Getting New Changes

Graphs/A*.py

@@ -0,0 +1,101 @@
+
+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
+
+#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
+
+
+#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] 
+