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103 lines
3.1 KiB
Python
103 lines
3.1 KiB
Python
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from __future__ import print_function
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grid = [[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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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 ], # go up
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[ 0, -1], # go left
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[ 1, 0 ], # go down
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[ 0, 1 ]] # 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 = [[0 for col in range(len(grid[0]))] for row in range(len(grid))]# the referrence grid
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closed[init[0]][init[1]] = 1
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action = [[0 for col in range(len(grid[0]))] for row in range(len(grid))]#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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resign = True
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return "FAIL"
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else:
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cell.sort()#to choose the least costliest action so as to move closer to the goal
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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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f = next[0]
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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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