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Merge pull request 'complete decision tree' (#23) from d_tree into main

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Reviewed-on: #23
Reviewed-by: Tim Barvenov <timbar@st.amu.edu.pl>
This commit is contained in:
Tim Barvenov 2023-05-19 17:51:03 +02:00
commit 3fe4b6f2e1
31 changed files with 1286 additions and 1105 deletions
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12
.gitignore vendored
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/venv /venv
.DS_Store .DS_Store
/.vscode /.vscode
__pycache__ __pycache__
#PyCharm #PyCharm
.idea/ .idea/

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.idea/.gitignore vendored
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# Default ignored files # Default ignored files
/shelf/ /shelf/
/workspace.xml /workspace.xml

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.idea/Machine_learning_2023.iml
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<?xml version="1.0" encoding="UTF-8"?> <?xml version="1.0" encoding="UTF-8"?>
<module type="PYTHON_MODULE" version="4"> <module type="PYTHON_MODULE" version="4">
<component name="NewModuleRootManager"> <component name="NewModuleRootManager">
<content url="file://$MODULE_DIR$"> <content url="file://$MODULE_DIR$">
<excludeFolder url="file://$MODULE_DIR$/PythEnv" /> <excludeFolder url="file://$MODULE_DIR$/PythEnv" />
</content> </content>
<orderEntry type="inheritedJdk" /> <orderEntry type="inheritedJdk" />
<orderEntry type="sourceFolder" forTests="false" /> <orderEntry type="sourceFolder" forTests="false" />
</component> </component>
<component name="PyDocumentationSettings"> <component name="PyDocumentationSettings">
<option name="format" value="PLAIN" /> <option name="format" value="PLAIN" />
<option name="myDocStringFormat" value="Plain" /> <option name="myDocStringFormat" value="Plain" />
</component> </component>
</module> </module>

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.idea/inspectionProfiles/profiles_settings.xml
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<component name="InspectionProjectProfileManager"> <component name="InspectionProjectProfileManager">
<settings> <settings>
<option name="USE_PROJECT_PROFILE" value="false" /> <option name="USE_PROJECT_PROFILE" value="false" />
<version value="1.0" /> <version value="1.0" />
</settings> </settings>
</component> </component>

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.idea/misc.xml
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<?xml version="1.0" encoding="UTF-8"?> <?xml version="1.0" encoding="UTF-8"?>
<project version="4"> <project version="4">
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9 (Machine_learning_2023)" project-jdk-type="Python SDK" /> <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9 (Machine_learning_2023)" project-jdk-type="Python SDK" />
</project> </project>

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.idea/modules.xml
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<?xml version="1.0" encoding="UTF-8"?> <?xml version="1.0" encoding="UTF-8"?>
<project version="4"> <project version="4">
<component name="ProjectModuleManager"> <component name="ProjectModuleManager">
<modules> <modules>
<module fileurl="file://$PROJECT_DIR$/.idea/Machine_learning_2023.iml" filepath="$PROJECT_DIR$/.idea/Machine_learning_2023.iml" /> <module fileurl="file://$PROJECT_DIR$/.idea/Machine_learning_2023.iml" filepath="$PROJECT_DIR$/.idea/Machine_learning_2023.iml" />
</modules> </modules>
</component> </component>
</project> </project>

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.idea/vcs.xml
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<?xml version="1.0" encoding="UTF-8"?> <?xml version="1.0" encoding="UTF-8"?>
<project version="4"> <project version="4">
<component name="VcsDirectoryMappings"> <component name="VcsDirectoryMappings">
<mapping directory="$PROJECT_DIR$" vcs="Git" /> <mapping directory="$PROJECT_DIR$" vcs="Git" />
</component> </component>
</project> </project>

160
AI_brain/go_any_direction.py
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from domain.commands.vacuum_move_command import VacuumMoveCommand from domain.commands.vacuum_move_command import VacuumMoveCommand
from domain.world import World from domain.world import World
class State: class State:
def __init__(self, x, y): def __init__(self, x, y):
self.x = x self.x = x
self.y = y self.y = y
def __hash__(self): def __hash__(self):
return hash((self.x, self.y)) return hash((self.x, self.y))
def __eq__(self, other): def __eq__(self, other):
return self.x == other.x and self.y == other.y return self.x == other.x and self.y == other.y
class GoAnyDirectionBFS: class GoAnyDirectionBFS:
def __init__(self, world: World, start_state: State, goal_state: State): def __init__(self, world: World, start_state: State, goal_state: State):
self.start_state = start_state self.start_state = start_state
self.goal_state = goal_state self.goal_state = goal_state
self.visited = set() self.visited = set()
self.parent = {} self.parent = {}
self.actions = [] self.actions = []
self.path = [] self.path = []
self.world = world self.world = world
self.queue = [] self.queue = []
def search(self): def search(self):
self.queue.append(self.start_state) self.queue.append(self.start_state)
self.visited.add(self.start_state) self.visited.add(self.start_state)
while self.queue: while self.queue:
state = self.queue.pop(0) state = self.queue.pop(0)
if state == self.goal_state: if state == self.goal_state:
self.actions = self.get_actions() self.actions = self.get_actions()
self.path = self.get_path() self.path = self.get_path()
return True return True
for successor in self.successors(state): for successor in self.successors(state):
if successor not in self.visited: if successor not in self.visited:
self.visited.add(successor) self.visited.add(successor)
self.parent[successor] = state self.parent[successor] = state
self.queue.append(successor) self.queue.append(successor)
return False return False
def successors(self, state): def successors(self, state):
new_successors = [ new_successors = [
State(state.x + dx, state.y + dy) State(state.x + dx, state.y + dy)
for dx, dy in [(1, 0), (0, 1), (-1, 0), (0, -1)] for dx, dy in [(1, 0), (0, 1), (-1, 0), (0, -1)]
if self.world.accepted_move(state.x + dx, state.y + dy) if self.world.accepted_move(state.x + dx, state.y + dy)
] ]
return new_successors return new_successors
def get_actions(self): def get_actions(self):
actions = [] actions = []
state = self.goal_state state = self.goal_state
while state != self.start_state: while state != self.start_state:
parent_state = self.parent[state] parent_state = self.parent[state]
dx = state.x - parent_state.x dx = state.x - parent_state.x
dy = state.y - parent_state.y dy = state.y - parent_state.y
if dx == 1: if dx == 1:
actions.append("RIGHT") actions.append("RIGHT")
elif dx == -1: elif dx == -1:
actions.append("LEFT") actions.append("LEFT")
elif dy == 1: elif dy == 1:
actions.append("DOWN") actions.append("DOWN")
elif dy == -1: elif dy == -1:
actions.append("UP") actions.append("UP")
state = parent_state state = parent_state
actions.reverse() actions.reverse()
return actions return actions
def get_path(self): def get_path(self):
path = [] path = []
state = self.goal_state state = self.goal_state
while state != self.start_state: while state != self.start_state:
path.append((state.x, state.y)) path.append((state.x, state.y))
state = self.parent[state] state = self.parent[state]
path.append((self.start_state.x, self.start_state.y)) path.append((self.start_state.x, self.start_state.y))
path.reverse() path.reverse()
return path return path

224
AI_brain/rotate_and_go_astar.py
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import heapq import heapq
from domain.world import World from domain.world import World
class State: class State:
def __init__(self, x, y, direction=(1, 0), entity=None): def __init__(self, x, y, direction=(1, 0), entity=None):
self.x = x self.x = x
self.y = y self.y = y
self.direction = direction self.direction = direction
def __hash__(self): def __hash__(self):
return hash((self.x, self.y)) return hash((self.x, self.y))
def __eq__(self, other): def __eq__(self, other):
return ( return (
self.x == other.x self.x == other.x
and self.y == other.y and self.y == other.y
and self.direction == other.direction and self.direction == other.direction
) )
def heuristic(self, goal_state): def heuristic(self, goal_state):
return abs(self.x - goal_state.x) + abs(self.y - goal_state.y) return abs(self.x - goal_state.x) + abs(self.y - goal_state.y)
class Node: class Node:
def __init__(self, state: State, g_score: int, goal_state: State): def __init__(self, state: State, g_score: int, goal_state: State):
self.state = state self.state = state
self.g_score = g_score self.g_score = g_score
self.f_score = g_score + state.heuristic(goal_state) self.f_score = g_score + state.heuristic(goal_state)
self.parent = None self.parent = None
self.action = None self.action = None
def __lt__(self, other): def __lt__(self, other):
return self.f_score < other.f_score return self.f_score < other.f_score
def action_sequence(node: Node): def action_sequence(node: Node):
actions = [] actions = []
while node.parent: while node.parent:
actions.append(node.action) actions.append(node.action)
node = node.parent node = node.parent
actions.reverse() actions.reverse()
return actions return actions
class RotateAndGoAStar: class RotateAndGoAStar:
def __init__(self, world: World, start_state: State, goal_state: State): def __init__(self, world: World, start_state: State, goal_state: State):
self.world = world self.world = world
self.start_state = start_state self.start_state = start_state
self.goal_state = goal_state self.goal_state = goal_state
self.fringe = [] self.fringe = []
self.enqueued_states = set() self.enqueued_states = set()
self.explored = set() self.explored = set()
self.actions = [] self.actions = []
def get_g_score(self, state): def get_g_score(self, state):
return self.world.get_cost(state.x, state.y) return self.world.get_cost(state.x, state.y)
def search(self): def search(self):
heapq.heappush( heapq.heappush(
self.fringe, Node(self.start_state, 0, self.goal_state) self.fringe, Node(self.start_state, 0, self.goal_state)
) )
while self.fringe: while self.fringe:
elem = heapq.heappop(self.fringe) elem = heapq.heappop(self.fringe)
if self.is_goal(elem.state): if self.is_goal(elem.state):
self.actions = action_sequence(elem) self.actions = action_sequence(elem)
return True return True
self.explored.add(elem.state) self.explored.add(elem.state)
for action, state in self.successors(elem.state): for action, state in self.successors(elem.state):
if state in self.explored: if state in self.explored:
continue continue
new_g_score = new_g_score = elem.g_score + self.world.get_cost(state.x, state.y) new_g_score = new_g_score = elem.g_score + self.world.get_cost(state.x, state.y)
if state not in self.enqueued_states: if state not in self.enqueued_states:
next_node = Node(state, new_g_score, self.goal_state) next_node = Node(state, new_g_score, self.goal_state)
next_node.action = action next_node.action = action
next_node.parent = elem next_node.parent = elem
heapq.heappush(self.fringe, next_node) heapq.heappush(self.fringe, next_node)
self.enqueued_states.add(state) self.enqueued_states.add(state)
elif new_g_score < self.get_g_score(state): elif new_g_score < self.get_g_score(state):
for node in self.fringe: for node in self.fringe:
if node.state == state: if node.state == state:
node.g_score = new_g_score node.g_score = new_g_score
node.f_score = ( node.f_score = (
new_g_score + node.state.heuristic(self.goal_state) new_g_score + node.state.heuristic(self.goal_state)
) )
node.parent = elem node.parent = elem
node.action = action node.action = action
heapq.heapify(self.fringe) heapq.heapify(self.fringe)
break break
return False return False
def successors(self, state: State): def successors(self, state: State):
new_successors = [ new_successors = [
("RR", State(state.x, state.y, (-state.direction[1], state.direction[0]))), ("RR", State(state.x, state.y, (-state.direction[1], state.direction[0]))),
("RL", State(state.x, state.y, (state.direction[1], -state.direction[0]))), ("RL", State(state.x, state.y, (state.direction[1], -state.direction[0]))),
] ]
next_x = state.x + state.direction[0] next_x = state.x + state.direction[0]
next_y = state.y + state.direction[1] next_y = state.y + state.direction[1]
if self.world.accepted_move(next_x, next_y): if self.world.accepted_move(next_x, next_y):
new_successors.append( new_successors.append(
("GO", State(next_x, next_y, state.direction)) ("GO", State(next_x, next_y, state.direction))
) )
return new_successors return new_successors
def is_goal(self, state: State) -> bool: def is_goal(self, state: State) -> bool:
return ( return (
state.x == self.goal_state.x state.x == self.goal_state.x
and state.y == self.goal_state.y ) and state.y == self.goal_state.y )

166
AI_brain/rotate_and_go_bfs.py
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import queue import queue
from domain.world import World from domain.world import World
class State: class State:
def __init__(self, x, y, direction=(1, 0)): def __init__(self, x, y, direction=(1, 0)):
self.x = x self.x = x
self.y = y self.y = y
self.direction = direction self.direction = direction
def __hash__(self): def __hash__(self):
return hash((self.x, self.y)) return hash((self.x, self.y))
def __eq__(self, other): def __eq__(self, other):
return (self.x == other.x and self.y == other.y return (self.x == other.x and self.y == other.y
and self.direction == other.direction) and self.direction == other.direction)
class Node: class Node:
def __init__(self, state: State): def __init__(self, state: State):
self.state = state self.state = state
self.parent = None self.parent = None
self.action = None self.action = None
def action_sequence(node: Node): def action_sequence(node: Node):
actions = [] actions = []
while node.parent: while node.parent:
actions.append(node.action) actions.append(node.action)
node = node.parent node = node.parent
actions.reverse() actions.reverse()
return actions return actions
class RotateAndGoBFS: class RotateAndGoBFS:
def __init__(self, world: World, start_state: State, goal_state: State): def __init__(self, world: World, start_state: State, goal_state: State):
self.world = world self.world = world
self.start_state = start_state self.start_state = start_state
self.goal_state = goal_state self.goal_state = goal_state
self.fringe = queue.Queue() self.fringe = queue.Queue()
self.enqueued_states = set() self.enqueued_states = set()
self.explored = set() self.explored = set()
self.actions = [] self.actions = []
def search(self): def search(self):
self.fringe.put(Node(self.start_state)) self.fringe.put(Node(self.start_state))
while self.fringe: while self.fringe:
elem = self.fringe.get() elem = self.fringe.get()
if self.is_goal(elem.state): if self.is_goal(elem.state):
self.actions = action_sequence(elem) self.actions = action_sequence(elem)
return True return True
self.explored.add(elem.state) self.explored.add(elem.state)
for (action, state) in self.successors(elem.state): for (action, state) in self.successors(elem.state):
if state in self.explored or state in self.enqueued_states: if state in self.explored or state in self.enqueued_states:
continue continue
next_node = Node(state) next_node = Node(state)
next_node.action = action next_node.action = action
next_node.parent = elem next_node.parent = elem
self.fringe.put(next_node) self.fringe.put(next_node)
self.enqueued_states.add(state) self.enqueued_states.add(state)
return False return False
def successors(self, state: State): def successors(self, state: State):
new_successors = [ new_successors = [
# rotate right # rotate right
("RR", State(state.x, state.y, (-state.direction[1], state.direction[0]))), ("RR", State(state.x, state.y, (-state.direction[1], state.direction[0]))),
# rotate left # rotate left
("RL", State(state.x, state.y, (state.direction[1], -state.direction[0]))), ("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]): if self.world.accepted_move(state.x + state.direction[0], state.y + state.direction[1]):
new_successors.append( new_successors.append(
("GO", State(state.x + state.direction[0], state.y + state.direction[1], state.direction))) ("GO", State(state.x + state.direction[0], state.y + state.direction[1], state.direction)))
return new_successors return new_successors
def is_goal(self, state: State) -> bool: def is_goal(self, state: State) -> bool:
return ( return (
state.x == self.goal_state.x state.x == self.goal_state.x
and state.y == self.goal_state.y and state.y == self.goal_state.y
) )

120
Project discription.txt
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****** ******
Dokumentacja projektu "Automatyczny robot sprzątający" Dokumentacja projektu "Automatyczny robot sprzątający"
Wprowadzenie: Wprowadzenie:
Projekt "Automatyczny robot sprzątający" jest projektem bazującym się na symulacji pracy robota sprzątającego w pomieszczeniu za pomocą sztucznej inteligencji. Robot ma za zadanie wyznaczać miejsca do sprzątania oraz uniknąć przeszkód oraz reagować na zdarzenia losowe. Projekt jest napisany w języku Python. Projekt "Automatyczny robot sprzątający" jest projektem bazującym się na symulacji pracy robota sprzątającego w pomieszczeniu za pomocą sztucznej inteligencji. Robot ma za zadanie wyznaczać miejsca do sprzątania oraz uniknąć przeszkód oraz reagować na zdarzenia losowe. Projekt jest napisany w języku Python.
Instrukcja obsługi: Instrukcja obsługi:
Uruchomienie projektu: Uruchomienie projektu:
Aby uruchomić projekt należy uruchomić plik "main.py" za pomocą interpretera Python. Projektu wyświetli się w konsoli.Po uruchomieniu projektu na ekranie wyświetli się plansza o wymiarach NxN (default: 10x10). Robot "Cleaner" (oznaczony jako "R" na planszy) startuje z pozycji (0,0). użytkownik ma za zadanie wprowadzić pozycje do sprzątania, które są oznaczone na planszy jako litery "D". Możliwe pozycje to liczby od 0 do N-1. Aby uruchomić projekt należy uruchomić plik "main.py" za pomocą interpretera Python. Projektu wyświetli się w konsoli.Po uruchomieniu projektu na ekranie wyświetli się plansza o wymiarach NxN (default: 10x10). Robot "Cleaner" (oznaczony jako "R" na planszy) startuje z pozycji (0,0). użytkownik ma za zadanie wprowadzić pozycje do sprzątania, które są oznaczone na planszy jako litery "D". Możliwe pozycje to liczby od 0 do N-1.
Użytkownik wprowadza pozycje za pomocą terminala. Wprowadzenie koordynat odbywa się w następujący sposób: Użytkownik wprowadza pozycje za pomocą terminala. Wprowadzenie koordynat odbywa się w następujący sposób:
Najpierw wprowadzamy numer wiersza, a następnie numer kolumny, oddzielając je spacją. Najpierw wprowadzamy numer wiersza, a następnie numer kolumny, oddzielając je spacją.
Przykładowo, jeśli chcemy wskazać pozycję (4,5) wpisujemy: "4 5". Przykładowo, jeśli chcemy wskazać pozycję (4,5) wpisujemy: "4 5".
Po wskazaniu pozycji do sprzątania, użytkownik musi uniknąć przeszkód, które są oznaczone na planszy jako znak "X". Robot nie może przejść przez przeszkody. Jeśli użytkownik wskazuje pozycję przeszkody, projektu zwróci błąd i będzie wymagała podania nowych współrzędnych. Po wskazaniu pozycji do sprzątania, użytkownik musi uniknąć przeszkód, które są oznaczone na planszy jako znak "X". Robot nie może przejść przez przeszkody. Jeśli użytkownik wskazuje pozycję przeszkody, projektu zwróci błąd i będzie wymagała podania nowych współrzędnych.
Przebieg projektu: Przebieg projektu:
Robot, zgodnie z zbudowaną mapą, musi obliczyć najkrótszą ścieżkę do sprzątania wszystkich pozycji oraz uniknąć przeszkód. Podczas sprzątania mogą wystąpić przypadkowe zdarzenia, na które robot będzie reagował. W tym celu, z pomocą sieci neuronowych, robot analizuje zdjęcie zdarzenia, aby wybrać najlepsze rozwiązania. Robot, zgodnie z zbudowaną mapą, musi obliczyć najkrótszą ścieżkę do sprzątania wszystkich pozycji oraz uniknąć przeszkód. Podczas sprzątania mogą wystąpić przypadkowe zdarzenia, na które robot będzie reagował. W tym celu, z pomocą sieci neuronowych, robot analizuje zdjęcie zdarzenia, aby wybrać najlepsze rozwiązania.
Zakończenie projektu: Zakończenie projektu:
Program kończy swoje działanie w momencie, gdy robot posprząta wszystkie przez użytkownika wybrane pola do sprzątania. Na zakończenie programu zostanie wyświetlona liczba wykonanych ruchów przez robota oraz podjęte decyzje w przypadku zaistnienia zdarzeń. Program kończy swoje działanie w momencie, gdy robot posprząta wszystkie przez użytkownika wybrane pola do sprzątania. Na zakończenie programu zostanie wyświetlona liczba wykonanych ruchów przez robota oraz podjęte decyzje w przypadku zaistnienia zdarzeń.
Możliwe modyfikacje: Możliwe modyfikacje:
Projekt zostanie napisany z myślą o możliwości łatwej modyfikacji. Można zmienić wymiary planszy, dodać lub usunąć przeszkody oraz ilość przypadkowych zdarzeń i pozycji do sprzątania. Wszystkie te zmiany można wprowadzić w pliku "config.py". Projekt zostanie napisany z myślą o możliwości łatwej modyfikacji. Można zmienić wymiary planszy, dodać lub usunąć przeszkody oraz ilość przypadkowych zdarzeń i pozycji do sprzątania. Wszystkie te zmiany można wprowadzić w pliku "config.py".
Podsumowanie: Podsumowanie:
Projekt "Automatyczny robot sprzątający" to prosty, ale edukacyjny projekt programistyczny. Użytkownik ma za zadanie wskazanie pozycji, które robot powinien posprzątać, a także koordynat przeszkody. Natomiast zadaniem robota, który został zbudowany przy użyciu sztucznej inteligencji, jest unikanie przeszkód, podejmowanie decyzji w przypadku wystąpienia przypadkowych zdarzeń oraz sprzątanie wyznaczonych punktów. Projekt został napisany w języku Python z wykorzystaniem sztucznej inteligencji. Analiza zdjęć jest oparta na sieciach neuronowych. Projekt "Automatyczny robot sprzątający" to prosty, ale edukacyjny projekt programistyczny. Użytkownik ma za zadanie wskazanie pozycji, które robot powinien posprzątać, a także koordynat przeszkody. Natomiast zadaniem robota, który został zbudowany przy użyciu sztucznej inteligencji, jest unikanie przeszkód, podejmowanie decyzji w przypadku wystąpienia przypadkowych zdarzeń oraz sprzątanie wyznaczonych punktów. Projekt został napisany w języku Python z wykorzystaniem sztucznej inteligencji. Analiza zdjęć jest oparta na sieciach neuronowych.
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Documentation of the "Automatic Cleaning Robot" project Documentation of the "Automatic Cleaning Robot" project
Introduction: Introduction:
The "Automatic Cleaning Robot" project is based on simulating the work of a cleaning robot in a room using artificial intelligence. The robot is tasked with determining the areas to be cleaned, avoiding obstacles, and reacting to random events. The project is written in Python. The "Automatic Cleaning Robot" project is based on simulating the work of a cleaning robot in a room using artificial intelligence. The robot is tasked with determining the areas to be cleaned, avoiding obstacles, and reacting to random events. The project is written in Python.
User Guide: User Guide:
Starting the project: Starting the project:
To start the project, you need to run the "main.py" file using a Python interpreter. The project will be displayed on the console. Once the project is launched, a 10x10 board will be displayed on the screen. The "Cleaner" robot (marked as "R" on the board) starts from the position (0,0). The user needs to enter the positions to be cleaned, which are marked as the letter "D" on the board. The possible positions are numbers from 0 to 9. To start the project, you need to run the "main.py" file using a Python interpreter. The project will be displayed on the console. Once the project is launched, a 10x10 board will be displayed on the screen. The "Cleaner" robot (marked as "R" on the board) starts from the position (0,0). The user needs to enter the positions to be cleaned, which are marked as the letter "D" on the board. The possible positions are numbers from 0 to 9.
The user enters the positions through the terminal. The entry of coordinates is done as follows: The user enters the positions through the terminal. The entry of coordinates is done as follows:
First, we enter the row number, and then the column number, separating them with a space. First, we enter the row number, and then the column number, separating them with a space.
For example, if we want to indicate the position (4,5), we enter "4 5". For example, if we want to indicate the position (4,5), we enter "4 5".
After indicating the positions to be cleaned, the user must avoid obstacles, which are marked on the board as the "X" symbol. The robot cannot pass through obstacles. If the user points to an obstacle position, the project will return an error and require new coordinates. After indicating the positions to be cleaned, the user must avoid obstacles, which are marked on the board as the "X" symbol. The robot cannot pass through obstacles. If the user points to an obstacle position, the project will return an error and require new coordinates.
Project process: Project process:
Based on the built map, the robot must calculate the shortest path to clean all positions and avoid obstacles. Random events may occur during cleaning, to which the robot will react. To do this, with the help of neural networks, the robot analyzes the image of the event to choose the best solutions. Based on the built map, the robot must calculate the shortest path to clean all positions and avoid obstacles. Random events may occur during cleaning, to which the robot will react. To do this, with the help of neural networks, the robot analyzes the image of the event to choose the best solutions.
Project conclusion: Project conclusion:
The program is ending when the robot cleans all the fields selected by the user. At the end of the program, the number of robot moves performed and the decisions made in case of events will be displayed. The program is ending when the robot cleans all the fields selected by the user. At the end of the program, the number of robot moves performed and the decisions made in case of events will be displayed.
Possible modifications: Possible modifications:
The "Automatic cleaning robot" project has been designed with the possibility of easy modifications in mind. Users can change the dimensions of the board, add or remove obstacles, and adjust the number of random events and cleaning positions. All these changes can be made in the "config.py" file. The "Automatic cleaning robot" project has been designed with the possibility of easy modifications in mind. Users can change the dimensions of the board, add or remove obstacles, and adjust the number of random events and cleaning positions. All these changes can be made in the "config.py" file.
Summary: Summary:
The "Automatic cleaning robot" project is a simple yet educational programming project. Users are tasked with specifying the positions that the robot should clean, as well as the coordinates of obstacles. The robot, built using artificial intelligence, is responsible for avoiding obstacles, making decisions in case of random events, and cleaning the designated points. The project was written in Python with the use of artificial intelligence. The analysis of images is based on neural networks. The "Automatic cleaning robot" project is a simple yet educational programming project. Users are tasked with specifying the positions that the robot should clean, as well as the coordinates of obstacles. The robot, built using artificial intelligence, is responsible for avoiding obstacles, making decisions in case of random events, and cleaning the designated points. The project was written in Python with the use of artificial intelligence. The analysis of images is based on neural networks.
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Dokumentacja projektu "Automatyczny robot sprzątający" Dokumentacja projektu "Automatyczny robot sprzątający"
Wprowadzenie: Wprowadzenie:
Projekt "Automatyczny robot sprzątający" jest projektem bazującym się na symulacji pracy robota sprzątającego w pomieszczeniu za pomocą sztucznej intelegencji. Robot ma za zadanie wyznaczać miejsca do sprzątania oraz uniknąć przeszkód oraz reagować na zdarzenia randomowe. Projekt jest napisany w języku Python. Projekt "Automatyczny robot sprzątający" jest projektem bazującym się na symulacji pracy robota sprzątającego w pomieszczeniu za pomocą sztucznej intelegencji. Robot ma za zadanie wyznaczać miejsca do sprzątania oraz uniknąć przeszkód oraz reagować na zdarzenia randomowe. Projekt jest napisany w języku Python.
Instrukcja obsługi: Instrukcja obsługi:
Uruchomienie projektu: Uruchomienie projektu:
Aby uruchomić projekt należy uruchomić plik "main.py" za pomocą interpretera Python. Projektu wyświetli się w konsoli.Po uruchomieniu projektu na ekranie wyświetli się plansza o wymiarach 10x10. Robot "Cleaner" (oznaczony jako "R" na planszy) startuje z pozycji (0,0). użytkownik ma za zadanie wprowadzić pozycje do sprzątania, które są oznaczone na planszy jako litery "D". Możliwe pozycje to liczby od 0 do 9. Aby uruchomić projekt należy uruchomić plik "main.py" za pomocą interpretera Python. Projektu wyświetli się w konsoli.Po uruchomieniu projektu na ekranie wyświetli się plansza o wymiarach 10x10. Robot "Cleaner" (oznaczony jako "R" na planszy) startuje z pozycji (0,0). użytkownik ma za zadanie wprowadzić pozycje do sprzątania, które są oznaczone na planszy jako litery "D". Możliwe pozycje to liczby od 0 do 9.
Użytkownik wprowadza pozycje za pomocą terminala. Wprowadzenie koordynat odbywa się w następujący sposób: Użytkownik wprowadza pozycje za pomocą terminala. Wprowadzenie koordynat odbywa się w następujący sposób:
Najpierw wprowadzamy numer wiersza, a następnie numer kolumny, oddzielając je spacją. Najpierw wprowadzamy numer wiersza, a następnie numer kolumny, oddzielając je spacją.
Przykładowo, jeśli chcemy wskazać pozycję (4,5) wpisujemy: "4 5". Przykładowo, jeśli chcemy wskazać pozycję (4,5) wpisujemy: "4 5".
Po wskazaniu pozycji do sprzątania, użytkownik musi uniknąć przeszkód, które są oznaczone na planszy jako znak "X". Robot nie może przejść przez przeszkody. Jeśli użytkownik wskazuje pozycję przeszkody, projektu zwróci błąd i będzie wymagała podania nowych koordynatów. Po wskazaniu pozycji do sprzątania, użytkownik musi uniknąć przeszkód, które są oznaczone na planszy jako znak "X". Robot nie może przejść przez przeszkody. Jeśli użytkownik wskazuje pozycję przeszkody, projektu zwróci błąd i będzie wymagała podania nowych koordynatów.
Przebieg projektu: Przebieg projektu:
Robot, zgodnie z zbudowaną mapą, musi obliczyć najkrótszą ścieżkę do sprzątania wszystkich pozycji oraz uniknąć przeszkód. Podczas sprzątania mogą wystąpić przypadkowe zdarzenia, na które robot będzie reagował. W tym celu, z pomocą sieci neuronowych, robot analizuje zdjęcie zdarzenia, aby wybrać najlepsze rozwiązania. Robot, zgodnie z zbudowaną mapą, musi obliczyć najkrótszą ścieżkę do sprzątania wszystkich pozycji oraz uniknąć przeszkód. Podczas sprzątania mogą wystąpić przypadkowe zdarzenia, na które robot będzie reagował. W tym celu, z pomocą sieci neuronowych, robot analizuje zdjęcie zdarzenia, aby wybrać najlepsze rozwiązania.
Zakończenie projektu: Zakończenie projektu:
Program kończy swoje działanie w momencie, gdy robot posprząta wszystkie przez użytkownika wybrane pola do sprzątania. Na zakończenie programu zostanie wyświetlona liczba wykonanych ruchów przez robota oraz podjęte decyzje w przypadku zaistnienia zdarzeń. Program kończy swoje działanie w momencie, gdy robot posprząta wszystkie przez użytkownika wybrane pola do sprzątania. Na zakończenie programu zostanie wyświetlona liczba wykonanych ruchów przez robota oraz podjęte decyzje w przypadku zaistnienia zdarzeń.
Możliwe modyfikacje: Możliwe modyfikacje:
Projekt zostanie napisany z myślą o możliwości łatwej modyfikacji. Można zmienić wymiary planszy, dodać lub usunąć przeszkody oraz ilość przypadkowych zdarzeń i pozycji do sprzątania. Wszystkie te zmiany można wprowadzić w pliku "config.py". Projekt zostanie napisany z myślą o możliwości łatwej modyfikacji. Można zmienić wymiary planszy, dodać lub usunąć przeszkody oraz ilość przypadkowych zdarzeń i pozycji do sprzątania. Wszystkie te zmiany można wprowadzić w pliku "config.py".
Podsumowanie: Podsumowanie:
Projekt "Automatyczny robot sprzątający" to prosty, ale edukacyjny projekt programistyczny. Użytkownik ma za zadanie wskazanie pozycji, które robot powinien posprzątać, a także koordynat przeszkody. Natomiast zadaniem robota, który został zbudowany przy użyciu sztucznej inteligencji, jest unikanie przeszkód, podejmowanie decyzji w przypadku wystąpienia przypadkowych zdarzeń oraz sprzątanie wyznaczonych punktów. Projekt został napisany w języku Python z wykorzystaniem sztucznej inteligencji.Analiza zdięć jest oparta na sieciach neuronowych. Projekt "Automatyczny robot sprzątający" to prosty, ale edukacyjny projekt programistyczny. Użytkownik ma za zadanie wskazanie pozycji, które robot powinien posprzątać, a także koordynat przeszkody. Natomiast zadaniem robota, który został zbudowany przy użyciu sztucznej inteligencji, jest unikanie przeszkód, podejmowanie decyzji w przypadku wystąpienia przypadkowych zdarzeń oraz sprzątanie wyznaczonych punktów. Projekt został napisany w języku Python z wykorzystaniem sztucznej inteligencji.Analiza zdięć jest oparta na sieciach neuronowych.
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Documentation of the "Automatic Cleaning Robot" project Documentation of the "Automatic Cleaning Robot" project
Introduction: Introduction:
The "Automatic Cleaning Robot" project is based on simulating the work of a cleaning robot in a room using artificial intelligence. The robot is tasked with determining the areas to be cleaned, avoiding obstacles, and reacting to random events. The project is written in Python. The "Automatic Cleaning Robot" project is based on simulating the work of a cleaning robot in a room using artificial intelligence. The robot is tasked with determining the areas to be cleaned, avoiding obstacles, and reacting to random events. The project is written in Python.
User Guide: User Guide:
Starting the project: Starting the project:
To start the project, you need to run the "main.py" file using a Python interpreter. The project will be displayed on the console. Once the project is launched, a 10x10 board will be displayed on the screen. The "Cleaner" robot (marked as "R" on the board) starts from the position (0,0). The user needs to enter the positions to be cleaned, which are marked as the letter "D" on the board. The possible positions are numbers from 0 to 9. To start the project, you need to run the "main.py" file using a Python interpreter. The project will be displayed on the console. Once the project is launched, a 10x10 board will be displayed on the screen. The "Cleaner" robot (marked as "R" on the board) starts from the position (0,0). The user needs to enter the positions to be cleaned, which are marked as the letter "D" on the board. The possible positions are numbers from 0 to 9.
The user enters the positions through the terminal. The entry of coordinates is done as follows: The user enters the positions through the terminal. The entry of coordinates is done as follows:
First, we enter the row number, and then the column number, separating them with a space. First, we enter the row number, and then the column number, separating them with a space.
For example, if we want to indicate the position (4,5), we enter "4 5". For example, if we want to indicate the position (4,5), we enter "4 5".
After indicating the positions to be cleaned, the user must avoid obstacles, which are marked on the board as the "X" symbol. The robot cannot pass through obstacles. If the user points to an obstacle position, the project will return an error and require new coordinates. After indicating the positions to be cleaned, the user must avoid obstacles, which are marked on the board as the "X" symbol. The robot cannot pass through obstacles. If the user points to an obstacle position, the project will return an error and require new coordinates.
Project process: Project process:
Based on the built map, the robot must calculate the shortest path to clean all positions and avoid obstacles. Random events may occur during cleaning, to which the robot will react. To do this, with the help of neural networks, the robot analyzes the image of the event to choose the best solutions. Based on the built map, the robot must calculate the shortest path to clean all positions and avoid obstacles. Random events may occur during cleaning, to which the robot will react. To do this, with the help of neural networks, the robot analyzes the image of the event to choose the best solutions.
Project conclusion: Project conclusion:
The program is ending when the robot cleans all the fields selected by the user. At the end of the program, the number of robot moves performed and the decisions made in case of events will be displayed. The program is ending when the robot cleans all the fields selected by the user. At the end of the program, the number of robot moves performed and the decisions made in case of events will be displayed.
Possible modifications: Possible modifications:
The "Automatic cleaning robot" project has been designed with the possibility of easy modifications in mind. Users can change the dimensions of the board, add or remove obstacles, and adjust the number of random events and cleaning positions. All these changes can be made in the "config.py" file. The "Automatic cleaning robot" project has been designed with the possibility of easy modifications in mind. Users can change the dimensions of the board, add or remove obstacles, and adjust the number of random events and cleaning positions. All these changes can be made in the "config.py" file.
Summary: Summary:
The "Automatic cleaning robot" project is a simple yet educational programming project. Users are tasked with specifying the positions that the robot should clean, as well as the coordinates of obstacles. The robot, built using artificial intelligence, is responsible for avoiding obstacles, making decisions in case of random events, and cleaning the designated points. The project was written in Python with the use of artificial intelligence. The analysis of images is based on neural networks. The "Automatic cleaning robot" project is a simple yet educational programming project. Users are tasked with specifying the positions that the robot should clean, as well as the coordinates of obstacles. The robot, built using artificial intelligence, is responsible for avoiding obstacles, making decisions in case of random events, and cleaning the designated points. The project was written in Python with the use of artificial intelligence. The analysis of images is based on neural networks.
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[APP] [APP]
cat = False cat = False
movement = robot movement = robot
#accept: human, robot #accept: human, robot

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decisionTree/data.csv
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1-2-3-4-5;1-green 2-yellow 3-orange 4-black 5-while 6-blue;in dB 0-100;0-24;0/1;in cm;in C;0/1 1 2 3 4 5;1 2 3 4 5 6 7 8 9;in db 0-100;0/1;0/1;in cm;in C;0-7;0/1
Size;Color;Sound;Time;Smell;Height;Temperature;ToRemove Size;Color;Sound;Sharp;Smell;Length;Temperature;Weight;ToRemove
1;2;0;16;1;10;25;1 1;1;0;0;0;2;22;0;1
2;1;0;12;0;50;24;0 1;2;0;0;0;2;22;0;1
2;3;30;13;1;38;38;0 1;3;0;0;0;2;22;0;1
1;4;0;7;1;5;27;1 1;4;0;0;0;2;22;0;1
1;2;0;16;1;10;25;1 1;5;0;0;0;2;22;0;1
2;1;0;12;0;50;24;0 1;6;0;0;0;2;22;0;1
2;3;30;13;1;38;38;0 1;7;0;0;0;2;22;0;1
1;4;0;7;1;5;27;1 1;8;0;0;0;2;22;0;1
1;2;0;16;1;10;25;1 1;9;0;0;0;2;22;0;1
2;1;0;12;0;50;24;0 1;2;0;0;1;3;25;0;1
2;3;30;13;1;38;38;0 1;2;0;0;1;4;25;0;1
1;4;0;7;1;5;27;1 1;2;0;0;1;5;25;0;1
1;2;0;16;1;10;25;1 2;2;0;0;1;3;25;0;1
2;1;0;12;0;50;24;0 2;2;0;0;1;4;25;0;1
2;3;30;13;1;38;38;0 2;2;0;0;1;5;25;0;1
1;4;0;7;1;5;27;1 2;2;0;0;1;6;25;0;1
1;2;0;16;1;10;25;1 3;2;0;0;1;3;25;0;1
2;1;0;12;0;50;24;0 1;7;0;0;1;3;25;2;1
2;3;30;13;1;38;38;0 1;6;0;0;1;4;25;2;1
1;4;0;7;1;5;27;1 1;6;0;0;1;5;25;2;1
1;6;0;0;1;2;25;2;1
2;6;0;0;1;2;25;3;1
2;6;0;0;1;3;25;3;1
2;6;0;0;1;4;25;3;1
2;6;0;0;1;5;25;3;1
3;6;0;0;1;2;25;4;1
2;1;0;0;0;20;24;1;0
2;2;0;0;0;20;24;1;0
2;3;0;0;0;20;24;1;0
2;4;0;0;0;20;24;1;0
2;5;0;0;0;20;24;1;0
2;6;0;0;0;20;24;1;0
2;7;0;0;0;20;24;1;0
2;8;0;0;0;20;24;1;0
2;9;0;0;0;20;24;1;0
1;1;0;1;0;1;20;0;0
1;2;0;1;0;1;20;0;0
1;3;0;1;0;1;20;0;0
1;4;0;1;0;1;20;0;0
1;5;0;1;0;1;20;0;0
1;6;0;1;0;1;20;0;0
1;7;0;1;0;1;20;0;0
1;8;0;1;0;1;20;0;0
2;4;0;0;0;14;22;1;0
1;2;0;0;1;6;24;1;1
2;2;0;0;1;6;24;1;1
1;2;0;0;1;5;24;1;1
3;1;4;0;0;18;24;2;0
3;2;4;0;0;18;24;2;0
3;3;4;0;0;18;24;2;0
3;4;4;0;0;18;24;2;0
3;5;4;0;0;18;24;2;0
3;6;4;0;0;18;24;2;0
3;7;4;0;0;18;24;2;0
3;8;4;0;0;18;24;2;0
3;9;4;0;0;18;24;2;0
4;3;20;0;1;32;37;5;0
4;4;20;0;1;32;37;5;0
4;5;20;0;1;32;37;5;0
4;6;20;0;1;32;37;5;0
5;3;25;0;1;40;37;6;0
5;4;25;0;1;40;37;6;0
5;5;25;0;1;40;37;6;0
5;6;25;0;1;40;37;6;0
1;5;0;0;0;20;22;2;0
1;5;0;0;0;30;22;2;0
1;5;0;0;0;40;22;2;0
1;5;0;0;0;50;22;2;0
1;4;0;0;0;20;22;2;0
1;4;0;0;0;30;22;2;0
1;4;0;0;0;40;22;2;0
1;4;0;0;0;50;22;2;0
2;5;0;0;0;20;22;2;0
2;5;0;0;0;30;22;2;0
2;5;0;0;0;40;22;2;0
2;4;0;0;0;20;22;2;0
2;4;0;0;0;30;22;2;0
2;4;0;0;0;40;22;2;0
1;5;0;0;1;2;24;0;1
1;3;0;0;0;13;23;0;1
1;4;0;0;0;13;23;0;1
1;5;0;0;0;13;23;0;1
1;6;0;0;0;13;23;0;1
1;3;0;0;0;14;23;0;1
1;4;0;0;0;14;23;0;1
1;5;0;0;0;14;23;0;1
1;6;0;0;0;14;23;0;1
1;3;0;0;0;15;23;0;1
1;4;0;0;0;15;23;0;1
1;5;0;0;0;15;23;0;1
1;6;0;0;0;15;23;0;1
1;1;0;1;0;3;22;1;1
1;2;0;1;0;3;22;1;1
1;3;0;1;0;3;22;1;1
1;4;0;1;0;3;22;1;1
1;5;0;1;0;3;22;1;1
1;6;0;1;0;3;22;1;1
1;7;0;1;0;3;22;1;1
1;8;0;1;0;3;22;1;1
1;9;0;1;0;3;22;1;1
2;1;0;1;0;7;22;1;0
2;2;0;1;0;7;22;1;0
2;3;0;1;0;7;22;1;0
2;4;0;1;0;7;22;1;0
2;5;0;1;0;7;22;1;0
2;6;0;1;0;7;22;1;0
2;7;0;1;0;7;22;1;0
2;8;0;1;0;7;22;1;0
2;9;0;1;0;7;22;1;0
3;3;10;0;1;24;36;3;0
3;4;10;0;1;24;36;3;0
3;5;10;0;1;24;36;3;0
3;6;10;0;1;24;36;3;0
1;1;0;0;0;2;20;1;0
1;2;0;0;0;2;20;1;0
1;3;0;0;0;2;20;1;0
1;4;0;0;0;2;20;1;0
1;5;0;0;0;2;20;1;0
1;6;0;0;0;2;20;1;0
1;7;0;0;0;2;20;1;0
1;8;0;0;0;2;20;1;0
1;9;0;0;0;2;20;1;0
2;1;0;0;1;2;24;0;1
2;2;0;0;1;2;24;0;1
2;3;0;0;1;2;24;0;1
2;7;0;0;1;2;24;0;1
2;8;0;0;1;2;24;0;1
2;9;0;0;1;2;24;0;1
1;5;0;0;0;2;22;0;1
1;5;0;0;0;3;22;0;1
1;5;0;0;0;4;22;0;1
1;6;0;0;0;2;22;0;1
1;6;0;0;0;3;22;0;1
1;6;0;0;0;4;22;0;1
2;5;0;0;0;2;22;0;1
2;5;0;0;0;3;22;0;1
2;5;0;0;0;4;22;0;1
2;6;0;0;0;2;22;0;1
2;6;0;0;0;3;22;0;1
2;6;0;0;0;4;22;0;1
2;1;0;0;0;2;20;0;0
2;2;0;0;0;2;20;0;0
2;3;0;0;0;2;20;0;0
2;4;0;0;0;2;20;0;0
2;5;0;0;0;2;20;0;0
2;6;0;0;0;2;20;0;0
2;7;0;0;0;2;20;0;0
2;8;0;0;0;2;20;0;0
2;9;0;0;0;2;20;0;0
2;1;0;0;1;3;22;0;1
2;2;0;0;1;3;22;0;1
2;3;0;0;1;3;22;0;1
2;4;0;0;1;3;22;0;1
2;5;0;0;1;3;22;0;1
2;6;0;0;1;3;22;0;1
2;7;0;0;1;3;22;0;1
2;8;0;0;1;3;22;0;1
2;9;0;0;1;3;22;0;1
3;1;0;0;0;16;23;3;0
3;2;0;0;0;16;23;3;0
3;3;0;0;0;16;23;3;0
3;4;0;0;0;16;23;3;0
3;5;0;0;0;16;23;3;0
3;6;0;0;0;16;23;3;0
3;7;0;0;0;16;23;3;0
3;8;0;0;0;16;23;3;0
3;9;0;0;0;16;23;3;0
1;5;0;0;0;2;23;0;1
1;5;0;0;0;3;23;0;1
1;5;0;0;0;4;23;0;1
1;5;0;0;0;5;23;0;1
1;5;0;0;0;6;23;0;1
2;5;0;0;0;3;23;0;1
2;5;0;0;0;4;23;0;1
2;5;0;0;0;5;23;0;1
2;5;0;0;0;6;23;0;1
2;5;0;0;0;2;23;0;1
2;5;0;0;1;4;26;1;1
3;5;0;0;1;4;26;2;1
1;7;0;1;0;1;22;0;1
1;7;0;1;0;1;22;0;1
4;1;0;0;1;30;21;4;0
4;1;0;0;1;25;21;4;0
1;6;0;0;1;1;22;0;1
1;6;0;0;1;1;22;1;1
4;3;30;0;1;50;36;4;0
5;3;30;0;1;50;36;4;0
1;1;0;0;0;9;22;0;0
1;8;0;0;0;9;22;0;0
3;1;0;0;0;25;22;2;0
3;2;0;0;0;25;22;2;0
3;3;0;0;0;25;22;2;0
3;4;0;0;0;25;22;2;0
3;5;0;0;0;25;22;2;0
3;6;0;0;0;25;22;2;0
3;7;0;0;0;25;22;2;0
3;8;0;0;0;25;22;2;0
3;9;0;0;0;25;22;2;0

1 1-2-3-4-5 1 2 3 4 5 1-green 2-yellow 3-orange 4-black 5-while 6-blue 1 2 3 4 5 6 7 8 9 in dB 0-100 in db 0-100 0-24 0/1 0/1 in cm in C 0-7 0/1
2 Size Size Color Color Sound Sound Time Smell Sharp ToRemove Smell Height Length Temperature Weight ToRemove
3 1 1 2 1 0 0 16 1 0 1 0 10 2 25 22 0 1
4 2 1 1 2 0 0 12 0 0 50 2 24 22 0 1
5 2 1 3 3 30 0 13 1 0 0 38 2 38 22 0 1
6 1 1 4 4 0 0 7 1 0 1 0 5 2 27 22 0 1
7 1 1 2 5 0 0 16 1 0 1 0 10 2 25 22 0 1
8 2 1 1 6 0 0 12 0 0 50 2 24 22 0 1
9 2 1 3 7 30 0 13 1 0 0 38 2 38 22 0 1
10 1 1 4 8 0 0 7 1 0 1 0 5 2 27 22 0 1
11 1 1 2 9 0 0 16 1 0 1 0 10 2 25 22 0 1
12 2 1 1 2 0 0 12 0 0 1 50 3 24 25 0 1
13 2 1 3 2 30 0 13 1 0 0 1 38 4 38 25 0 1
14 1 1 4 2 0 0 7 1 0 1 5 27 25 0 1
15 1 2 2 2 0 0 16 1 0 1 10 3 25 0 1
16 2 2 1 2 0 0 12 0 0 1 50 4 24 25 0 1
17 2 2 3 2 30 0 13 1 0 0 1 38 5 38 25 0 1
18 1 2 4 2 0 0 7 1 0 1 5 6 27 25 0 1
19 1 3 2 2 0 0 16 1 0 1 10 3 25 0 1
20 2 1 1 7 0 0 12 0 0 1 50 3 24 25 2 1
21 2 1 3 6 30 0 13 1 0 0 1 38 4 38 25 2 1
22 1 1 4 6 0 0 7 1 0 1 5 27 25 2 1
23 1 6 0 0 1 2 25 2 1
24 2 6 0 0 1 2 25 3 1
25 2 6 0 0 1 3 25 3 1
26 2 6 0 0 1 4 25 3 1
27 2 6 0 0 1 5 25 3 1
28 3 6 0 0 1 2 25 4 1
29 2 1 0 0 0 20 24 1 0
30 2 2 0 0 0 20 24 1 0
31 2 3 0 0 0 20 24 1 0
32 2 4 0 0 0 20 24 1 0
33 2 5 0 0 0 20 24 1 0
34 2 6 0 0 0 20 24 1 0
35 2 7 0 0 0 20 24 1 0
36 2 8 0 0 0 20 24 1 0
37 2 9 0 0 0 20 24 1 0
38 1 1 0 1 0 1 20 0 0
39 1 2 0 1 0 1 20 0 0
40 1 3 0 1 0 1 20 0 0
41 1 4 0 1 0 1 20 0 0
42 1 5 0 1 0 1 20 0 0
43 1 6 0 1 0 1 20 0 0
44 1 7 0 1 0 1 20 0 0
45 1 8 0 1 0 1 20 0 0
46 2 4 0 0 0 14 22 1 0
47 1 2 0 0 1 6 24 1 1
48 2 2 0 0 1 6 24 1 1
49 1 2 0 0 1 5 24 1 1
50 3 1 4 0 0 18 24 2 0
51 3 2 4 0 0 18 24 2 0
52 3 3 4 0 0 18 24 2 0
53 3 4 4 0 0 18 24 2 0
54 3 5 4 0 0 18 24 2 0
55 3 6 4 0 0 18 24 2 0
56 3 7 4 0 0 18 24 2 0
57 3 8 4 0 0 18 24 2 0
58 3 9 4 0 0 18 24 2 0
59 4 3 20 0 1 32 37 5 0
60 4 4 20 0 1 32 37 5 0
61 4 5 20 0 1 32 37 5 0
62 4 6 20 0 1 32 37 5 0
63 5 3 25 0 1 40 37 6 0
64 5 4 25 0 1 40 37 6 0
65 5 5 25 0 1 40 37 6 0
66 5 6 25 0 1 40 37 6 0
67 1 5 0 0 0 20 22 2 0
68 1 5 0 0 0 30 22 2 0
69 1 5 0 0 0 40 22 2 0
70 1 5 0 0 0 50 22 2 0
71 1 4 0 0 0 20 22 2 0
72 1 4 0 0 0 30 22 2 0
73 1 4 0 0 0 40 22 2 0
74 1 4 0 0 0 50 22 2 0
75 2 5 0 0 0 20 22 2 0
76 2 5 0 0 0 30 22 2 0
77 2 5 0 0 0 40 22 2 0
78 2 4 0 0 0 20 22 2 0
79 2 4 0 0 0 30 22 2 0
80 2 4 0 0 0 40 22 2 0
81 1 5 0 0 1 2 24 0 1
82 1 3 0 0 0 13 23 0 1
83 1 4 0 0 0 13 23 0 1
84 1 5 0 0 0 13 23 0 1
85 1 6 0 0 0 13 23 0 1
86 1 3 0 0 0 14 23 0 1
87 1 4 0 0 0 14 23 0 1
88 1 5 0 0 0 14 23 0 1
89 1 6 0 0 0 14 23 0 1
90 1 3 0 0 0 15 23 0 1
91 1 4 0 0 0 15 23 0 1
92 1 5 0 0 0 15 23 0 1
93 1 6 0 0 0 15 23 0 1
94 1 1 0 1 0 3 22 1 1
95 1 2 0 1 0 3 22 1 1
96 1 3 0 1 0 3 22 1 1
97 1 4 0 1 0 3 22 1 1
98 1 5 0 1 0 3 22 1 1
99 1 6 0 1 0 3 22 1 1
100 1 7 0 1 0 3 22 1 1
101 1 8 0 1 0 3 22 1 1
102 1 9 0 1 0 3 22 1 1
103 2 1 0 1 0 7 22 1 0
104 2 2 0 1 0 7 22 1 0
105 2 3 0 1 0 7 22 1 0
106 2 4 0 1 0 7 22 1 0
107 2 5 0 1 0 7 22 1 0
108 2 6 0 1 0 7 22 1 0
109 2 7 0 1 0 7 22 1 0
110 2 8 0 1 0 7 22 1 0
111 2 9 0 1 0 7 22 1 0
112 3 3 10 0 1 24 36 3 0
113 3 4 10 0 1 24 36 3 0
114 3 5 10 0 1 24 36 3 0
115 3 6 10 0 1 24 36 3 0
116 1 1 0 0 0 2 20 1 0
117 1 2 0 0 0 2 20 1 0
118 1 3 0 0 0 2 20 1 0
119 1 4 0 0 0 2 20 1 0
120 1 5 0 0 0 2 20 1 0
121 1 6 0 0 0 2 20 1 0
122 1 7 0 0 0 2 20 1 0
123 1 8 0 0 0 2 20 1 0
124 1 9 0 0 0 2 20 1 0
125 2 1 0 0 1 2 24 0 1
126 2 2 0 0 1 2 24 0 1
127 2 3 0 0 1 2 24 0 1
128 2 7 0 0 1 2 24 0 1
129 2 8 0 0 1 2 24 0 1
130 2 9 0 0 1 2 24 0 1
131 1 5 0 0 0 2 22 0 1
132 1 5 0 0 0 3 22 0 1
133 1 5 0 0 0 4 22 0 1
134 1 6 0 0 0 2 22 0 1
135 1 6 0 0 0 3 22 0 1
136 1 6 0 0 0 4 22 0 1
137 2 5 0 0 0 2 22 0 1
138 2 5 0 0 0 3 22 0 1
139 2 5 0 0 0 4 22 0 1
140 2 6 0 0 0 2 22 0 1
141 2 6 0 0 0 3 22 0 1
142 2 6 0 0 0 4 22 0 1
143 2 1 0 0 0 2 20 0 0
144 2 2 0 0 0 2 20 0 0
145 2 3 0 0 0 2 20 0 0
146 2 4 0 0 0 2 20 0 0
147 2 5 0 0 0 2 20 0 0
148 2 6 0 0 0 2 20 0 0
149 2 7 0 0 0 2 20 0 0
150 2 8 0 0 0 2 20 0 0
151 2 9 0 0 0 2 20 0 0
152 2 1 0 0 1 3 22 0 1
153 2 2 0 0 1 3 22 0 1
154 2 3 0 0 1 3 22 0 1
155 2 4 0 0 1 3 22 0 1
156 2 5 0 0 1 3 22 0 1
157 2 6 0 0 1 3 22 0 1
158 2 7 0 0 1 3 22 0 1
159 2 8 0 0 1 3 22 0 1
160 2 9 0 0 1 3 22 0 1
161 3 1 0 0 0 16 23 3 0
162 3 2 0 0 0 16 23 3 0
163 3 3 0 0 0 16 23 3 0
164 3 4 0 0 0 16 23 3 0
165 3 5 0 0 0 16 23 3 0
166 3 6 0 0 0 16 23 3 0
167 3 7 0 0 0 16 23 3 0
168 3 8 0 0 0 16 23 3 0
169 3 9 0 0 0 16 23 3 0
170 1 5 0 0 0 2 23 0 1
171 1 5 0 0 0 3 23 0 1
172 1 5 0 0 0 4 23 0 1
173 1 5 0 0 0 5 23 0 1
174 1 5 0 0 0 6 23 0 1
175 2 5 0 0 0 3 23 0 1
176 2 5 0 0 0 4 23 0 1
177 2 5 0 0 0 5 23 0 1
178 2 5 0 0 0 6 23 0 1
179 2 5 0 0 0 2 23 0 1
180 2 5 0 0 1 4 26 1 1
181 3 5 0 0 1 4 26 2 1
182 1 7 0 1 0 1 22 0 1
183 1 7 0 1 0 1 22 0 1
184 4 1 0 0 1 30 21 4 0
185 4 1 0 0 1 25 21 4 0
186 1 6 0 0 1 1 22 0 1
187 1 6 0 0 1 1 22 1 1
188 4 3 30 0 1 50 36 4 0
189 5 3 30 0 1 50 36 4 0
190 1 1 0 0 0 9 22 0 0
191 1 8 0 0 0 9 22 0 0
192 3 1 0 0 0 25 22 2 0
193 3 2 0 0 0 25 22 2 0
194 3 3 0 0 0 25 22 2 0
195 3 4 0 0 0 25 22 2 0
196 3 5 0 0 0 25 22 2 0
197 3 6 0 0 0 25 22 2 0
198 3 7 0 0 0 25 22 2 0
199 3 8 0 0 0 25 22 2 0
200 3 9 0 0 0 25 22 2 0

BIN
decisionTree/decision_tree_model.pkl
View File

Binary file not shown.

16
decisionTree/evaluate.py
View File

@ -1,9 +1,9 @@
import joblib import joblib
def evaluate(data): def evaluate(data):
# Load the model # Load the model
clf = joblib.load('decisionTree/decision_tree_model.pkl') clf = joblib.load('decisionTree/decision_tree_model.pkl')
# Make a prediction # Make a prediction
prediction = clf.predict(data) prediction = clf.predict(data)
return prediction return prediction

40
decisionTree/prepare.py
View File

@ -1,21 +1,21 @@
import pandas as pd import pandas as pd
from sklearn.tree import DecisionTreeClassifier from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split from sklearn.model_selection import train_test_split
from sklearn import metrics from sklearn import metrics
import joblib import joblib
pima = pd.read_csv("data.csv", header=1, delimiter=';') pima = pd.read_csv("data.csv", header=1, delimiter=';')
feature_cols = ['Size', 'Color', 'Sound', 'Time','Smell', 'Height','Temperature'] feature_cols = ['Size', 'Color', 'Sound', 'Sharp','Smell', 'Length','Temperature', 'Weight']
X = pima[feature_cols] X = pima[feature_cols]
y = pima.ToRemove y = pima.ToRemove
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)
clf = DecisionTreeClassifier() clf = DecisionTreeClassifier()
clf = clf.fit(X_train,y_train) clf = clf.fit(X_train.values, y_train)
joblib.dump(clf, 'decision_tree_model.pkl') joblib.dump(clf, 'decision_tree_model.pkl')
y_pred = clf.predict(X_test) y_pred = clf.predict(X_test)
print("Accuracy:",metrics.accuracy_score(y_test, y_pred)) print("Accuracy:",metrics.accuracy_score(y_test, y_pred))

6
domain/commands/command.py
View File

@ -1,3 +1,3 @@
class Command: class Command:
def run(self): def run(self):
raise NotImplementedError() raise NotImplementedError()

140
domain/commands/random_cat_move_command.py
View File

@ -1,70 +1,70 @@
from random import randint from random import randint
from typing import Tuple from typing import Tuple
import pygame import pygame
from domain.commands.command import Command from domain.commands.command import Command
from domain.entities.cat import Cat from domain.entities.cat import Cat
from domain.world import World from domain.world import World
class RandomCatMoveCommand(Command): class RandomCatMoveCommand(Command):
def __init__(self, world: World, cat: Cat) -> None: def __init__(self, world: World, cat: Cat) -> None:
super().__init__() super().__init__()
self.world = world self.world = world
self.cat = cat self.cat = cat
def run(self): def run(self):
move_vector = (0, 0) move_vector = (0, 0)
now = pygame.time.get_ticks() now = pygame.time.get_ticks()
# region cat random movement # region cat random movement
cat = self.world.cat cat = self.world.cat
if now - cat.last_tick >= cat.cooldown: if now - cat.last_tick >= cat.cooldown:
if not cat.busy: if not cat.busy:
while True: while True:
cat.direction = randint(0, 3) cat.direction = randint(0, 3)
if not ( if not (
(cat.direction == 0 and cat.y == 0) (cat.direction == 0 and cat.y == 0)
or (cat.direction == 1 and cat.x == self.world.width - 1) or (cat.direction == 1 and cat.x == self.world.width - 1)
or (cat.direction == 2 and cat.y == self.world.height - 1) or (cat.direction == 2 and cat.y == self.world.height - 1)
or (cat.direction == 3 and cat.x == 0) or (cat.direction == 3 and cat.x == 0)
): ):
break break
if cat.direction == 0: # up if cat.direction == 0: # up
if cat.busy: if cat.busy:
move_vector = (0, -1) move_vector = (0, -1)
cat.busy = not cat.busy cat.busy = not cat.busy
if cat.direction == 1: # right if cat.direction == 1: # right
if cat.busy: if cat.busy:
move_vector = (1, 0) move_vector = (1, 0)
cat.busy = not cat.busy cat.busy = not cat.busy
if cat.direction == 2: # down if cat.direction == 2: # down
if cat.busy: if cat.busy:
move_vector = (0, 1) move_vector = (0, 1)
cat.busy = not cat.busy cat.busy = not cat.busy
if cat.direction == 3: # left if cat.direction == 3: # left
if cat.busy: if cat.busy:
move_vector = (-1, 0) move_vector = (-1, 0)
cat.busy = not cat.busy cat.busy = not cat.busy
cat.last_tick = pygame.time.get_ticks() cat.last_tick = pygame.time.get_ticks()
if move_vector == (0, 0): if move_vector == (0, 0):
return return
end_x = cat.x + move_vector[0] end_x = cat.x + move_vector[0]
end_y = cat.y + move_vector[1] end_y = cat.y + move_vector[1]
if ( if (
end_x > self.world.width - 1 end_x > self.world.width - 1
or end_y > self.world.height - 1 or end_y > self.world.height - 1
or end_x < 0 or end_x < 0
or end_y < 0 or end_y < 0
): ):
return return
self.world.obstacles[cat.x][cat.y].remove(cat) self.world.obstacles[cat.x][cat.y].remove(cat)
cat.x = end_x cat.x = end_x
cat.y = end_y cat.y = end_y
self.world.obstacles[end_x][end_y].append(cat) self.world.obstacles[end_x][end_y].append(cat)
# endregion cat random movement # endregion cat random movement

68
domain/commands/vacuum_move_command.py
View File

@ -1,33 +1,35 @@
from typing import Tuple from typing import Tuple
from domain.commands.command import Command from domain.commands.command import Command
from domain.entities.vacuum import Vacuum from domain.entities.vacuum import Vacuum
from domain.world import World from domain.world import World
class VacuumMoveCommand(Command): class VacuumMoveCommand(Command):
def __init__( def __init__(
self, world: World, vacuum: Vacuum, move_vector: Tuple[int, int] self, world: World, vacuum: Vacuum, move_vector: Tuple[int, int]
) -> None: ) -> None:
super().__init__() super().__init__()
self.world = world self.world = world
self.vacuum = vacuum self.vacuum = vacuum
self.dx = move_vector[0] self.dx = move_vector[0]
self.dy = move_vector[1] self.dy = move_vector[1]
def run(self): def run(self):
end_x = self.vacuum.x + self.dx end_x = self.vacuum.x + self.dx
end_y = self.vacuum.y + self.dy end_y = self.vacuum.y + self.dy
if not self.world.accepted_move(end_x, end_y): if not self.world.accepted_move(end_x, end_y):
return return
if self.world.is_garbage_at(end_x, end_y): tmp = self.world.is_garbage_at(end_x, end_y)
if self.vacuum.get_container_filling() < 100: if len(tmp) > 0:
self.vacuum.increase_container_filling() for t in tmp:
self.world.dust[end_x][end_y].pop() if self.vacuum.get_container_filling() < 1000:
self.vacuum.increase_container_filling()
if self.world.is_docking_station_at(end_x, end_y): self.world.dust[end_x][end_y].remove(t)
self.vacuum.dump_trash()
if self.world.is_docking_station_at(end_x, end_y):
self.vacuum.x = end_x self.vacuum.dump_trash()
self.vacuum.y = end_y
self.vacuum.x = end_x
self.vacuum.y = end_y

34
domain/entities/cat.py
View File

@ -1,17 +1,17 @@
import pygame import pygame
from domain.entities.entity import Entity from domain.entities.entity import Entity
from domain.world import World from domain.world import World
class Cat(Entity): class Cat(Entity):
def __init__(self, x: int, y: int): def __init__(self, x: int, y: int):
super().__init__(x, y, "CAT") super().__init__(x, y, "CAT")
self.last_tick = pygame.time.get_ticks() self.last_tick = pygame.time.get_ticks()
self.cooldown = 1000 self.cooldown = 1000
self.velocity = 1 self.velocity = 1
self.busy = False self.busy = False
self.sleeping = False self.sleeping = False
self.direction = 0 self.direction = 0
self.props = [1,2,0,16,1,10,25] self.props = [4,2,20,0,1,32,37,5]

18
domain/entities/docking_station.py
View File

@ -1,10 +1,10 @@
from domain.entities.entity import Entity from domain.entities.entity import Entity
from domain.world import World from domain.world import World
class Doc_Station(Entity): class Doc_Station(Entity):
def __init__(self, x: int, y: int): def __init__(self, x: int, y: int):
super().__init__(x, y, "DOC_STATION") super().__init__(x, y, "DOC_STATION")
self.power = True self.power = True
# TODO Docing Station: add more properties # TODO Docing Station: add more properties

16
domain/entities/earring.py
View File

@ -1,8 +1,8 @@
from domain.entities.entity import Entity from domain.entities.entity import Entity
from domain.world import World from domain.world import World
class Earring(Entity): class Earring(Entity):
def __init__(self, x: int, y: int): def __init__(self, x: int, y: int):
super().__init__(x, y, "EARRING") super().__init__(x, y, "EARRING")
self.props = [2,1,0,12,0,50,24] self.props = [1,9,0,1,0,1,20,0]

10
domain/entities/entity.py
View File

@ -1,5 +1,5 @@
class Entity: class Entity:
def __init__(self, x: int, y: int, type: str): def __init__(self, x: int, y: int, type: str):
self.x = x self.x = x
self.y = y self.y = y
self.type = type self.type = type

22
domain/entities/garbage.py
View File

@ -1,11 +1,11 @@
from domain.entities.entity import Entity from domain.entities.entity import Entity
class Garbage(Entity): class Garbage(Entity):
def __init__(self, x: int, y: int): def __init__(self, x: int, y: int):
super().__init__(x, y, "PEEL") super().__init__(x, y, "PEEL")
self.wet = False self.wet = False
self.size = 0 self.size = 0
self.props = [1,2,0,16,1,10,25] self.props = [2,2,0,0,1,4,24,1]
# TODO GARBAGE: add more properties # TODO GARBAGE: add more properties

20
domain/entities/plant.py
View File

@ -1,10 +1,10 @@
from domain.entities.entity import Entity from domain.entities.entity import Entity
from domain.world import World from domain.world import World
class Plant(Entity): class Plant(Entity):
def __init__(self, x: int, y: int): def __init__(self, x: int, y: int):
super().__init__(x, y, "PLANT") super().__init__(x, y, "PLANT")
self.watered = 100 self.watered = 100
# TODO PLANT: add more properties to # TODO PLANT: add more properties to

44
domain/entities/vacuum.py
View File

@ -1,22 +1,22 @@
from domain.entities.entity import Entity from domain.entities.entity import Entity
from domain.world import World from domain.world import World
class Vacuum(Entity): class Vacuum(Entity):
def __init__(self, x: int, y: int): def __init__(self, x: int, y: int):
super().__init__(x, y, "VACUUM") super().__init__(x, y, "VACUUM")
self.direction = (1, 0) self.direction = (1, 0)
self.battery = 100 self.battery = 100
self.cleaning_detergent = 100 self.cleaning_detergent = 100
self.container_filling = 0 self.container_filling = 0
def increase_container_filling(self) -> None: def increase_container_filling(self) -> None:
self.container_filling += 25 self.container_filling += 25
def dump_trash(self) -> None: def dump_trash(self) -> None:
self.container_filling = 0 self.container_filling = 0
def get_container_filling(self): def get_container_filling(self):
return self.container_filling return self.container_filling
# TODO VACUUM: add more properties # TODO VACUUM: add more properties

113
domain/world.py
View File

@ -1,58 +1,57 @@
from decisionTree.evaluate import evaluate from decisionTree.evaluate import evaluate
from domain.entities.entity import Entity from domain.entities.entity import Entity
class World: class World:
def __init__(self, width: int, height: int) -> object: def __init__(self, width: int, height: int) -> object:
self.costs = [[1000 for j in range(height)] for i in range(width)] self.costs = [[1000 for j in range(height)] for i in range(width)]
self.width = width self.width = width
self.height = height self.height = height
self.dust = [[[] for j in range(height)] for i in range(width)] self.dust = [[[] for j in range(height)] for i in range(width)]
self.obstacles = [[[] for j in range(height)] for i in range(width)] self.obstacles = [[[] for j in range(height)] for i in range(width)]
self.vacuum = None self.vacuum = None
self.cat = None self.cat = None
self.doc_station = None self.doc_station = None
def add_entity(self, entity: Entity): def add_entity(self, entity: Entity):
if entity.type == "PEEL": if entity.type == "PEEL":
self.dust[entity.x][entity.y].append(entity) self.dust[entity.x][entity.y].append(entity)
elif entity.type == "EARRING": elif entity.type == "EARRING":
self.dust[entity.x][entity.y].append(entity) self.dust[entity.x][entity.y].append(entity)
elif entity.type == "VACUUM": elif entity.type == "VACUUM":
self.vacuum = entity self.vacuum = entity
elif entity.type == "DOC_STATION": elif entity.type == "DOC_STATION":
self.doc_station = entity self.doc_station = entity
elif entity.type == "CAT": elif entity.type == "CAT":
self.cat = entity self.cat = entity
self.obstacles[entity.x][entity.y].append(entity) self.obstacles[entity.x][entity.y].append(entity)
else: else:
self.obstacles[entity.x][entity.y].append(entity) self.obstacles[entity.x][entity.y].append(entity)
def is_obstacle_at(self, x: int, y: int) -> bool: def is_obstacle_at(self, x: int, y: int) -> bool:
return bool(self.obstacles[x][y]) return bool(self.obstacles[x][y])
def is_garbage_at(self, x: int, y: int) -> bool: def is_garbage_at(self, x: int, y: int):
if len(self.dust[x][y]) == 0: if len(self.dust[x][y]) == 0:
return False return []
tmp = evaluate([self.dust[x][y][0].props]) return [i for i in self.dust[x][y] if evaluate([i.props])[0] == 1]
return bool(tmp[0])
def is_docking_station_at(self, x: int, y: int) -> bool:
def is_docking_station_at(self, x: int, y: int) -> bool: return bool(self.doc_station.x == x and self.doc_station.y == y)
return bool(self.doc_station.x == x and self.doc_station.y == y)
def accepted_move(self, checking_x, checking_y):
def accepted_move(self, checking_x, checking_y): if (
if ( checking_x > self.width - 1
checking_x > self.width - 1 or checking_y > self.height - 1
or checking_y > self.height - 1 or checking_x < 0
or checking_x < 0 or checking_y < 0
or checking_y < 0 ):
): return False
return False
if self.is_obstacle_at(checking_x, checking_y):
if self.is_obstacle_at(checking_x, checking_y): return False
return False
return True
return True def get_cost(self, x, y):
def get_cost(self, x, y):
return self.costs[x][y] return self.costs[x][y]

340
main.py
View File

@ -1,169 +1,171 @@
from random import randint from random import randint
import pygame import pygame
import configparser import configparser
from domain.commands.random_cat_move_command import RandomCatMoveCommand from domain.commands.random_cat_move_command import RandomCatMoveCommand
from domain.commands.vacuum_move_command import VacuumMoveCommand from domain.commands.vacuum_move_command import VacuumMoveCommand
from domain.entities.cat import Cat from domain.entities.cat import Cat
from domain.entities.entity import Entity from domain.entities.entity import Entity
from domain.entities.vacuum import Vacuum from domain.entities.vacuum import Vacuum
from domain.entities.garbage import Garbage from domain.entities.garbage import Garbage
from domain.entities.earring import Earring from domain.entities.earring import Earring
from domain.entities.docking_station import Doc_Station from domain.entities.docking_station import Doc_Station
from domain.world import World from domain.world import World
from view.renderer import Renderer from view.renderer import Renderer
# from AI_brain.movement import GoAnyDirectionBFS, State # 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_astar import RotateAndGoAStar, State from AI_brain.rotate_and_go_astar import RotateAndGoAStar, State
config = configparser.ConfigParser() config = configparser.ConfigParser()
config.read("config.ini") config.read("config.ini")
class Main: class Main:
def __init__(self): def __init__(self):
tiles_x = 10 tiles_x = 10
tiles_y = 10 tiles_y = 10
self.renderer = Renderer(800, 800, tiles_x, tiles_y) self.renderer = Renderer(800, 800, tiles_x, tiles_y)
self.world = generate_world(tiles_x, tiles_y) self.world = generate_world(tiles_x, tiles_y)
self.commands = [] self.commands = []
self.clock = pygame.time.Clock() self.clock = pygame.time.Clock()
self.running = True self.running = True
self.fps = 60 self.fps = 60
def run(self): def run(self):
while self.running: while self.running:
self.process_input() self.process_input()
self.update() self.update()
self.renderer.render(self.world) self.renderer.render(self.world)
self.clock.tick(self.fps) self.clock.tick(self.fps)
pygame.quit() pygame.quit()
def run_robot(self): def run_robot(self):
self.renderer.render(self.world) self.renderer.render(self.world)
start_state = State(self.world.vacuum.x, self.world.vacuum.y) start_state = State(self.world.vacuum.x, self.world.vacuum.y)
end_state = State(self.world.doc_station.x, self.world.doc_station.y) end_state = State(self.world.doc_station.x, self.world.doc_station.y)
# path_searcher = GoAnyDirectionBFS(self.world, start_state, end_state) # 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) path_searcher = RotateAndGoAStar(self.world, start_state, end_state)
if not path_searcher.search(): if not path_searcher.search():
print("No solution") print("No solution")
exit(0) exit(0)
path_searcher.actions.reverse() path_searcher.actions.reverse()
while self.running: while self.running:
for event in pygame.event.get(): for event in pygame.event.get():
if event.type == pygame.QUIT: if event.type == pygame.QUIT:
self.running = False self.running = False
if len(path_searcher.actions) > 0: if len(path_searcher.actions) > 0:
action_direction = path_searcher.actions.pop() action_direction = path_searcher.actions.pop()
# self.handle_action1(action_direction) # self.handle_action1(action_direction)
self.handle_action2(action_direction) self.handle_action2(action_direction)
self.update() self.update()
self.renderer.render(self.world) self.renderer.render(self.world)
self.clock.tick(5) self.clock.tick(5)
pygame.quit() pygame.quit()
def handle_action1(self, action): def handle_action1(self, action):
if action == "UP": if action == "UP":
self.commands.append( self.commands.append(
VacuumMoveCommand(self.world, self.world.vacuum, (0, -1)) VacuumMoveCommand(self.world, self.world.vacuum, (0, -1))
) )
elif action == "DOWN": elif action == "DOWN":
self.commands.append( self.commands.append(
VacuumMoveCommand(self.world, self.world.vacuum, (0, 1)) VacuumMoveCommand(self.world, self.world.vacuum, (0, 1))
) )
elif action == "LEFT": elif action == "LEFT":
self.commands.append( self.commands.append(
VacuumMoveCommand(self.world, self.world.vacuum, (-1, 0)) VacuumMoveCommand(self.world, self.world.vacuum, (-1, 0))
) )
elif action == "RIGHT": elif action == "RIGHT":
self.commands.append( self.commands.append(
VacuumMoveCommand(self.world, self.world.vacuum, (1, 0)) VacuumMoveCommand(self.world, self.world.vacuum, (1, 0))
) )
def handle_action2(self, action): def handle_action2(self, action):
if action == "GO": if action == "GO":
self.commands.append( self.commands.append(
VacuumMoveCommand(self.world, self.world.vacuum, self.world.vacuum.direction) VacuumMoveCommand(self.world, self.world.vacuum, self.world.vacuum.direction)
) )
elif action == "RR": elif action == "RR":
self.world.vacuum.direction = (-self.world.vacuum.direction[1], self.world.vacuum.direction[0]) self.world.vacuum.direction = (-self.world.vacuum.direction[1], self.world.vacuum.direction[0])
elif action == "RL": elif action == "RL":
self.world.vacuum.direction = (self.world.vacuum.direction[1], -self.world.vacuum.direction[0]) self.world.vacuum.direction = (self.world.vacuum.direction[1], -self.world.vacuum.direction[0])
def process_input(self): def process_input(self):
for event in pygame.event.get(): for event in pygame.event.get():
if event.type == pygame.QUIT: if event.type == pygame.QUIT:
self.running = False self.running = False
if event.type == pygame.KEYDOWN: if event.type == pygame.KEYDOWN:
if event.key == pygame.K_LEFT: if event.key == pygame.K_LEFT:
self.commands.append( self.commands.append(
VacuumMoveCommand(self.world, self.world.vacuum, (-1, 0)) VacuumMoveCommand(self.world, self.world.vacuum, (-1, 0))
) )
if event.key == pygame.K_RIGHT: if event.key == pygame.K_RIGHT:
self.commands.append( self.commands.append(
VacuumMoveCommand(self.world, self.world.vacuum, (1, 0)) VacuumMoveCommand(self.world, self.world.vacuum, (1, 0))
) )
if event.key == pygame.K_UP: if event.key == pygame.K_UP:
self.commands.append( self.commands.append(
VacuumMoveCommand(self.world, self.world.vacuum, (0, -1)) VacuumMoveCommand(self.world, self.world.vacuum, (0, -1))
) )
if event.key == pygame.K_DOWN: if event.key == pygame.K_DOWN:
self.commands.append( self.commands.append(
VacuumMoveCommand(self.world, self.world.vacuum, (0, 1)) VacuumMoveCommand(self.world, self.world.vacuum, (0, 1))
) )
def update(self): def update(self):
if config.getboolean("APP", "cat"): if config.getboolean("APP", "cat"):
self.commands.append(RandomCatMoveCommand(self.world, self.world.cat)) self.commands.append(RandomCatMoveCommand(self.world, self.world.cat))
for command in self.commands: for command in self.commands:
command.run() command.run()
self.commands.clear() self.commands.clear()
def generate_world(tiles_x: int, tiles_y: int) -> World: def generate_world(tiles_x: int, tiles_y: int) -> World:
world = World(tiles_x, tiles_y) world = World(tiles_x, tiles_y)
for _ in range(35): for _ in range(35):
temp_x = randint(0, tiles_x - 1) temp_x = randint(0, tiles_x - 1)
temp_y = randint(0, tiles_y - 1) temp_y = randint(0, tiles_y - 1)
world.add_entity(Garbage(temp_x, temp_y)) world.add_entity(Garbage(temp_x, temp_y))
world.vacuum = Vacuum(1, 1) world.vacuum = Vacuum(1, 1)
world.doc_station = Doc_Station(9, 8) world.doc_station = Doc_Station(9, 8)
if config.getboolean("APP", "cat"): if config.getboolean("APP", "cat"):
world.cat = Cat(7, 8) world.cat = Cat(7, 8)
world.add_entity(world.cat) world.add_entity(world.cat)
world.add_entity(Entity(2, 8, "PLANT1")) world.add_entity(Entity(2, 8, "PLANT1"))
world.add_entity(Entity(4, 1, "PLANT1")) world.add_entity(Entity(4, 1, "PLANT1"))
world.add_entity(Entity(3, 4, "PLANT2")) world.add_entity(Entity(3, 4, "PLANT2"))
world.add_entity(Entity(8, 8, "PLANT2")) world.add_entity(Entity(8, 8, "PLANT2"))
world.add_entity(Entity(9, 3, "PLANT3")) world.add_entity(Entity(9, 3, "PLANT3"))
world.add_entity(Earring(5, 5)) world.add_entity(Earring(9, 7))
world.add_entity(Earring(5, 5))
world.add_entity(Earring(4, 6))
for x in range(world.width):
for y in range(world.height):
if world.is_garbage_at(x, y): for x in range(world.width):
world.costs[x][y] = 1 for y in range(world.height):
else: if world.is_garbage_at(x, y):
world.costs[x][y] = 10 world.costs[x][y] = 1
return world else:
world.costs[x][y] = 10
if __name__ == "__main__": return world
app = Main()
if config["APP"]["movement"] == "human": if __name__ == "__main__":
app.run() app = Main()
elif config["APP"]["movement"] == "robot": if config["APP"]["movement"] == "human":
app.run_robot() app.run()
elif config["APP"]["movement"] == "robot":
app.run_robot()

10
requirements.txt
View File

@ -1,6 +1,6 @@
pygame pygame
configparser configparser
pandas pandas
scikit-learn scikit-learn
joblib joblib
# formaFormatting: Provider - black # formaFormatting: Provider - black

396
view/renderer.py
View File

@ -1,198 +1,198 @@
import random import random
from random import randint from random import randint
import pygame import pygame
import configparser import configparser
from pygame import Color from pygame import Color
from domain.entities.cat import Cat from domain.entities.cat import Cat
from domain.entities.entity import Entity from domain.entities.entity import Entity
from domain.world import World from domain.world import World
config = configparser.ConfigParser() config = configparser.ConfigParser()
config.read("config.ini") config.read("config.ini")
class Renderer: class Renderer:
def __init__( def __init__(
self, self,
width=800, width=800,
height=800, height=800,
tiles_x=10, tiles_x=10,
tiles_y=10, tiles_y=10,
): ):
self.width = width self.width = width
self.height = height self.height = height
self.tiles_x = tiles_x self.tiles_x = tiles_x
self.tiles_y = tiles_y self.tiles_y = tiles_y
self.tile_width = self.width / self.tiles_x self.tile_width = self.width / self.tiles_x
self.tile_height = self.height / self.tiles_y self.tile_height = self.height / self.tiles_y
pygame.init() pygame.init()
pygame.display.set_caption("AI Vacuum Cleaner") pygame.display.set_caption("AI Vacuum Cleaner")
self.screen = pygame.display.set_mode((self.width, self.height)) self.screen = pygame.display.set_mode((self.width, self.height))
self.font = pygame.font.SysFont("Arial", 26, bold=True) self.font = pygame.font.SysFont("Arial", 26, bold=True)
self.sprites = { self.sprites = {
"VACUUM": pygame.transform.scale( "VACUUM": pygame.transform.scale(
pygame.image.load("media/sprites/vacuum.png"), pygame.image.load("media/sprites/vacuum.png"),
(self.tile_width, self.tile_height), (self.tile_width, self.tile_height),
), ),
"DOC_STATION": pygame.transform.scale( "DOC_STATION": pygame.transform.scale(
pygame.image.load("media/sprites/docking_station.png"), pygame.image.load("media/sprites/docking_station.png"),
(self.tile_width, self.tile_height), (self.tile_width, self.tile_height),
), ),
"WALL": pygame.transform.scale( "WALL": pygame.transform.scale(
pygame.image.load("media/sprites/wall.png"), pygame.image.load("media/sprites/wall.png"),
(self.tile_width, self.tile_height), (self.tile_width, self.tile_height),
), ),
"TILE": pygame.transform.scale( "TILE": pygame.transform.scale(
pygame.image.load("media/sprites/tile_cropped.jpeg"), pygame.image.load("media/sprites/tile_cropped.jpeg"),
(self.tile_width, self.tile_height), (self.tile_width, self.tile_height),
), ),
"PEEL": pygame.transform.scale( "PEEL": pygame.transform.scale(
pygame.image.load("media/sprites/peel.webp"), pygame.image.load("media/sprites/peel.webp"),
(self.tile_width, self.tile_height), (self.tile_width, self.tile_height),
), ),
"CAT_FRONT": pygame.transform.scale( "CAT_FRONT": pygame.transform.scale(
pygame.image.load("media/sprites/cat/standing_front.png"), pygame.image.load("media/sprites/cat/standing_front.png"),
(self.tile_width, self.tile_height), (self.tile_width, self.tile_height),
), ),
"CAT_BACK": pygame.transform.scale( "CAT_BACK": pygame.transform.scale(
pygame.image.load("media/sprites/cat/standing_back.png"), pygame.image.load("media/sprites/cat/standing_back.png"),
(self.tile_width, self.tile_height), (self.tile_width, self.tile_height),
), ),
"CAT_LEFT": pygame.transform.scale( "CAT_LEFT": pygame.transform.scale(
pygame.image.load("media/sprites/cat/standing_left.png"), pygame.image.load("media/sprites/cat/standing_left.png"),
(self.tile_width, self.tile_height), (self.tile_width, self.tile_height),
), ),
"CAT_RIGHT": pygame.transform.scale( "CAT_RIGHT": pygame.transform.scale(
pygame.image.load("media/sprites/cat/standing_right.png"), pygame.image.load("media/sprites/cat/standing_right.png"),
(self.tile_width, self.tile_height), (self.tile_width, self.tile_height),
), ),
"PLANT1": pygame.transform.scale( "PLANT1": pygame.transform.scale(
pygame.image.load("media/sprites/plants/plant1.png"), pygame.image.load("media/sprites/plants/plant1.png"),
( (
self.tile_width + self.tile_width / 4, self.tile_width + self.tile_width / 4,
self.tile_height + self.tile_height / 4, self.tile_height + self.tile_height / 4,
), ),
), ),
"PLANT2": pygame.transform.scale( "PLANT2": pygame.transform.scale(
pygame.image.load("media/sprites/plants/plant2.png"), pygame.image.load("media/sprites/plants/plant2.png"),
( (
self.tile_width + self.tile_width / 4, self.tile_width + self.tile_width / 4,
self.tile_height + self.tile_height / 4, self.tile_height + self.tile_height / 4,
), ),
), ),
"PLANT3": pygame.transform.scale( "PLANT3": pygame.transform.scale(
pygame.image.load("media/sprites/plants/plant3.png"), pygame.image.load("media/sprites/plants/plant3.png"),
( (
self.tile_width + self.tile_width / 4, self.tile_width + self.tile_width / 4,
self.tile_height + self.tile_height / 4, self.tile_height + self.tile_height / 4,
), ),
), ),
"EARRING": pygame.transform.scale( "EARRING": pygame.transform.scale(
pygame.image.load("media/sprites/earrings.webp"), pygame.image.load("media/sprites/earrings.webp"),
( (
self.tile_width + self.tile_width / 4, self.tile_width + self.tile_width / 4,
self.tile_height + self.tile_height / 4, self.tile_height + self.tile_height / 4,
), ),
), ),
} }
self.cat_direction_sprite = { self.cat_direction_sprite = {
0: self.sprites["CAT_BACK"], 0: self.sprites["CAT_BACK"],
1: self.sprites["CAT_RIGHT"], 1: self.sprites["CAT_RIGHT"],
2: self.sprites["CAT_FRONT"], 2: self.sprites["CAT_FRONT"],
3: self.sprites["CAT_LEFT"], 3: self.sprites["CAT_LEFT"],
} }
def render(self, world: World): def render(self, world: World):
self.render_floor() self.render_floor()
self.render_board() self.render_board()
for x in range(world.width): for x in range(world.width):
for y in range(world.height): for y in range(world.height):
for entity in world.dust[x][y]: for entity in world.dust[x][y]:
self.draw_entity(entity) self.draw_entity(entity)
for x in range(world.width): for x in range(world.width):
for y in range(world.height): for y in range(world.height):
for entity in world.obstacles[x][y]: for entity in world.obstacles[x][y]:
self.draw_entity(entity) self.draw_entity(entity)
self.draw_entity(world.vacuum) self.draw_entity(world.vacuum)
self.draw_entity(world.doc_station) self.draw_entity(world.doc_station)
if config.getboolean("APP", "cat"): if config.getboolean("APP", "cat"):
self.draw_entity(world.cat) self.draw_entity(world.cat)
pygame.display.update() pygame.display.update()
def line(self, x_1, y_1, x_2, y_2, color=None): def line(self, x_1, y_1, x_2, y_2, color=None):
pygame.draw.line(self.screen, color, (x_1, y_1), (x_2, y_2)) pygame.draw.line(self.screen, color, (x_1, y_1), (x_2, y_2))
def render_board(self, color=Color("black")): def render_board(self, color=Color("black")):
for i in range(1, self.tiles_x): for i in range(1, self.tiles_x):
self.line( self.line(
self.tile_width * i, 0, self.tile_width * i, self.height, color=color self.tile_width * i, 0, self.tile_width * i, self.height, color=color
) )
for i in range(1, self.tiles_y): for i in range(1, self.tiles_y):
self.line( self.line(
0, self.tile_height * i, self.width, self.tile_height * i, color=color 0, self.tile_height * i, self.width, self.tile_height * i, color=color
) )
def draw_entity(self, entity: Entity): def draw_entity(self, entity: Entity):
sprite = self.sprites.get(entity.type, None) sprite = self.sprites.get(entity.type, None)
draw_pos = (entity.x * self.tile_width, entity.y * self.tile_height) draw_pos = (entity.x * self.tile_width, entity.y * self.tile_height)
if "PEEL" in entity.type: if "PEEL" in entity.type:
draw_pos = ( draw_pos = (
(entity.x - 0.1) * self.tile_width, (entity.x - 0.1) * self.tile_width,
(entity.y - 0.25) * self.tile_height, (entity.y - 0.25) * self.tile_height,
) )
if "PLANT" in entity.type: if "PLANT" in entity.type:
draw_pos = ( draw_pos = (
(entity.x - 0.1) * self.tile_width, (entity.x - 0.1) * self.tile_width,
(entity.y - 0.25) * self.tile_height, (entity.y - 0.25) * self.tile_height,
) )
if "CAT" in entity.type and isinstance(entity, Cat): if "CAT" in entity.type and isinstance(entity, Cat):
sprite = self.cat_direction_sprite[entity.direction] sprite = self.cat_direction_sprite[entity.direction]
if "VACUUM" in entity.type: if "VACUUM" in entity.type:
# Add text displaying container filling level # Add text displaying container filling level
text_surface = self.font.render( text_surface = self.font.render(
f"Filling: {entity.container_filling}%", True, Color("black") f"Filling: {entity.container_filling}%", True, Color("black")
) )
text_pos = ( text_pos = (
draw_pos[0] + self.tile_width / 2 - text_surface.get_width() / 2, draw_pos[0] + self.tile_width / 2 - text_surface.get_width() / 2,
draw_pos[1] + self.tile_height, draw_pos[1] + self.tile_height,
) )
self.screen.blit(text_surface, text_pos) self.screen.blit(text_surface, text_pos)
sprite = self.create_vacuum_sprite(entity) sprite = self.create_vacuum_sprite(entity)
if "DOC_STATION" in entity.type: if "DOC_STATION" in entity.type:
draw_pos = ( draw_pos = (
(entity.x - 0.1) * self.tile_width, (entity.x - 0.1) * self.tile_width,
(entity.y - 0.25) * self.tile_height, (entity.y - 0.25) * self.tile_height,
) )
self.screen.blit(sprite, draw_pos) self.screen.blit(sprite, draw_pos)
def create_vacuum_sprite(self, vacuum): def create_vacuum_sprite(self, vacuum):
angles = { angles = {
(1, 0): 0, (1, 0): 0,
(-1, 0): 180, (-1, 0): 180,
(0, 1): 270, (0, 1): 270,
(0, -1): 90, (0, -1): 90,
} }
init_sprite = self.sprites.get(vacuum.type, None) init_sprite = self.sprites.get(vacuum.type, None)
return pygame.transform.rotate(init_sprite, angles[vacuum.direction]) return pygame.transform.rotate(init_sprite, angles[vacuum.direction])
def draw_sprite(self, x: int, y: int, sprite_name: str): def draw_sprite(self, x: int, y: int, sprite_name: str):
self.screen.blit( self.screen.blit(
self.sprites[sprite_name], (x * self.tile_width, y * self.tile_height) self.sprites[sprite_name], (x * self.tile_width, y * self.tile_height)
) )
def fill_grid_with_sprite(self, sprite): def fill_grid_with_sprite(self, sprite):
for tile_x in range(self.tiles_x): for tile_x in range(self.tiles_x):
for tile_y in range(self.tiles_y): for tile_y in range(self.tiles_y):
self.draw_sprite(tile_x, tile_y, sprite) self.draw_sprite(tile_x, tile_y, sprite)
def render_floor(self): def render_floor(self):
self.fill_grid_with_sprite("TILE") self.fill_grid_with_sprite("TILE")