astar fix

.idea/misc.xml

@@ -1,4 +1,7 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.9 (traktor)" />
+  </component>
   <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9 (traktor)" project-jdk-type="Python SDK" />
 </project>

astar.py

@@ -0,0 +1,150 @@
+import pygame
+from board import Board
+from constant import width, height, rows, cols
+from tractor import Tractor
+import heapq
+
+fps = 2
+WIN = pygame.display.set_mode((width, height))
+pygame.display.set_caption('Inteligenty Traktor')
+
+
+class Node:
+    def __init__(self, state, parent=None, action=None, cost=0):
+        self.state = state  # Stan reprezentowany przez węzeł
+        self.parent = parent  # Węzeł rodzica
+        self.action = action  # Akcja prowadząca do tego stanu
+        self.cost = cost  # Koszt przejścia do tego stanu
+        self.f = 0  # Wartość funkcji priorytetowej
+        self.tie_breaker = 0  # Wartość używana do rozwiązywania konfliktów priorytetów
+
+    def __lt__(self, other):
+        # Porównanie węzłów w celu ustalenia kolejności w kolejce priorytetowej
+        if self.f == other.f:
+            return self.tie_breaker > other.tie_breaker  # Większy tie_breaker ma wyższy priorytet
+        return self.f < other.f
+        # Jesli pare wezlow ma taie same f, to tilebreaker ustawia
+        # prorytety akcje right i down maja wyzszy priorytet
+
+
+def manhattan(pos0, pos1):
+    # Heurystyka odległości Manhattan
+    d1 = abs(pos1[0] - pos0[0])
+    d2 = abs(pos1[1] - pos0[1])
+    return d1 + d2
+
+
+def nastepnik(state, board):
+    # Funkcja generująca możliwe następne stany (akcje)
+    x, y = state
+    successors = []
+    actions = [('right', (x+1, y), 1), ('down', (x, y+1), 1), ('up', (x, y-1), 0), ('left', (x-1, y), 0)]
+    for action, next_state, tie_breaker in actions:
+        if 0 <= next_state[0] < cols and 0 <= next_state[1] < rows:
+            cost = board.get_cost(next_state[0], next_state[1])
+            successors.append((action, next_state, cost, tie_breaker))
+    return successors
+
+
+def goal_test(state, goal):
+    #  Czy dany stan jest stanem docelowym
+    return state == goal
+
+
+def graphsearch(istate, goal, board, heuristic=manhattan):
+    # Algorytm przeszukiwania grafu
+    fringe = []  # Kolejka priorytetowa przechowująca węzły do odwiedzenia
+    explored = set()  # Zbiór odwiedzonych stanów
+    start_node = Node(istate)
+    start_node.f = heuristic(istate, goal)  # Obliczenie wartości heurystycznej dla stanu początkowego
+    start_node.tie_breaker = 0  # Ustawienie tie_breaker dla węzła startowego,
+    heapq.heappush(fringe, start_node)
+
+    while fringe:
+        elem = heapq.heappop(fringe)
+        if goal_test(elem.state, goal):
+            path = []
+            total_cost = elem.cost  # Zapisanie całkowitego kosztu
+            while elem:
+                path.append((elem.state, elem.action))
+                elem = elem.parent
+            return path[::-1], total_cost  # Zwrócenie ścieżki i kosztu
+
+        explored.add(elem.state)
+        for action_index, (action, state, cost, tie_breaker) in enumerate(nastepnik(elem.state, board)):
+            x = Node(state, parent=elem, action=action, cost=elem.cost + cost)
+            x.f = x.cost + heuristic(state, goal)  # Obliczenie wartości funkcji priorytetowej
+            x.tie_breaker = elem.tie_breaker * 4 + action_index  # Obliczanie tie_breaker na podstawie akcji
+
+            if state not in explored and not any(node.state == state for node in fringe):
+                heapq.heappush(fringe, x)
+            else:
+                for i, node in enumerate(fringe):
+                    if node.state == state and (node.f > x.f or (node.f == x.f and node.tie_breaker < x.tie_breaker)):
+                        fringe[i] = x
+                        heapq.heapify(fringe)
+                        break
+
+    print("Nie znaleziono ścieżki.")
+    return None, 0  # Zwrócenie ścieżki jako None i kosztu jako 0 w przypadku braku ścieżki
+
+def main():
+    run = True
+    clock = pygame.time.Clock()
+    board = Board()
+    board.load_images()
+
+    start_state = (9, 9)  # Stan początkowy
+    goal_state = (0, 0)  # Stan docelowy
+    tractor = Tractor(start_state[1], start_state[0])
+    board.set_grass(start_state[0], start_state[1])  # Ustawienie startowego pola jako trawę
+    board.set_grass(goal_state[0], goal_state[1])  # Ustawienie docelowego pola jako trawę
+
+    path, total_cost = graphsearch(start_state, goal_state, board)
+
+    while run:
+        clock.tick(fps)
+
+        for event in pygame.event.get():
+            if event.type == pygame.QUIT:
+                run = False
+
+        if not path:
+            run = False
+            continue
+
+        next_state, action = path.pop(0) if path else (start_state, None)
+        print(next_state)  # Wypisanie następnego stanu
+        tractor.row, tractor.col = next_state[1], next_state[0]
+
+        if action == "right":
+            tractor.direction = "right"
+        elif action == "left":
+            tractor.direction = "left"
+        elif action == "down":
+            tractor.direction = "down"
+        elif action == "up":
+            tractor.direction = "up"
+
+        # Aktualizacja planszy na podstawie położenia traktora
+        if board.is_weed(tractor.col, tractor.row):
+            board.set_grass(tractor.col, tractor.row)
+        elif board.is_dirt(tractor.col, tractor.row):
+            board.set_soil(tractor.col, tractor.row)
+        elif board.is_soil(tractor.col, tractor.row):
+            board.set_carrot(tractor.col, tractor.row)
+
+        board.draw_cubes(WIN)
+        tractor.draw(WIN)
+        pygame.display.update()
+
+    print(f"Całkowity koszt trasy: {total_cost}")
+
+    while True:
+        for event in pygame.event.get():
+            if event.type == pygame.QUIT:
+                pygame.quit()
+                return
+
+
+main()

board.py

@@ -1,13 +1,14 @@
 import pygame
 from constant import size, rows, cols
 import random
-
+from tractor import Tractor
 
 class Board:
     def __init__(self):
         self.board = []
         self.load_images()
         self.generate_board()
+        self.load_costs()
 
 
     def load_images(self):
@@ -16,6 +17,7 @@ class Board:
         self.rock= pygame.image.load("board/rock.png")
         self.weeds =  pygame.image.load("board/weeds.png")
         self.soil = pygame.image.load("board/zyzna.png")
+        self.carrot = pygame.image.load("board/carrot.png")
 
     def generate_board(self):
         self.board = [[random.choice([0,1,2,3,4,5,6,7,8,9]) for _ in range(rows)] for _ in range(cols)]
@@ -41,11 +43,38 @@ class Board:
                     win.blit(self.grass,  cube_rect)
                 elif cube == 10:
                     win.blit(self.soil, cube_rect)
+                elif cube == 11:
+                    carrot_scale = pygame.transform.scale(self.carrot, (size,size))
+                    win.blit(self.carrot, cube_rect) 
+                    win.blit(carrot_scale, cube_rect)
+                       
                 else:
                     win.blit(self.dirt,  cube_rect)
+                    
+                    
+    def load_costs(self):
+            self.costs = {
+                0: 100,  #kamien
+                1:2,     #chwast
+                2: 1,   #po trawie
+                3: 1,   #po trawie
+                4: 1,   #po trawie
+                5: 1,   #po trawie
+                6: 3,   #ziemia
+                7: 3,   #ziemia
+                8: 3,   #ziemia
+                9: 3,   #ziemia    
+                10: 4,  #zyzna
+                11: 10  #marchewka
+            }
 
+    def get_cost(self, row, col):
+        tile_type = self.board[row][col]
+        return self.costs.get(tile_type, 1)  
+            
     def is_rock(self, row, col):
-        return self.board[row][col] == 0
+        tractor = Tractor(row, col)
+        return self.board[row][col] == 0 and not (row == tractor.row and col == tractor.col)
 
     def is_weed(self,row,col):
         return self.board[row][col] == 1
@@ -55,6 +84,12 @@ class Board:
 
     def is_dirt(self,row,col):
         return self.board[row][col] in (6,7,8,9)
-
+        
+    def is_soil(self, row, col):
+        return self.board[row][col] == 10
+    
     def set_soil(self, row, col):
-        self.board[row][col] = 10
+        self.board[row][col] = 10
+        
+    def set_carrot(self, row, col):
+        self.board[row][col] = 11   

constant.py

@@ -1,6 +1,6 @@
 import pygame
 width, height = 640, 640
-rows, cols = 8, 8
+rows, cols = 10, 10
 size = width//cols
 yellow = (216,178,0)
 green= (103,178,0)

dane.csv

@@ -0,0 +1,206 @@
+wilgotnosc_gleby,temperatura_gleby,opady_deszczu,wiek_rosliny,proc_ekspo_na_swiatlo,pora_dnia,pora_roku,roslina,podlac
+40, 20, 5, 3, 70, 10, 2, 1, nie
+60, 25, 0, 2, 50, 15, 3, 2, tak
+30, 18, 10, 1, 80, 12, 4, 3, nie
+50, 22, 3, 4, 60, 18, 1, 4, tak
+45, 23, 2, 5, 75, 8, 2, 5, tak
+55, 26, 8, 3, 70, 14, 3, 6, nie
+35, 21, 1, 2, 55, 16, 4, 7, tak
+65, 24, 6, 4, 80, 11, 1, 8, nie
+42, 20, 4, 3, 65, 13, 2, 9, tak
+48, 22, 7, 5, 75, 9, 3, 10, tak
+30, 25, 0, 1, 70, 14, 2, 1, tak
+60, 18, 10, 2, 60, 8, 3, 2, nie
+35, 20, 2, 3, 50, 12, 4, 3, tak
+50, 23, 1, 4, 65, 17, 1, 4, tak
+45, 24, 5, 5, 80, 10, 2, 5, tak
+55, 22, 3, 3, 75, 13, 3, 6, nie
+40, 26, 8, 6, 70, 16, 1, 7, nie
+62, 21, 1, 4, 85, 9, 2, 8, tak
+47, 25, 6, 5, 60, 11, 3, 9, nie
+58, 20, 4, 3, 75, 14, 4, 10, tak
+38, 24, 7, 3, 65, 11, 1, 1, tak
+59, 20, 3, 2, 55, 16, 2, 2, nie
+33, 22, 5, 1, 70, 9, 3, 3, nie
+52, 25, 2, 4, 80, 14, 4, 4, tak
+46, 21, 1, 5, 75, 10, 1, 5, tak
+56, 26, 8, 3, 70, 15, 2, 6, nie
+34, 23, 0, 2, 60, 8, 3, 7, nie
+63, 18, 4, 4, 85, 12, 4, 8, nie
+41, 20, 1, 3, 65, 13, 1, 9, tak
+49, 22, 9, 5, 75, 17, 2, 10, tak
+29, 25, 0, 2, 70, 14, 3, 1, tak
+61, 18, 11, 3, 60, 8, 1, 2, nie
+37, 20, 2, 4, 50, 12, 2, 3, tak
+51, 23, 0, 5, 65, 16, 4, 4, tak
+44, 24, 4, 6, 80, 10, 3, 5, tak
+54, 22, 3, 3, 75, 13, 1, 6, nie
+39, 26, 7, 1, 70, 15, 2, 7, nie
+64, 21, 0, 4, 85, 9, 3, 8, tak
+48, 25, 5, 5, 60, 11, 4, 9, nie
+57, 20, 2, 3, 75, 14, 1, 10, tak
+32, 24, 8, 3, 65, 10, 2, 1, tak
+62, 19, 2, 2, 55, 15, 3, 2, nie
+36, 21, 1, 1, 70, 12, 4, 3, nie
+53, 22, 4, 4, 80, 16, 1, 4, tak
+42, 23, 2, 5, 75, 8, 2, 5, tak
+58, 26, 6, 3, 70, 14, 3, 6, nie
+35, 21, 0, 2, 60, 17, 4, 7, nie
+65, 18, 3, 4, 85, 11, 1, 8, nie
+45, 20, 1, 3, 65, 13, 2, 9, tak
+50, 22, 7, 5, 75, 9, 3, 10, tak
+31, 25, 0, 2, 70, 14, 1, 1, tak
+60, 18, 9, 3, 60, 8, 2, 2, nie
+34, 20, 3, 4, 50, 12, 3, 3, tak
+52, 23, 1, 5, 65, 16, 4, 4, tak
+47, 24, 6, 6, 80, 10, 1, 5, tak
+55, 22, 2, 3, 75, 13, 2, 6, nie
+38, 26, 8, 1, 70, 15, 3, 7, nie
+64, 21, 0, 4, 85, 9, 4, 8, tak
+41, 20, 2, 3, 65, 13, 1, 9, tak
+49, 22, 8, 5, 75, 17, 2, 10, tak
+30, 24, 1, 2, 70, 14, 3, 1, tak
+61, 18, 10, 3, 60, 8, 1, 2, nie
+37, 20, 4, 4, 50, 12, 2, 3, tak
+51, 23, 2, 5, 65, 16, 4, 4, tak
+44, 24, 5, 6, 80, 10, 3, 5, tak
+53, 22, 3, 3, 75, 13, 1, 6, nie
+39, 26, 7, 1, 70, 15, 2, 7, nie
+65, 21, 0, 4, 85, 9, 3, 8, tak
+48, 25, 6, 5, 60, 11, 4, 9, nie
+57, 20, 3, 3, 75, 14, 1, 10, tak
+32, 24, 9, 2, 65, 10, 2, 1, tak
+62, 19, 1, 2, 55, 15, 3, 2, nie
+36, 21, 2, 1, 70, 12, 4, 3, nie
+55, 23, 3, 4, 80, 16, 1, 4, tak
+42, 23, 1, 5, 75, 8, 2, 5, tak
+58, 26, 5, 3, 70, 14, 3, 6, nie
+35, 21, 0, 2, 60, 17, 4, 7, nie
+46, 20, 2, 3, 65, 13, 2, 9, tak
+50, 22, 6, 5, 75, 9, 3, 10, tak
+31, 25, 1, 2, 70, 14, 1, 1, tak
+60, 18, 8, 3, 60, 8, 2, 2, nie
+33, 20, 4, 4, 50, 12, 3, 3, tak
+52, 23, 1, 5, 65, 16, 4, 4, tak
+47, 24, 7, 6, 80, 10, 1, 5, tak
+38, 25, 2, 3, 65, 10, 1, 1, tak
+59, 21, 1, 2, 55, 15, 2, 2, nie
+33, 23, 3, 1, 70, 9, 3, 3, nie
+47, 22, 7, 4, 75, 11, 2, 1, tak
+58, 20, 4, 3, 60, 16, 1, 2, nie
+32, 24, 6, 2, 70, 8, 3, 3, nie
+53, 23, 3, 5, 80, 15, 4, 4, tak
+45, 21, 8, 6, 75, 12, 1, 5, tak
+55, 26, 11, 4, 70, 17, 2, 6, nie
+36, 24, 5, 3, 60, 9, 3, 7, nie
+67, 19, 10, 4, 85, 13, 4, 8, nie
+43, 22, 1, 3, 65, 14, 1, 9, tak
+51, 24, 9, 5, 75, 10, 2, 10, tak
+31, 27, 2, 2, 70, 15, 3, 1, tak
+62, 18, 12, 3, 60, 8, 1, 2, nie
+38, 21, 6, 4, 50, 11, 2, 3, tak
+49, 24, 4, 5, 65, 16, 4, 4, tak
+42, 25, 7, 3, 80, 9, 3, 5, tak
+57, 23, 4, 3, 75, 12, 1, 6, nie
+35, 28, 9, 1, 70, 16, 2, 7, nie
+76, 20, 11, 4, 85, 10, 3, 8, nie
+46, 22, 0, 3, 65, 13, 1, 9, tak
+10, 25, 10, 5, 95, 9, 2, 10, tak
+38, 19, 3, 6, 80, 11, 2, 1, tak
+57, 24, 2, 7, 90, 15, 1, 2, tak
+81, 18, 9, 8, 70, 12, 4, 3, nie
+49, 22, 2, 9, 85, 18, 3, 4, tak
+44, 23, 1, 3, 60, 8, 2, 5, nie
+24, 26, 5, 2, 75, 14, 3, 6, tak
+76, 13, 0, 4, 25, 19, 4, 7, nie
+67, 15, 5, 2, 80, 11, 1, 8, nie
+43, 20, 1, 1, 65, 13, 2, 9, tak
+50, 22, 8, 1, 20, 19, 3, 10, nie
+62, 25, 0, 2, 75, 14, 2, 1, tak
+58, 18, 11, 3, 30, 8, 3, 2, nie
+37, 20, 2, 2, 50, 12, 4, 3, tak
+21, 23, 0, 1, 65, 20, 1, 4, tak
+46, 24, 2, 2, 75, 12, 2, 5, tak
+76, 22, 6, 2, 70, 13, 3, 6, nie
+39, 26, 9, 2, 15, 16, 1, 7, nie
+53, 21, 2, 3, 80, 12, 2, 8, tak
+48, 25, 7, 4, 10, 11, 3, 9, nie
+88, 19, 3, 6, 30, 11, 2, 1, nie
+57, 24, 4, 7, 10, 15, 1, 2, nie
+11, 18, 9, 8, 90, 12, 4, 3, tak
+49, 22, 2, 9, 85, 18, 3, 4, tak
+44, 23, 1, 1, 60, 8, 2, 5, tak
+54, 26, 7, 2, 75, 14, 3, 6, nie
+36, 21, 0, 2, 55, 16, 4, 7, tak
+27, 24, 2, 3, 80, 11, 1, 8, tak
+13, 20, 6, 4, 95, 10, 2, 9, tak
+50, 22, 8, 5, 20, 17, 3, 10, nie
+32, 25, 1, 6, 95, 14, 2, 1, tak
+58, 18, 11, 3, 80, 8, 3, 2, nie
+37, 20, 2, 1, 50, 12, 4, 3, tak
+51, 23, 5, 2, 25, 22, 1, 4, nie
+46, 24, 0, 2, 75, 10, 2, 5, tak
+56, 22, 1, 3, 70, 13, 3, 6, tak
+39, 26, 9, 4, 35, 20, 1, 7, nie
+63, 21, 2, 3, 90, 9, 2, 8, tak
+48, 25, 7, 1, 70, 11, 3, 9, nie
+42, 24, 1, 5, 75, 14, 2, 1, tak
+55, 20, 1, 8, 65, 13, 1, 2, tak
+58, 22, 6, 4, 10, 12, 3, 3, nie
+47, 21, 2, 2, 70, 9, 4, 4, tak
+50, 25, 1, 1, 85, 15, 1, 5, nie
+60, 23, 7, 7, 70, 11, 2, 6, nie
+83, 19, 4, 3, 60, 13, 3, 7, nie
+68, 26, 8, 5, 90, 16, 4, 8, nie
+41, 18, 9, 1, 75, 14, 2, 9, nie
+48, 20, 0, 8, 70, 8, 1, 10, tak
+89, 23, 2, 6, 55, 12, 3, 1, nie
+59, 21, 2, 7, 80, 10, 4, 2, tak
+97, 24, 3, 5, 65, 9, 1, 3, nie
+49, 22, 2, 9, 75, 15, 2, 4, tak
+92, 26, 1, 1, 85, 11, 3, 5, nie
+21, 18, 4, 8, 70, 13, 4, 6, tak
+34, 25, 9, 4, 60, 16, 1, 7, nie
+70, 19, 8, 5, 90, 14, 2, 8, nie
+45, 20, 2, 2, 80, 12, 3, 9, tak
+53, 24, 7, 7, 75, 10, 1, 10, nie
+43, 21, 3, 6, 70, 11, 2, 1, tak
+56, 22, 8, 8, 80, 13, 3, 2, nie
+39, 19, 5, 7, 65, 9, 4, 3, nie
+48, 23, 1, 9, 75, 14, 1, 4, tak
+51, 25, 6, 1, 85, 12, 2, 5, nie
+62, 24, 4, 2, 90, 15, 3, 6, nie
+35, 20, 2, 2, 60, 10, 4, 7, tak
+69, 26, 7, 3, 85, 16, 1, 8, nie
+40, 18, 9, 1, 70, 12, 2, 9, nie
+49, 22, 0, 5, 75, 11, 3, 10, tak
+54, 21, 3, 7, 80, 14, 4, 1, tak
+63, 23, 8, 8, 90, 12, 1, 2, nie
+36, 20, 2, 9, 65, 10, 2, 3, tak
+47, 24, 1, 1, 75, 13, 3, 4, tak
+50, 26, 5, 2, 85, 15, 4, 5, nie
+34, 25, 2, 2, 90, 9, 1, 6, tak
+47, 19, 6, 3, 60, 11, 2, 7, nie
+70, 25, 9, 2, 85, 17, 3, 8, nie
+41, 21, 7, 1, 70, 13, 4, 9, nie
+52, 20, 1, 1, 75, 10, 1, 10, tak
+38, 22, 3, 6, 70, 12, 2, 1, tak
+87, 23, 9, 7, 80, 14, 3, 2, nie
+61, 20, 5, 2, 65, 10, 4, 3, nie
+49, 21, 0, 9, 75, 11, 1, 4, tak
+44, 25, 6, 1, 80, 15, 2, 5, nie
+54, 19, 2, 1, 90, 13, 3, 6, tak
+36, 24, 8, 1, 60, 9, 4, 7, nie
+67, 20, 3, 2, 85, 16, 1, 8, nie
+43, 22, 7, 4, 75, 12, 2, 9, nie
+50, 23, 1, 5, 80, 10, 3, 10, tak
+55, 21, 4, 7, 70, 13, 4, 1, nie
+63, 25, 9, 8, 90, 14, 1, 2, nie
+57, 19, 5, 9, 65, 11, 2, 3, nie
+49, 24, 0, 1, 75, 16, 3, 4, tak
+51, 20, 6, 1, 85, 12, 4, 5, nie
+64, 22, 4, 2, 90, 9, 1, 6, nie
+39, 23, 8, 3, 60, 14, 2, 7, tak
+70, 21, 2, 1, 85, 13, 3, 8, nie
+41, 25, 7, 5, 70, 15, 4, 9, nie
+52, 19, 1, 2, 75, 10, 1, 10, tak

decisiontree.py

@@ -0,0 +1,44 @@
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier, plot_tree
+import matplotlib.pyplot as plt
+import pandas as pd
+
+
+data = pd.read_csv("dane.csv")
+print(data)
+
+# Wczytanie danych
+X = data.drop(columns=["podlac"])
+X = pd.get_dummies(X)
+y = data["podlac"]
+
+# Podział danych na zbiór treningowy i testowy
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+
+# Inicjalizacja i dopasowanie modelu drzewa decyzyjnego
+model = DecisionTreeClassifier(max_depth=4)
+model.fit(X_train, y_train)
+
+# Wyliczenie poprawności algorytmu
+accuracy = model.score(X_test, y_test)
+print("Accuracy:", accuracy)
+
+
+
+
+
+# Wyświetlenie drzewa decyzyjnego
+plt.figure(figsize=(20, 20))
+plot_tree(model, feature_names=X.columns, class_names=sorted(y.unique()), filled=True)
+plt.show()
+
+#Marchew = 1
+#zmiemniaki = 2
+#pomidor = 3
+#salata = 4
+#cebula = 5
+#Papryka = 6
+#Buraki = 7
+#Bruksela = 8
+#Rzepak = 9
+#Szpinak = 10

main.py

@@ -5,8 +5,6 @@ from tractor import Tractor
 from kolejka import Stan, Kolejka, Odwiedzone
 
 
-
-
 fps = 5
 WIN = pygame.display.set_mode((width, height))
 
@@ -24,8 +22,8 @@ def actions(elem, istate):
     while((elem.row != istate.row) or (elem.col != istate.col) or (elem.direction != istate.direction)):
         akcje.append(elem.a)
         elem = elem.p[0]
-
     return akcje
+
 def graphsearch(istate, goaltest, board):
     explored = Odwiedzone()
     fringe = Kolejka()
@@ -46,14 +44,14 @@ def graphsearch(istate, goaltest, board):
 def main():
     rotation = ["left", "up", "right", "down"]
     istate = Stan(4,4, "down")
-    goaltest = Stan(1,1, "up")
+    goaltest = Stan(2,3, "up")
     run = True
     clock = pygame.time.Clock()
     board = Board()
     board.load_images()
     actions = graphsearch(istate, goaltest, board)
     print("akcje:  >",actions )
-    tractor = Tractor(4, 4)
+    tractor = Tractor(2, 3)
     while run:
         clock.tick(fps)
 
@@ -64,6 +62,7 @@ def main():
         keys = pygame.key.get_pressed()
 
         if keys[pygame.K_UP]:
+           if keys[pygame.K_UP]:
             if(tractor.direction == "up" and tractor.row > 0 ):
                 if board.is_weed(tractor.col, tractor.row - 1):
                     board.set_grass(tractor.col, tractor.row - 1)
@@ -71,8 +70,12 @@ def main():
                 elif board.is_dirt(tractor.col, tractor.row - 1):
                     board.set_soil(tractor.col, tractor.row - 1)
                     tractor.row -= 1
+                elif board.is_soil(tractor.col, tractor.row - 1):
+                    board.set_carrot(tractor.col, tractor.row - 1)
+                    tractor.row -= 1    
                 elif not board.is_rock(tractor.col, tractor.row - 1):
                     tractor.row -= 1
+
             if(tractor.direction == "left" and tractor.col > 0):
                  if board.is_weed(tractor.col - 1, tractor.row):
                     board.set_grass(tractor.col - 1, tractor.row)
@@ -80,6 +83,9 @@ def main():
                  elif board.is_dirt(tractor.col - 1, tractor.row):
                     board.set_soil(tractor.col - 1, tractor.row)
                     tractor.col -= 1
+                 elif board.is_soil(tractor.col - 1, tractor.row):
+                    board.set_carrot(tractor.col - 1, tractor.row)
+                    tractor.col -= 1    
                  elif not board.is_rock(tractor.col - 1, tractor.row):
                     tractor.col -= 1
             if(tractor.direction == "down" and tractor.row < rows - 1):
@@ -89,6 +95,9 @@ def main():
                 elif board.is_dirt(tractor.col, tractor.row + 1):
                     board.set_soil(tractor.col, tractor.row + 1)
                     tractor.row += 1
+                elif board.is_soil(tractor.col, tractor.row + 1):
+                    board.set_carrot(tractor.col, tractor.row + 1)
+                    tractor.row += 1    
                 elif not board.is_rock(tractor.col, tractor.row + 1):
                     tractor.row += 1
             if(tractor.direction == "right" and tractor.col < cols - 1):
@@ -98,6 +107,9 @@ def main():
                 elif board.is_dirt(tractor.col + 1, tractor.row):
                     board.set_soil(tractor.col + 1, tractor.row)
                     tractor.col += 1
+                elif board.is_soil(tractor.col + 1, tractor.row ):
+                    board.set_carrot(tractor.col + 1, tractor.row )
+                    tractor.col += 1     
                 elif not board.is_rock(tractor.col + 1, tractor.row):
                     tractor.col += 1
         if keys[pygame.K_LEFT]: