python - 如何在障碍物或机器人周围添加安全区？

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我正在用Python编写一个A_-star算法，当算法避开障碍物时，我需要在障碍物或机器人本身周围创建一个安全区，直到机器人靠近障碍物时，才有机会撞到障碍物。那么，我怎样才能添加这个安全区域呢？有什么帮助吗？
我的代码如下所示：

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]]


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


#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])




#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]]
    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:
                            g2 = g + cost
                            f2 = g2 + heuristic[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])

最佳答案

实现这一目标的典型方法是在搜索之前将障碍物充气。
假设你的机器人是半径为25厘米的圆形。如果机器人中心距离障碍物不到25厘米，机器人的边缘就会碰到障碍物，对吧？因此，您将障碍物放大（充气）25厘米（这意味着距离原始障碍物25厘米以内的任何点都将成为障碍物），以便您可以规划机器人中心的运动。
如果你想要额外的安全距离，例如10厘米（即机器人距离障碍物10厘米以外的边缘），你可以将障碍物充气35厘米而不是25厘米。
对于非圆机器人，障碍物至少要膨胀一半的轴线，以确保与障碍物不发生碰撞。例如，如果机器人的外形是50x80，障碍物应该充气80/2=40，以确保轨迹安全。
另外，请注意，障碍物充气法最适合圆形/方形机器人。对于具有一个长轴的矩形机器人/机器人，当机器人周围的栅格地图具有狭窄的通道时，即使机器人能够真实地通过它，障碍物膨胀也会使其不可行，这可能是有问题的。
这种障碍物膨胀可以通过编程的方式在地图上使用形态学操作来实现。参见scikit-image.morphology中的膨胀。