optimal goaltest TODO
This commit is contained in:
Tomasz Adamczyk
parent
06e30867c3
commit
56bebed942
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__pycache__/astar.cpython-37.pyc
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__pycache__/astar.cpython-37.pyc
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128
astar.py
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128
astar.py
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from operator import itemgetter
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import copy
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import tractor
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class Istate: #stan początkowy traktora (strona, w którą patrzy, miejsce, w którym się on znajduje)
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def __init__(self, direction, x, y):
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self.direction = direction
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self.x = x
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self.y = y
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def get_direction(self):
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return self.direction
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def set_direction(self, direction):
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self.direction = direction
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def get_x(self):
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return self.x
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def set_x(self, x):
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self.x = x
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def get_y(self):
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return self.y
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def set_y(self, y):
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self.y = y
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class Node: #wierzchołek grafu
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def __init__(self, action, direction, parent, x, y):
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self.action = action #akcja jaką ma wykonać (obróc się w lewo, obróć się w prawo, ruch do przodu)
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self.direction = direction
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self.parent = parent #ojciec wierzchołka
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self.x = x
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self.y = y
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def get_action(self):
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return self.action
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def set_action(self, action):
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self.action = action
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def get_direction(self):
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return self.direction
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def set_direction(self, direction):
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self.direction = direction
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def get_parent(self):
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return self.parent
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def set_parent(self, parent):
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self.parent = parent
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def get_x(self):
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return self.x
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def set_x(self, x):
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self.x = x
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def get_y(self):
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return self.y
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def set_y(self, y):
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self.y = y
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def cost(map, node): #funkcja kosztu : ile kosztuje przejechanie przez dane pole
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return map.get_field_cost(int(node.get_x()), int(node.get_y()))
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def heuristic(node, goaltest): #funkcja heurestyki : oszacowuje koszt osiągnięcia stanu końcowego (droga)
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return abs(node.get_x() - goaltest[0]) + abs(node.get_y() - goaltest[1])
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def f(map, node, goaltest): #funkcja zwracająca sumę funkcji kosztu oraz heurestyki
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return cost(map, node) + heuristic(node, goaltest)
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# def optimal_goal_test():
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def goal_test(elem, goaltest): #funkcja sprawdzająca czy położenie traktora równa się położeniu punktu docelowego, jeśli tak zwraca prawdę, w przeciwnym wypadku fałsz
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if elem.get_x() == goaltest[0] and elem.get_y() == goaltest[1]:
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return True
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else:
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return False
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def print_moves(elem): #zwraca listę ruchów jakie należy wykonać by dotrzeć do punktu docelowego
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moves_list = []
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while (elem[0].get_parent() != None):
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moves_list.append(elem[0].get_action())
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elem = elem[0].get_parent()
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moves_list.reverse()
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return moves_list
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def succ(elem): #funkcja następnika, przypisuje jakie akcje są możliwe do wykonania na danym polu oraz jaki będzie stan (kierunek, położenie) po wykonaniu tej akcji
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actions_list = []
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temp = copy.copy(elem.get_direction())
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if temp == 1:
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temp = 4
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else:
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temp = temp - 1
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actions_list.append(("rotate_left", (temp, elem.get_x(), elem.get_y())))
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temp = copy.copy(elem.get_direction())
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if temp == 4:
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temp = 1
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else:
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temp = temp + 1
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actions_list.append(("rotate_right", (temp, elem.get_x(), elem.get_y())))
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temp_move_south = elem.get_y() + 1
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temp_move_west = elem.get_x() - 1
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temp_move_east = elem.get_x() + 1
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temp_move_north = elem.get_y() - 1
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if tractor.Tractor.is_move_allowed_succ(elem) == "x + 1":
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actions_list.append(("move", (elem.get_direction(), temp_move_east, elem.get_y())))
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elif tractor.Tractor.is_move_allowed_succ(elem) == "y - 1":
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actions_list.append(("move", (elem.get_direction(), elem.get_x(), temp_move_north)))
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elif tractor.Tractor.is_move_allowed_succ(elem) == "y + 1":
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actions_list.append(("move", (elem.get_direction(), elem.get_x(), temp_move_south)))
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elif tractor.Tractor.is_move_allowed_succ(elem) == "x - 1":
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actions_list.append(("move", (elem.get_direction(), temp_move_west, elem.get_y())))
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return actions_list
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def graphsearch(fringe, explored, istate, succ, goaltest, f, map): #przeszukiwanie grafu wszerz
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node = Node(None, istate.get_direction(), None, istate.get_x(), istate.get_y()) #wierzchołek początkowy, stworzony ze stanu początkowego traktora
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fringe.append((node, 0)) #wierzchołki do odwiedzenia z priorytetem
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while True:
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if not fringe:
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return False
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elem = fringe.pop(0) #zdejmujemy wierzchołek z kolejki fringe i rozpatrujemy go
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temp = copy.copy(elem[0])
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if goal_test(elem[0], goaltest) is True: #jeżeli osiągniemy cel w trakcie przeszukiwania grafu wszerz (wjedziemy na pole docelowe) : zwracamy listę ruchów, po których wykonaniu dotrzemy na miejsce
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return print_moves(elem)
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explored.append(elem) #dodajemy wierzchołek do listy wierzchołków odwiedzonych
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for (action, state) in succ(temp): #iterujemy po wszystkich możliwych akcjach i stanach otrzymanych dla danego wierzchołka grafu
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fringe_tuple = []
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fringe_tuple_prio = []
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explored_tuple = []
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for (x, y) in fringe:
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fringe_tuple.append((x.get_direction(), x.get_x(), x.get_y()))
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fringe_tuple_prio.append(((x.get_direction(), x.get_x(), x.get_y()), y))
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for (x, y) in explored:
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explored_tuple.append((x.get_direction(), x.get_x(), x.get_y()))
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x = Node(action, state[0], elem, state[1], state[2]) #stworzenie nowego wierzchołka, którego rodzicem jest elem
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p = f(map, x, goaltest) #liczy priorytet
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if state not in fringe_tuple and state not in explored_tuple: #jeżeli stan nie znajduje się na fringe oraz nie znajduje się w liście wierzchołków odwiedzonych
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fringe.append((x, p)) #dodanie wierzchołka na fringe
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fringe = sorted(fringe, key=itemgetter(1)) #sortowanie fringe'a według priorytetu
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elif state in fringe_tuple:
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i = 0
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for (state_prio, r) in fringe_tuple_prio:
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if str(state_prio) == str(state):
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if r > p:
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fringe.insert(i, (x, p)) #zamiana state, który należy do fringe z priorytetem r na state z priorytetem p (niższym)
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fringe = sorted(fringe, key=itemgetter(1)) #sortowanie fringe'a według priorytetu
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break
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i = i + 1
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26
map.py
26
map.py
@ -23,6 +23,32 @@ class Map:
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temp_field = field.Field(temp_plant, temp_rect, temp_soil)
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temp_map_field.append(temp_field)
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self.fields.append(temp_map_field)
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def get_field_cost(self, x, y): #zwraca koszt danego pola
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field = self.fields[x][y]
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if field.get_plant().get_name() == "station" and field.get_plant().get_state() == -1:
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return 5
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elif field.get_plant().get_name() == "beetroot" and field.get_plant().get_state() > 0 and field.get_plant().get_state() <= 3 * definitions.BEETROOTS_GROW_TIME:
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return 10
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elif field.get_plant().get_name() == "beetroot" and field.get_plant().get_state() == definitions.BEETROOTS_MAXIMUM_STATE:
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return 4
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elif field.get_plant().get_name() == "carrot" and field.get_plant().get_state() > 0 and field.get_plant().get_state() <= 3 * definitions.CARROTS_GROW_TIME:
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return 10
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elif field.get_plant().get_name() == "carrot" and field.get_plant().get_state() == definitions.CARROTS_MAXIMUM_STATE:
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return 4
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elif field.get_plant().get_name() == "potato" and field.get_plant().get_state() > 0 and field.get_plant().get_state() <= 3 * definitions.POTATOES_GROW_TIME:
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return 10
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elif field.get_plant().get_name() == "potato" and field.get_plant().get_state() == definitions.POTATOES_MAXIMUM_STATE:
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return 4
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elif field.get_plant().get_name() == "wheat" and field.get_plant().get_state() > 0 and field.get_plant().get_state() <= 7 * definitions.WHEAT_GROW_TIME:
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return 10
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elif field.get_plant().get_name() == "wheat" and field.get_plant().get_state() == definitions.WHEAT_MAXIMUM_STATE:
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return 4
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elif field.get_soil().get_state() is False:
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return 2
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elif field.get_soil().get_state() is True and field.get_soil().get_water_level() is False:
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return 3
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elif field.get_soil().get_state() is True and field.get_soil().get_water_level() is True:
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return 1
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def fill_map(self): #wypełnia mapę teksturami na podstawie logicznego stanu pól
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for i in range(definitions.WIDTH_AMOUNT):
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for j in range(definitions.HEIGHT_AMOUNT):
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4
py.py
4
py.py
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import astar
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import definitions
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import graph
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import map
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@ -27,7 +28,8 @@ def main():
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map1.draw_window(tractor1, tractor1_rect)
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if not move_list and plant.Plant.if_any_mature_plant(map1) is True: #jeżeli są jakieś ruchy do wykonania w move_list oraz istnieje jakaś dojrzała roślina
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istate = graph.Istate(tractor1.get_direction(), tractor1.get_x() / definitions.BLOCK_SIZE, tractor1.get_y() / definitions.BLOCK_SIZE) #stan początkowy traktora (jego orientacja oraz jego aktualne współrzędne)
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move_list = (graph.graphsearch([], [], istate, graph.succ, plant.Plant.get_closest_mature_plant(map1, tractor1))) #lista z ruchami, które należy po kolei wykonać
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#move_list = (graph.graphsearch([], [], istate, graph.succ, plant.Plant.get_closest_mature_plant(map1, tractor1))) #lista z ruchami, które należy po kolei wykonać, graph
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move_list = (astar.graphsearch([], [], istate, graph.succ, plant.Plant.get_closest_mature_plant(map1, tractor1), astar.f, map1)) #lista z ruchami, które należy po kolei wykonać, astar
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elif move_list: #jeżeli move_list nie jest pusta
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tractor1.handle_movement(move_list.pop(0), tractor1_rect) #wykonaj kolejny ruch oraz zdejmij ten ruch z początku listy
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else:
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