Implementacja przeszukiwania stanów przez BFS

agent.py

@@ -1,34 +1,62 @@
 import pygame
+from state_space_search import is_border, is_obstacle
 
 class Agent:
-    def __init__(self, x, y, image_path, grid_size):
+    def __init__(self, istate, image_path, grid_size):
-        self.x = x
+        self.istate = istate
-        self.y = y
+        self.x, self.y, self.direction = istate 
         self.grid_size = grid_size
-        self.image = pygame.image.load(image_path)
+        self.image_original = pygame.image.load(image_path)
-        self.image = pygame.transform.scale(self.image, (grid_size, grid_size))
+        self.image_original = pygame.transform.scale(self.image_original, (grid_size, grid_size))
+        self.image = self.image_original
         
 
     def draw(self, screen):
+        # Obróć obrazek zgodnie z kierunkiem
+        if self.direction == 'E':
+            self.image = pygame.transform.rotate(self.image_original, -90)
+        elif self.direction == 'S':
+            self.image = pygame.transform.rotate(self.image_original, 180)
+        elif self.direction == 'W':
+            self.image = pygame.transform.rotate(self.image_original, 90)
+        else:  # direction == 'N'
+            self.image = self.image_original
+        
         screen.blit(self.image, (self.x * self.grid_size, self.y * self.grid_size))
 
-    def move(self, dx, dy):
+    def handle_event(self, event, max_x, max_y, animals, obstacles):
-        self.x += dx
-        self.y += dy
-
-    def handle_event(self, event, grid_height,grid_width, animals, blocked_fields):
         if event.type == pygame.KEYDOWN:
-            if event.key == pygame.K_UP and self.y > 0 and (self.x, self.y-1) not in blocked_fields:
+            if event.key == pygame.K_UP:
-                self.move(0, -1)
+                self.move('Go Forward', max_x, max_y, obstacles)
-            elif event.key == pygame.K_DOWN and self.y < grid_height - 1 and (self.x, self.y+1) not in blocked_fields:
+            elif event.key == pygame.K_LEFT:
-                self.move(0, 1)
+                self.move('Turn Left', max_x, max_y, obstacles)
-            elif event.key == pygame.K_LEFT and self.x > 0 and (self.x-1, self.y) not in blocked_fields:
+            elif event.key == pygame.K_RIGHT:
-                self.move(-1, 0)
+                self.move('Turn Right', max_x, max_y, obstacles)
-            elif event.key == pygame.K_RIGHT and self.x < grid_width - 1 and (self.x+1, self.y) not in blocked_fields:
-                self.move(1, 0)
 
         for animal in animals:
             if self.x == animal.x and self.y == animal.y:
                 if animal.feed() == 'True':
                     animal._feed = 0
                     print(animal.name, "fed with", animal.food)
+
+    def move(self, action, max_x, max_y, obstacles):
+        if action == 'Go Forward':
+            new_x, new_y = self.x, self.y
+            if self.direction == 'N':
+                new_y -= 1
+            elif self.direction == 'E':
+                new_x += 1
+            elif self.direction == 'S':
+                new_y += 1
+            elif self.direction == 'W':
+                new_x -= 1
+            
+            # Sprawdź, czy nowe położenie mieści się w granicach kraty i nie jest przeszkodą
+            if is_border(new_x, new_y, max_x, max_y) and not(is_obstacle(new_x, new_y, obstacles)):
+                self.x, self.y = new_x, new_y
+                
+        elif action == 'Turn Left':
+            self.direction = {'N': 'W', 'W': 'S', 'S': 'E', 'E': 'N'}[self.direction]
+
+        elif action == 'Turn Right':
+            self.direction = {'N': 'E', 'E': 'S', 'S': 'W', 'W': 'N'}[self.direction]

animal.py

@@ -1,6 +1,7 @@
 import pygame
 from abc import ABC, abstractmethod
 
+
 class Animal:
     def __init__(self, x, y,name, image, food_image, food, environment, adult=False,):
         self.x = x - 1

main.py

@@ -8,6 +8,8 @@ from bear import Bear
 from agent import Agent
 from enclosure import Enclosure
 from spawner import Spawner
+from state_space_search import succ
+from state_space_search import graphsearch
 
 BLACK = (0, 0, 0)
 
@@ -44,10 +46,11 @@ an4 = Elephant(4,3)
 
 Animals = [an1, an2, an3, an4, old_an1, old_an2]
 
+# Enclosure (lewy_górny_x, lewy_górny_y, prawy_dolny_x, prawy_dolny_y, brama, klimat, fenceH, fenceV, gate_obrazek)
 en1 = Enclosure(1,5,  9,11, (9,6),"medium", fenceH, fenceV, gate)
-en2 = Enclosure(29,3, 13,1,(16,3), 'medium', fenceH, fenceV, gate)
+en2 = Enclosure(13,1, 29,3, (16,3), 'medium', fenceH, fenceV, gate)
 en3 = Enclosure(11,5, 16,11, (12,5),'cold', fenceH, fenceV, gate)
-en4 = Enclosure(19,11, 30,5, (25,5),'hot', fenceH, fenceV, gate)
+en4 = Enclosure(19,5, 30,11, (25,5),'hot', fenceH, fenceV, gate)
 en5 = Enclosure(4,13, 28,15, (16,13),'cold', fenceH, fenceV, gate)
 
 
@@ -85,18 +88,89 @@ def spawn_all_animals():
         spawner1 = Spawner(Animal, Enclosures)
         spawner1.spawn_animal(fences, animals_position)
 
+def generate_obstacles():
+    obstacles = []
 
+    for en in Enclosures:
+        # Pobierz współrzędne bramy
+        gate_x, gate_y = en.gate
+        gate_x -= 1
+        gate_y -= 1
+        
+        # Dodaj lewy brzeg prostokąta
+        for y in range(en.y1, en.y2 + 1):
+            if (en.x1, y) != (gate_x, gate_y):
+                obstacles.append((en.x1, y))
+
+        # Dodaj prawy brzeg prostokąta
+        for y in range(en.y1, en.y2 + 1):
+            if (en.x2, y) != (gate_x, gate_y):
+                obstacles.append((en.x2, y))
+
+        # Dodaj górny brzeg prostokąta
+        for x in range(en.x1+1, en.x2):
+            if (x, en.y1) != (gate_x, gate_y):
+                obstacles.append((x, en.y1))
+
+        # Dodaj dolny brzeg prostokąta
+        for x in range(en.x1+1, en.x2):
+            if (x, en.y2) != (gate_x, gate_y):
+                obstacles.append((x, en.y2))
+
+    return obstacles
+
+# region Obstacles Tests
+# WHITE = (255,255,255)
+# TRANSPARENT_BLACK = (0, 0, 0, 128)  # Półprzezroczysty czarny
+# def draw_obstacles(obstacles):
+#     for obstacle in obstacles:
+#         x, y = obstacle
+#         pygame.draw.rect(screen, TRANSPARENT_BLACK, (x * GRID_SIZE, y * GRID_SIZE, GRID_SIZE, GRID_SIZE))
+
+# def main():
+#     obstacles = generate_obstacles()  # Załóżmy, że masz funkcję generate_obstacles, która generuje listę przeszkód
+
+#     # Pętla główna
+#     running = True
+#     while running:
+#         # Obsługa zdarzeń
+#         for event in pygame.event.get():
+#             if event.type == pygame.QUIT:
+#                 running = False
+
+#         # Czyszczenie ekranu
+#         screen.fill(WHITE)
+
+#         # Rysowanie przeszkód
+#         draw_obstacles(obstacles)
+
+#         # Odświeżenie ekranu
+#         pygame.display.flip()
+
+#     # Wyjście z Pygame
+#     pygame.quit()
+
+# endregion
 
 def main():
-    agent = Agent(0, 0, 'images/avatar.png', GRID_SIZE)
+    initial_state = (1,1,'W')
+    agent = Agent(initial_state, 'images/agent.png', GRID_SIZE)
+    
+    obstacles = generate_obstacles()
+    
+    actions = []
+    spawn_all_animals()
+    actions = graphsearch(agent.istate, (an1.x, an1.y), GRID_WIDTH, GRID_HEIGHT, obstacles)
+    # actions = graphsearch(agent.istate, (25,1), GRID_WIDTH, GRID_HEIGHT, obstacles)
+    
     clock = pygame.time.Clock()
-    spawned = False
+
     while True:
         for event in pygame.event.get():
             if event.type == pygame.QUIT:
                 pygame.quit()
                 sys.exit()
-            agent.handle_event(event, GRID_HEIGHT, GRID_WIDTH, Animals, fences)
+            agent.handle_event(event, GRID_WIDTH, GRID_HEIGHT, Animals, obstacles)
 
        
 
@@ -104,14 +178,16 @@ def main():
         draw_grid()
         draw_enclosures()
         draw_gates()
-        if not spawned:
-            spawn_all_animals()
-            spawned = True
         draw_Animals()
         opengates()
         agent.draw(screen)
         pygame.display.flip()
         clock.tick(10)
         
+        if actions:
+            action = actions.pop(0)
+            agent.move(action, GRID_WIDTH, GRID_HEIGHT, obstacles)
+            pygame.time.wait(200)
+        
 if __name__ == "__main__":
     main()

state_space_search.py

@@ -0,0 +1,60 @@
+def is_border(x, y, max_x, max_y):
+    return 0 <= x < max_x and 0 <= y < max_y
+
+def is_obstacle(x, y, obstacles):
+    return (x, y) in obstacles
+
+def succ(current_state, max_x, max_y, obstacles):
+    successors = []
+    x, y, direction = current_state
+
+    # Akcja: Do przodu
+    direction_x, direction_y = {'N': (0, -1), 'E': (1, 0), 'S': (0, 1), 'W': (-1, 0)}[direction] # Słownik przesunięć w zależności od kierunku
+    new_x, new_y = x + direction_x, y + direction_y
+
+    if is_border(new_x, new_y, max_x, max_y) and not(is_obstacle(new_x, new_y, obstacles)):
+        successors.append(((new_x, new_y, direction), 'Go Forward'))
+
+    # Akcja: Obrót w lewo
+    left_turns = {'N': 'W', 'W': 'S', 'S': 'E', 'E': 'N'} # Słownik kierunków po obrocie w lewo
+    successors.append(((x, y, left_turns[direction]), 'Turn Left')) 
+
+    # Akcja: Obrót w prawo
+    right_turns = {'N': 'E', 'E': 'S', 'S': 'W', 'W': 'N'} # Słownik kierunków po obrocie w prawo
+    successors.append(((x, y, right_turns[direction]), 'Turn Right'))
+
+    return successors
+
+def graphsearch(istate, goal, max_x, max_y, obstacles):
+    fringe = [{"state": istate, "parent": None, "action": None}]
+    explored = set()
+
+    while fringe:
+        elem = fringe.pop(0)
+        state = elem["state"]
+
+        if goaltest(state, goal):
+            return build_action_sequence(elem)
+
+        explored.add(state)
+
+        successors = succ(state, max_x, max_y, obstacles)
+
+        for new_state, action in successors:
+            if new_state not in fringe and new_state not in explored:
+                fringe.append({"state": new_state, "parent": elem, "action": action})
+
+    return False
+
+def build_action_sequence(node):
+    actions = []
+    while node["parent"]:
+        actions.append(node["action"])
+        node = node["parent"]
+    actions.reverse()
+    return actions
+
+def goaltest(state, goal):
+    x, y, _ = state
+    goal_x, goal_y = goal
+    return (x,y) == (goal_x, goal_y)