test A* just in case

AI_brain/rotate_and_go_a_star.py

@@ -0,0 +1,124 @@
+import math
+import queue
+from dataclasses import dataclass, field
+from typing import Any
+
+from domain.world import World
+
+
+class State:
+    def __init__(self, x, y, direction=(1, 0)):
+        self.x = x
+        self.y = y
+        self.direction = direction
+
+    def __hash__(self):
+        return hash((self.x, self.y))
+
+    def __eq__(self, other):
+        return (self.x == other.x and self.y == other.y
+                and self.direction == other.direction)
+
+
+class Node:
+    def __init__(self, state: State):
+        self.state = state
+        self.parent = None
+        self.action = None
+        self.g = None
+        self.h = None
+
+
+@dataclass(order=True)
+class PrioritizedItem:
+    priority: int
+    item: Any = field(compare=False)
+
+    def __iter__(self):
+        return iter((self.priority, self.item))
+
+
+def action_sequence(node: Node):
+    actions = []
+    while node.parent:
+        actions.append(node.action)
+        node = node.parent
+
+        print(node.g)
+    actions.reverse()
+    return actions
+
+
+class RotateAndGoAStar:
+    def __init__(self, world: World, start_state: State, goal_state: State):
+        self.world = world
+        self.start_state = start_state
+        self.goal_state = goal_state
+        self.fringe = queue.PriorityQueue()
+        self.enqueued_states = {}
+        self.explored = set()
+        self.actions = []
+
+    def search(self):
+        h = abs(self.start_state.x - self.goal_state.x) ** 2 + abs(self.start_state.y - self.goal_state.y) ** 2
+        self.fringe.put(PrioritizedItem(h, Node(self.start_state)))
+
+        while not self.fringe.empty():
+            priority, elem = self.fringe.get()
+
+            self.enqueued_states.pop(elem, 0)
+
+            if self.is_goal(elem.state):
+                self.actions = action_sequence(elem)
+                return True
+
+            self.explored.add(elem.state)
+
+            for (action, state) in self.successors(elem.state):
+                next_node = Node(state)
+                next_node.action = action
+                next_node.parent = elem
+                next_node.g = abs(elem.state.x - state.x) + abs(elem.state.y - state.y) + self.world.get_cost(state.x, state.y)
+                if next_node.g > 100:
+                    print(str(state.x) + ":" + str(state.y))
+                next_node.h = abs(state.x - self.goal_state.x) ** 2 + abs(state.y - self.goal_state.y) ** 2
+                f = next_node.g + next_node.h
+
+                if state not in self.enqueued_states and state not in self.explored:
+                    self.fringe.put(PrioritizedItem(f, next_node))
+                    self.enqueued_states[state] = f
+                elif self.enqueued_states.get(state, -math.inf) > f:
+                    self.add_existed(next_node, f)
+                    self.enqueued_states.pop(state, 0)
+                    self.enqueued_states[state] = f
+
+        return False
+
+    def add_existed(self, node: Node, f: int):
+        old = []
+        while not self.fringe.empty():
+            e = self.fringe.get()
+            if e.item.state == node.state:
+                break
+            old.append(e)
+        self.fringe.put(PrioritizedItem(f, node))
+        for e in old:
+            self.fringe.put(e)
+
+    def successors(self, state: State):
+        new_successors = [
+            # rotate right
+            ("RR", State(state.x, state.y, (-state.direction[1], state.direction[0]))),
+            # rotate left
+            ("RL", State(state.x, state.y, (state.direction[1], -state.direction[0]))),
+        ]
+        if self.world.accepted_move(state.x + state.direction[0], state.y + state.direction[1]):
+            new_successors.append(
+                ("GO", State(state.x + state.direction[0], state.y + state.direction[1], state.direction)))
+        return new_successors
+
+    def is_goal(self, state: State) -> bool:
+        return (
+                state.x == self.goal_state.x
+                and state.y == self.goal_state.y
+        )

domain/world.py

@@ -3,6 +3,7 @@ from domain.entities.entity import Entity
 
 class World:
     def __init__(self, width: int, height: int) -> object:
+        self.costs = [[0 for j in range(height)] for i in range(width)]
         self.width = width
         self.height = height
         self.dust = [[[] for j in range(height)] for i in range(width)]
@@ -47,3 +48,6 @@ class World:
             return False
 
         return True
+
+    def get_cost(self, x, y):
+        return self.costs[x][y]

main.py

@@ -12,7 +12,8 @@ from domain.entities.docking_station import Doc_Station
 from domain.world import World
 from view.renderer import Renderer
 # from AI_brain.movement import GoAnyDirectionBFS, State
-from AI_brain.rotate_and_go_bfs import RotateAndGoBFS, State
+# from AI_brain.rotate_and_go_bfs import RotateAndGoBFS, State
+from AI_brain.rotate_and_go_a_star import RotateAndGoAStar, State
 
 
 config = configparser.ConfigParser()
@@ -50,7 +51,8 @@ class Main:
         end_state = State(self.world.doc_station.x, self.world.doc_station.y)
 
         # path_searcher = GoAnyDirectionBFS(self.world, start_state, end_state)
-        path_searcher = RotateAndGoBFS(self.world, start_state, end_state)
+        # path_searcher = RotateAndGoBFS(self.world, start_state, end_state)
+        path_searcher = RotateAndGoAStar(self.world, start_state, end_state)
         if not path_searcher.search():
             print("No solution")
             exit(0)
@@ -146,6 +148,16 @@ def generate_world(tiles_x: int, tiles_y: int) -> World:
     world.add_entity(Entity(3, 4, "PLANT2"))
     world.add_entity(Entity(8, 8, "PLANT2"))
     world.add_entity(Entity(9, 3, "PLANT3"))
+
+    # TEST
+    world.costs[9][3] = 1000
+    world.costs[8][3] = 1000
+    world.costs[7][3] = 1000
+    world.costs[6][3] = 1000
+    world.costs[5][3] = 1000
+    world.costs[4][3] = 1000
+    world.costs[3][3] = 1000
+
     return world