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* flake8 --max-line-length=88
* fixup! Format Python code with psf/black push
Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
101 lines
3.0 KiB
Python
101 lines
3.0 KiB
Python
grid = [
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[0, 1, 0, 0, 0, 0],
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[0, 1, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
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[0, 1, 0, 0, 0, 0],
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[0, 1, 0, 0, 1, 0],
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[0, 0, 0, 0, 1, 0],
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]
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"""
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heuristic = [[9, 8, 7, 6, 5, 4],
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[8, 7, 6, 5, 4, 3],
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[7, 6, 5, 4, 3, 2],
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[6, 5, 4, 3, 2, 1],
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[5, 4, 3, 2, 1, 0]]"""
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init = [0, 0]
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goal = [len(grid) - 1, len(grid[0]) - 1] # all coordinates are given in format [y,x]
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cost = 1
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# the cost map which pushes the path closer to the goal
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heuristic = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
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for i in range(len(grid)):
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for j in range(len(grid[0])):
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heuristic[i][j] = abs(i - goal[0]) + abs(j - goal[1])
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if grid[i][j] == 1:
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heuristic[i][j] = 99 # added extra penalty in the heuristic map
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# the actions we can take
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delta = [[-1, 0], [0, -1], [1, 0], [0, 1]] # go up # go left # go down # go right
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# function to search the path
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def search(grid, init, goal, cost, heuristic):
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closed = [
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[0 for col in range(len(grid[0]))] for row in range(len(grid))
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] # the reference grid
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closed[init[0]][init[1]] = 1
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action = [
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[0 for col in range(len(grid[0]))] for row in range(len(grid))
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] # the action grid
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x = init[0]
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y = init[1]
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g = 0
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f = g + heuristic[init[0]][init[0]]
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cell = [[f, g, x, y]]
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found = False # flag that is set when search is complete
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resign = False # flag set if we can't find expand
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while not found and not resign:
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if len(cell) == 0:
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return "FAIL"
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else: # to choose the least costliest action so as to move closer to the goal
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cell.sort()
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cell.reverse()
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next = cell.pop()
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x = next[2]
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y = next[3]
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g = next[1]
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if x == goal[0] and y == goal[1]:
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found = True
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else:
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for i in range(len(delta)): # to try out different valid actions
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x2 = x + delta[i][0]
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y2 = y + delta[i][1]
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if x2 >= 0 and x2 < len(grid) and y2 >= 0 and y2 < len(grid[0]):
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if closed[x2][y2] == 0 and grid[x2][y2] == 0:
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g2 = g + cost
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f2 = g2 + heuristic[x2][y2]
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cell.append([f2, g2, x2, y2])
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closed[x2][y2] = 1
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action[x2][y2] = i
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invpath = []
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x = goal[0]
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y = goal[1]
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invpath.append([x, y]) # we get the reverse path from here
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while x != init[0] or y != init[1]:
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x2 = x - delta[action[x][y]][0]
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y2 = y - delta[action[x][y]][1]
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x = x2
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y = y2
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invpath.append([x, y])
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path = []
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for i in range(len(invpath)):
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path.append(invpath[len(invpath) - 1 - i])
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print("ACTION MAP")
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for i in range(len(action)):
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print(action[i])
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return path
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a = search(grid, init, goal, cost, heuristic)
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for i in range(len(a)):
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print(a[i])
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