]
except:
walkable = True
if (visited) or (node in stack) or (node in close_list) or (not walkable):
continue
G.nodes[node][PARENT] = visit_node
stack.append(node)
count = count + 1
if count == 0:
print(visit_node, '尽头')
del (stack[0])
print('弹出', visit_node)
print('stack--', stack)
return stack
def find_path_by_parent(G, START, GOAL):
t = GOAL
path = [t]
is_find = False
while not is_find:
for n in G.nodes(data=True):
if n[0] == t:
parent = n[1][PARENT]
path.append(parent)
if parent == START:
is_find = True
break
t = parent
list.reverse(path)
return path
# 障碍物点
def obstacle_nodes(G, START, GOAL, obstacle, M, N):
road_closed_nodes = []
for i in range(obstacle):
n = (random.randint(0, M - 1), random.randint(0, N - 1))
if n == START or n == GOAL:
continue
if n in road_closed_nodes:
continue
G.nodes[n][WALKABLE] = False
road_closed_nodes.append(n)
return road_closed_nodes
def dummy_nodes(G):
fun_nodes = []
n0 = (1, 2)
G.nodes[n0][WALKABLE] = False
fun_nodes.append(n0)
n1 = (1, 3)
G.nodes[n1][WALKABLE] = False
fun_nodes.append(n1)
n2 = (1, 4)
G.nodes[n2][WALKABLE] = False
fun_nodes.append(n2)
n3 = (1, 5)
G.nodes[n3][WALKABLE] = False
fun_nodes.append(n3)
n4 = (1, 6)
G.nodes[n4][WALKABLE] = False
fun_nodes.append(n4)
n5 = (2, 6)
G.nodes[n5][WALKABLE] = False
fun_nodes.append(n5)
n6 = (3, 6)
G.nodes[n6][WALKABLE] = False
fun_nodes.append(n6)
n7 = (4, 6)
G.nodes[n7][WALKABLE] = False
fun_nodes.append(n7)
n8 = (5, 6)
G.nodes[n8][WALKABLE] = False
fun_nodes.append(n8)
n9 = (5, 5)
G.nodes[n9][WALKABLE] = False
fun_nodes.append(n9)
n10 = (5, 4)
G.nodes[n10][WALKABLE] = False
fun_nodes.append(n10)
n11 = (5, 3)
G.nodes[n11][WALKABLE] = False
fun_nodes.append(n11)
n12 = (5, 2)
G.nodes[n12][WALKABLE] = False
fun_nodes.append(n12)
return fun_nodes
if __name__ == '__main__':
my_graph()
算法选路效果:
由于代码中对于随机生成的障碍物点限制不多(不等于出发点和终点即可),那么极大概率生成的点集把出发点到终点之间的路线堵死,最终选路选不出来,出于简单代码说明算法的目的,程序对这些情况未增加代码量规避处理。
现在,使用障碍物模式1,
obstacle_mode = 1
故意生成一组精心挑选的点,这些点形成一个凹形的围墙,围墙正面对出发点,同时把出发点改成(2,2),
START = (2, 2)
看看算法的表现:
算法走出的路线很聪明,机智的以捷径绕过围墙。
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