1. 从迪杰斯特拉开始说起
老生常谈，具体实现直接上对应代码：
"""
迪杰斯特拉算法用于路径规划
"""
"""
迪杰斯特拉算法用于路径规划
其实这个算法的思路很明确，就是一个优先队列
从某点开始，
1. 弹出头
2. 将弹出头的周围一圈符合要求的装进队列中
循环退出条件：
1. 找到终点
2. 没找到终点，但是队列长度已经为空
"""
grid = [[0, 0, 1, 0, 0, 0],
[0, 0, 1, 0, 0, 0],
[0, 1, 0, 0, 1, 0],
[0, 1, 0, 1, 1, 0],
[0, 0, 0, 0, 1, 0]]
init = [0, 0]
goal = [len(grid)-1, len(grid[0])-1]
cost = 1
delta = [[-1, 0], # go up
[ 0,-1], # go left
[ 1, 0], # go down
[ 0, 1]] # go right
delta_name = ['^', '<', 'v', '>']
def search(grid,init,goal,cost):
flag = [[0 for j in range(len(grid[i]))]for i in range(len(grid))] # 标识是否访问过
expension = [[-1 for j in range(len(grid[i]))] for i in range(len(grid))]
expension[0][0] = 0
this_map = [[' ' for col in range(len(grid[0]))] for row in range(len(grid))]
# 存储访问过的节点，并从中挑选cost最小的节点
L = []
L_map = []
count = 0
# 标识当前所处位置
now = [0,0,0]
flag[0][0] = 1
while now[:2] != goal:
next_cost = now[2]
now_next = [[0, 0]+now, [0, 0]+now, [0, 0]+now, [0, 0]+now]
for i in range(len(delta)):
for j in range(len(delta[i])):
now_next[i][j] = delta[i][j] + now[j]
for item in now_next:
if 0<=item[0]<len(grid) and 0<=item[1]<len(grid[0]):
if flag[item[0]][item[1]] == 0 and grid[item[0]][item[1]] == 0 :
item[4] += 1
flag[item[0]][item[1]] = 1
if item not in L_map:
L_map.append(item)
if item[:2]+[item[4]] not in L:
L.append(item[:2]+[item[4]])
count = count + 1
expension[item[0]][item[1]] = count
L.sort(key=lambda cost: cost[2])
if len(L) == 0 and now[:2] != goal:
return 'fail'
# 更新now为当前
now = L.pop(0)
L_map.reverse()
location_list = []
location_list.append(L_map[0][:2])
for i in range(len(L_map)):
if L_map[i][:2] == location_list[-1]:
location_list.append(L_map[i][2:4])
location_list.reverse()
this_move = [0, 0]
for i in range(len(location_list) - 1):
this_move[0] = location_list[i+1][0]-location_list[i][0]
this_move[1] = location_list[i+1][1]-location_list[i][1]
a = delta.index(this_move)
this_map[location_list[i][0]][location_list[i][1]] = delta_name[a]
this_map[goal[0]][goal[1]] = '*'
return this_map
"""
# 这部分是老师给的答案，他的思路还是很清晰的
def search(grid, init, goal, cost):
# ----------------------------------------
# modify code below
# ----------------------------------------
closed = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
closed[init[0]][init[1]] = 1
x = init[0]
y = init[1]
g = 0
open = [[g, x, y]]
found = False # flag that is set when search is complete
resign = False # flag set if we can't find expand
while not found and not resign:
if len(open) == 0:
resign = True
return 'fail'
else:
open.sort()
open.reverse()
next = open.pop()
x = next[1]
y = next[2]
g = next[0]
if x == goal[0] and y == goal[1]:
found = True
else:
for i in range(len(delta)):
x2 = x + delta[i][0]
y2 = y + delta[i][1]
if x2 >= 0 and x2 < len(grid) and y2 >= 0 and y2 < len(grid[0]):
if closed[x2][y2] == 0 and grid[x2][y2] == 0:
g2 = g + cost
open.append([g2, x2, y2])
closed[x2][y2] = 1
return open # make sure you return the shortest path
"""
a= search(grid,init,goal,cost)
for i in range(len(a)):
print(a[i])
