如何在不手动编写障碍的情况下将障碍分布到我的网格？

我正在研究 A 星算法，正如我下面的代码所示，网格是手动编写的，我正在考虑制作一个 100* 100 大小的网格。因此，手动编写它们会非常糟糕。我需要将起点放在 (0,0) 位置，将目标点放在 (99,99) 位置。

我正在尝试使用下面的这一行制作网格

grid1 = [[0 for i in range(100)]for j in range(100)]

但是如何在不触及起点和目标点的位置的情况下随机或不随机地为该网格分配障碍物？

这是在我的代码下面：

from __future__ import print_function
import random

grid = [[0, 1, 0, 0, 0, 0],
        [0, 1, 0, 0, 0, 0],#0 are free path whereas 1's are obstacles
        [0, 1, 0, 0, 0, 0],
        [0, 1, 0, 0, 1, 0],
        [0, 0, 0, 0, 1, 0]]

'''
heuristic = [[9, 8, 7, 6, 5, 4],
             [8, 7, 6, 5, 4, 3],
             [7, 6, 5, 4, 3, 2],
             [6, 5, 4, 3, 2, 1],
             [5, 4, 3, 2, 1, 0]]'''

init = [0, 0]
goal = [len(grid)-1, len(grid[0])-1] #all coordinates are given in format [y,x] 
cost = 1

drone_h = 60

#the cost map which pushes the path closer to the goal
heuristic = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
for i in range(len(grid)):    
    for j in range(len(grid[0])):            
        heuristic[i][j] = abs(i - goal[0]) + abs(j - goal[1])

        #if grid[i][j] == 1:
            #heuristic[i][j] = 99 #added extra penalty in the heuristic map
print(heuristic)
elevation = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
for i in range(len(grid)):    
    for j in range(len(grid[0])): 
        if grid[i][j] == 1:
            elevation[i][j] = random.randint(1,100)
        else:
            elevation[i][j] = 0



#the actions we can take
delta = [[-1, 0 ], # go up
         [ 0, -1], # go left
         [ 1, 0 ], # go down
         [ 0, 1 ]] # go right


#function to search the path
def search(grid,init,goal,cost,heuristic):

    closed = [[0 for col in range(len(grid[0]))] for row in range(len(grid))]# the referrence grid
    closed[init[0]][init[1]] = 1
    action = [[0 for col in range(len(grid[0]))] for row in range(len(grid))]#the action grid

    x = init[0]
    y = init[1]
    g = 0

    f = g + heuristic[init[0]][init[0]] + elevation[init[0]][init[0]]
    cell = [[f, 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(cell) == 0:
            resign = True
            return "FAIL"
        else:
            cell.sort()#to choose the least costliest action so as to move closer to the goal
            cell.reverse()
            next = cell.pop()
            x = next[2]
            y = next[3]
            g = next[1]
            f = next[0]


            if x == goal[0] and y == goal[1]:
                found = True
            else:
                for i in range(len(delta)):#to try out different valid actions
                    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 and elevation[x2][y2] < drone_h :
                            g2 = g + cost
                            f2 = g2 + heuristic[x2][y2] + elevation[x2][y2]
                            cell.append([f2, g2, x2, y2])
                            closed[x2][y2] = 1
                            action[x2][y2] = i
    invpath = []
    x = goal[0]
    y = goal[1]
    invpath.append([x, y])#we get the reverse path from here
    while x != init[0] or y != init[1]:
        x2 = x - delta[action[x][y]][0]
        y2 = y - delta[action[x][y]][1]
        x = x2
        y = y2
        invpath.append([x, y])

    path = []
    for i in range(len(invpath)):
        path.append(invpath[len(invpath) - 1 - i])
    print("ACTION MAP")
    for i in range(len(action)):
        print(action[i])

    return path

a = search(grid,init,goal,cost,heuristic)
for i in range(len(a)):
    print(a[i])