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updated agent to collect garbage

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This commit is contained in:
Mateusz 2023-05-26 19:08:22 +02:00
parent 63596f20f2
commit 55baf24513
19 changed files with 190 additions and 149 deletions
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3
.idea/.gitignore vendored
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@ -1,3 +0,0 @@
# Default ignored files
/shelf/
/workspace.xml

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23
astar.py
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@ -36,26 +36,3 @@ def astar(istate, goalx, goaly, passedFields):
element.priority = value.priority
return False
# def bfs(istate, goalx, goaly, passedFields):
# fringe = [istate]
# explored = []
# steps = []
# while fringe:
# state = fringe.pop(0)
# if state.xpos == goalx and state.ypos == goaly:
# steps.insert(0, state)
# while (state.parent != None):
# state = state.parent
# steps.insert(0, state)
# return steps
# element = successors(state, passedFields)
# explored.append((state.xpos, state.ypos, state.orientation))
# for value in element:
# val = (value.xpos, value.ypos, value.orientation)
# if val not in explored and value not in fringe:
# fringe.append(value)
# return False

22
collect
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@ -24,7 +24,7 @@ edge [fontname="helvetica"] ;
6 -> 10 ;
11 [label="garbage_weight <= 0.612\ngini = 0.094\nsamples = 61\nvalue = [3, 58]\nclass = no-collect"] ;
10 -> 11 ;
12 [label="odour_intensity <= 5.682\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = collect"] ;
12 [label="distance <= 10.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = collect"] ;
11 -> 12 ;
13 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = collect"] ;
12 -> 13 ;
@ -36,7 +36,7 @@ edge [fontname="helvetica"] ;
15 -> 16 ;
17 [label="garbage_weight <= 15.925\ngini = 0.26\nsamples = 13\nvalue = [2, 11]\nclass = no-collect"] ;
15 -> 17 ;
18 [label="fuel <= 13561.0\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = collect"] ;
18 [label="odour_intensity <= 5.724\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = collect"] ;
17 -> 18 ;
19 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = collect"] ;
18 -> 19 ;
@ -54,11 +54,11 @@ edge [fontname="helvetica"] ;
23 -> 25 ;
26 [label="gini = 0.0\nsamples = 6\nvalue = [6, 0]\nclass = collect"] ;
25 -> 26 ;
27 [label="distance <= 7.0\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = collect"] ;
27 [label="space_occupied <= 0.936\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = collect"] ;
25 -> 27 ;
28 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = collect"] ;
28 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = no-collect"] ;
27 -> 28 ;
29 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = no-collect"] ;
29 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = collect"] ;
27 -> 29 ;
30 [label="odour_intensity <= 7.156\ngini = 0.292\nsamples = 107\nvalue = [88, 19]\nclass = collect"] ;
0 -> 30 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
@ -88,14 +88,18 @@ edge [fontname="helvetica"] ;
40 -> 42 ;
43 [label="gini = 0.0\nsamples = 8\nvalue = [0, 8]\nclass = no-collect"] ;
42 -> 43 ;
44 [label="distance <= 24.0\ngini = 0.48\nsamples = 10\nvalue = [4, 6]\nclass = no-collect"] ;
44 [label="days_since_last_collection <= 20.0\ngini = 0.48\nsamples = 10\nvalue = [4, 6]\nclass = no-collect"] ;
42 -> 44 ;
45 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = collect"] ;
44 -> 45 ;
46 [label="space_occupied <= 0.243\ngini = 0.375\nsamples = 8\nvalue = [2, 6]\nclass = no-collect"] ;
46 [label="paid_on_time <= 0.5\ngini = 0.375\nsamples = 8\nvalue = [2, 6]\nclass = no-collect"] ;
44 -> 46 ;
47 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = collect"] ;
47 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = collect"] ;
46 -> 47 ;
48 [label="gini = 0.0\nsamples = 6\nvalue = [0, 6]\nclass = no-collect"] ;
48 [label="space_occupied <= 0.243\ngini = 0.245\nsamples = 7\nvalue = [1, 6]\nclass = no-collect"] ;
46 -> 48 ;
49 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = collect"] ;
48 -> 49 ;
50 [label="gini = 0.0\nsamples = 6\nvalue = [0, 6]\nclass = no-collect"] ;
48 -> 50 ;
}

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collect.pdf
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81
garbage_truck.py
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@ -1,31 +1,38 @@
from heuristicfn import heuristicfn
FIELDWIDTH = 50
TURN_FUEL_COST = 10
MOVE_FUEL_COST = 200
MAX_FUEL = 20000
MAX_SPACE = 5
MAX_WEIGHT = 200
class GarbageTank:
def __init__(self, volume_capacity, mass_capacity):
self.vcapacity = volume_capacity #m^3
self.mcapacity = mass_capacity #kg
class Engine:
def __init__(self, power):
self.power = power #HP
class GarbageTruck:
def __init__(self, dump_location, fuel_capacity, rect, orientation):
self.dump_location = dump_location
self.tank = GarbageTank(15, 18000)
self.engine = Engine(400)
self.fuel = fuel_capacity
garbage_types = {'bio': 0, 'electronics': 1, 'mixed': 2, 'recyclable': 3}
def __init__(self, dump_x, dump_y, rect, orientation, request_list: list, clf):
self.dump_x = dump_x
self.dump_y = dump_y
self.fuel = MAX_FUEL
self.free_space = MAX_SPACE
self.weight_capacity = MAX_WEIGHT
self.rect = rect
self.orientation = orientation
self.houses = [] #lista domów do odwiedzenia
self.request_list = request_list #lista domów do odwiedzenia
self.clf = clf
def turn_left(self):
self.orientation = (self.orientation - 1) % 4
self.fuel -= TURN_FUEL_COST
def turn_right(self):
self.orientation = (self.orientation + 1) % 4
self.fuel -= TURN_FUEL_COST
def forward(self):
self.fuel -= MOVE_FUEL_COST
if self.orientation == 0:
self.rect.x += FIELDWIDTH
elif self.orientation == 1:
@ -34,3 +41,49 @@ class GarbageTruck:
self.rect.x -= FIELDWIDTH
else:
self.rect.y -= FIELDWIDTH
def next_destination(self):
if self.fuel <= 0 or not self.request_list:
return self.dump_x, self.dump_y
for i in range(len(self.request_list)):
request = self.request_list[i]
#nie ma miejsca w zbiorniku lub za ciężkie śmieci
if request.volume > self.free_space or request.weight > self.weight_capacity:
continue
#nie straczy paliwa na dojechanie i powrót na wysypisko
if heuristicfn(request.x_pos, request.y_pos, self.dump_x, self.dump_y) / 50 * 200 > self.fuel:
continue
distance = heuristicfn(self.rect.x, self.rect.y, request.x_pos, request.y_pos) / 50
r = [
self.fuel,
distance,
request.volume,
request.last_collection,
request.is_paid,
request.odour_intensity,
request.weight,
request.type
]
if self.clf.predict([r]) == True:
self.request_list.pop(i)
self.free_space -= request.volume
self.weight_capacity -= request.weight
return request.x_pos, request.y_pos
return self.dump_x, self.dump_y
def collect(self):
if self.rect.x == self.dump_x and self.rect.y == self.dump_y:
self.fuel = MAX_WEIGHT
self.free_space = MAX_SPACE
self.weight_capacity = MAX_WEIGHT
print(f'agent at ({self.rect.x}, {self.rect.y}); fuel: {self.fuel}; free space: {self.free_space}; weight capacity: {self.weight_capacity}')
pass

4
home.py
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@ -1,4 +0,0 @@
class Home:
def __init__(self, coord):
self.coord = coord
self.collect_request = False

8
litter.py
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@ -1,8 +0,0 @@
class Litter:
types = ['PAPER', 'GLASS', 'PLASTIC', 'METAL', 'BIO', 'MUNICIPAL', 'ELECTRONICS']
def __init__(self, type, volume, mass):
self.type = type
self.volume = volume
self.mass = mass

87
main.py
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@ -1,9 +1,5 @@
import pygame
import random
import pandas as pd
from sklearn import tree
from sklearn.preprocessing import LabelEncoder
import graphviz
from treelearn import treelearn
from astar import astar
@ -11,6 +7,7 @@ from state import State
import time
from garbage_truck import GarbageTruck
from heuristicfn import heuristicfn
from map import randomize_map
pygame.init()
WIDTH, HEIGHT = 800, 800
@ -18,52 +15,12 @@ window = pygame.display.set_mode((WIDTH, HEIGHT))
pygame.display.set_caption("Intelligent Garbage Collector")
AGENT_IMG = pygame.image.load("garbage-truck-nbg.png")
AGENT = pygame.transform.scale(AGENT_IMG, (50, 50))
DIRT_IMG = pygame.image.load("dirt.jpg")
DIRT = pygame.transform.scale(DIRT_IMG, (50, 50))
GRASS_IMG = pygame.image.load("grass.png")
GRASS = pygame.transform.scale(GRASS_IMG, (50, 50))
SAND_IMG = pygame.image.load("sand.jpeg")
SAND = pygame.transform.scale(SAND_IMG, (50, 50))
COBBLE_IMG = pygame.image.load("cobble.jpeg")
COBBLE = pygame.transform.scale(COBBLE_IMG, (50, 50))
FPS = 10
FIELDCOUNT = 16
FIELDWIDTH = 50
class Agent:
def __init__(self, rect, direction):
self.rect = rect
self.direction = direction
def randomize_map(): # tworzenie mapy z losowymi polami
field_array_1 = []
field_array_2 = []
field_priority = []
for i in range(16):
temp_priority = []
for j in range(16):
if i in (0, 1) and j in (0, 1):
field_array_2.append(GRASS)
temp_priority.append(1)
else:
prob = random.uniform(0, 100)
if 0 <= prob <= 12:
field_array_2.append(COBBLE)
temp_priority.append(3)
elif 12 < prob <= 24:
field_array_2.append(SAND)
temp_priority.append(2)
else:
field_array_2.append(GRASS)
temp_priority.append(1)
field_array_1.append(field_array_2)
field_array_2 = []
field_priority.append(temp_priority)
return field_array_1, field_priority
GRASS_IMG = pygame.image.load("grass.png")
GRASS = pygame.transform.scale(GRASS_IMG, (50, 50))
def draw_window(agent, fields, flip):
if flip:
direction = pygame.transform.flip(AGENT, True, False)
@ -77,35 +34,22 @@ def draw_window(agent, fields, flip):
def main():
train_data = pd.read_csv('./data_set.csv')
attributes = train_data.drop('collect', axis='columns')
e_type = LabelEncoder()
attributes['type_num'] = e_type.fit_transform(attributes['garbage_type'])
attr_encoded = attributes.drop(['garbage_type'], axis='columns')
attr_names = ['fuel','distance','space_occupied','days_since_last_collection','paid_on_time','odour_intensity','garbage_weight', 'garbage_type']
label_names = ['collect', 'no-collect']
label = train_data['collect']
print(attr_encoded)
print(label)
classifier = tree.DecisionTreeClassifier()
classifier.fit(attr_encoded, label)
dot_data = tree.export_graphviz(classifier, out_file=None, feature_names=attr_names, class_names=label_names)
graph = graphviz.Source(dot_data)
graph.render('collect')
clf = treelearn()
clock = pygame.time.Clock()
run = True
x, y = [0, 0]
agent = GarbageTruck(0, 0, pygame.Rect(x, y, 50, 50), 0) # tworzenie pola dla agenta
fields, priority_array = randomize_map()
final_x, final_y = [100, 300]
fields, priority_array, request_list = randomize_map()
agent = GarbageTruck(0, 0, pygame.Rect(0, 0, 50, 50), 0, request_list, clf) # tworzenie pola dla agenta
while run:
clock.tick(FPS)
for event in pygame.event.get():
if event.type == pygame.QUIT:
run = False
# keys_pressed = pygame.key.get_pressed()
draw_window(agent, fields, False) # false = kierunek east (domyslny), true = west
steps = astar(State(None, None, x, y, 'E', priority_array[0][0], heuristicfn(x, y, final_x, final_y)), final_x, final_y, priority_array)
x, y = agent.next_destination()
if x == agent.rect.x and y == agent.rect.y:
print('out of jobs')
break
steps = astar(State(None, None, agent.rect.x, agent.rect.y, agent.orientation, priority_array[0][0], heuristicfn(agent.rect.x, agent.rect.y, x, y)), x, y, priority_array)
for interm in steps:
if interm.action == 'LEFT':
agent.turn_left()
@ -121,10 +65,11 @@ def main():
draw_window(agent, fields, True)
else:
draw_window(agent, fields, False)
time.sleep(0.5)
time.sleep(0.3)
agent.collect()
fields[agent.rect.x//50][agent.rect.y//50] = GRASS
time.sleep(0.5)
while True:
pass
pygame.quit()

44
map.py Normal file
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@ -0,0 +1,44 @@
import pygame, random
from request import Request
DIRT_IMG = pygame.image.load("dirt.jpg")
DIRT = pygame.transform.scale(DIRT_IMG, (50, 50))
GRASS_IMG = pygame.image.load("grass.png")
GRASS = pygame.transform.scale(GRASS_IMG, (50, 50))
SAND_IMG = pygame.image.load("sand.jpeg")
SAND = pygame.transform.scale(SAND_IMG, (50, 50))
COBBLE_IMG = pygame.image.load("cobble.jpeg")
COBBLE = pygame.transform.scale(COBBLE_IMG, (50, 50))
def randomize_map(): # tworzenie mapy z losowymi polami
request_list = []
field_array_1 = []
field_array_2 = []
field_priority = []
for i in range(16):
temp_priority = []
for j in range(16):
if i in (0, 1) and j in (0, 1):
field_array_2.append(GRASS)
temp_priority.append(1)
else:
prob = random.uniform(0, 100)
if 0 <= prob <= 12:
field_array_2.append(COBBLE)
temp_priority.append(100)
request_list.append(Request(
i*50,j*50, #lokacja
random.randint(0,3), #typ śmieci
random.random(), #objętość śmieci
random.randint(0,30), #ostatni odbiór
random.randint(0,1), #czy opłacone w terminie
random.random() * 10, #intensywność odoru
random.random() * 50 #waga śmieci
))
else:
field_array_2.append(GRASS)
temp_priority.append(1)
field_array_1.append(field_array_2)
field_array_2 = []
field_priority.append(temp_priority)
return field_array_1, field_priority, request_list

13
request.py Normal file
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@ -0,0 +1,13 @@
from dataclasses import dataclass
@dataclass
class Request:
def __init__(self, x_pos, y_pos, type, volume, last_collection, is_paid, odour_intensity, weight):
self.x_pos = x_pos
self.y_pos = y_pos
self.type = type
self.volume = volume
self.last_collection = last_collection
self.is_paid = is_paid
self.odour_intensity = odour_intensity
self.weight = weight

32
succ.py
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@ -5,27 +5,27 @@ FIELDWIDTH, FIELDCOUNT = 50, 16
def succ(st: State, passedPriorities, goalx, goaly):
successors = []
if st.orientation == 'N':
successors.append(State(st, 'LEFT', st.xpos, st.ypos, 'W', passedPriorities[st.xpos//50][st.ypos//50], heuristicfn(st.xpos, st.ypos, goalx, goaly)))
successors.append(State(st, 'RIGHT', st.xpos, st.ypos, 'E', passedPriorities[st.xpos//50][st.ypos//50], heuristicfn(st.xpos, st.ypos, goalx, goaly)))
if st.orientation == 3:
successors.append(State(st, 'LEFT', st.xpos, st.ypos, 2, passedPriorities[st.xpos//50][st.ypos//50], heuristicfn(st.xpos, st.ypos, goalx, goaly)))
successors.append(State(st, 'RIGHT', st.xpos, st.ypos, 0, passedPriorities[st.xpos//50][st.ypos//50], heuristicfn(st.xpos, st.ypos, goalx, goaly)))
if st.ypos > 0:
successors.append(State(st, 'FORWARD', st.xpos, st.ypos - FIELDWIDTH , 'N', passedPriorities[st.xpos//50][st.ypos//50], heuristicfn(st.xpos, st.ypos, goalx, goaly)))
successors.append(State(st, 'FORWARD', st.xpos, st.ypos - FIELDWIDTH , 3, passedPriorities[st.xpos//50][st.ypos//50 - 1], heuristicfn(st.xpos, st.ypos - 50, goalx, goaly)))
if st.orientation == 'S':
successors.append(State(st, 'LEFT', st.xpos, st.ypos, 'E', passedPriorities[st.xpos//50][st.ypos//50], heuristicfn(st.xpos, st.ypos, goalx, goaly)))
successors.append(State(st,'RIGHT', st.xpos, st.ypos, 'W', passedPriorities[st.xpos//50][st.ypos//50], heuristicfn(st.xpos, st.ypos, goalx, goaly)))
if st.orientation == 1:
successors.append(State(st, 'LEFT', st.xpos, st.ypos, 0, passedPriorities[st.xpos//50][st.ypos//50], heuristicfn(st.xpos, st.ypos, goalx, goaly)))
successors.append(State(st,'RIGHT', st.xpos, st.ypos, 2, passedPriorities[st.xpos//50][st.ypos//50], heuristicfn(st.xpos, st.ypos, goalx, goaly)))
if st.ypos < FIELDWIDTH * (FIELDCOUNT - 1):
successors.append(State(st, 'FORWARD', st.xpos, st.ypos + FIELDWIDTH , 'S', passedPriorities[st.xpos//50][st.ypos//50], heuristicfn(st.xpos, st.ypos, goalx, goaly)))
successors.append(State(st, 'FORWARD', st.xpos, st.ypos + FIELDWIDTH , 1, passedPriorities[st.xpos//50][st.ypos//50 + 1], heuristicfn(st.xpos, st.ypos + 50, goalx, goaly)))
if st.orientation == 'W':
successors.append(State(st, 'LEFT', st.xpos, st.ypos, 'S', passedPriorities[st.xpos//50][st.ypos//50], heuristicfn(st.xpos, st.ypos, goalx, goaly)))
successors.append(State(st,'RIGHT', st.xpos, st.ypos, 'N', passedPriorities[st.xpos//50][st.ypos//50], heuristicfn(st.xpos, st.ypos, goalx, goaly)))
if st.orientation == 2:
successors.append(State(st, 'LEFT', st.xpos, st.ypos, 1, passedPriorities[st.xpos//50][st.ypos//50], heuristicfn(st.xpos, st.ypos, goalx, goaly)))
successors.append(State(st,'RIGHT', st.xpos, st.ypos, 3, passedPriorities[st.xpos//50][st.ypos//50], heuristicfn(st.xpos, st.ypos, goalx, goaly)))
if st.xpos > 0:
successors.append(State(st, 'FORWARD', st.xpos - FIELDWIDTH , st.ypos, 'W', passedPriorities[st.xpos//50][st.ypos//50], heuristicfn(st.xpos, st.ypos, goalx, goaly)))
successors.append(State(st, 'FORWARD', st.xpos - FIELDWIDTH , st.ypos, 2, passedPriorities[st.xpos//50 - 1][st.ypos//50], heuristicfn(st.xpos - 50, st.ypos, goalx, goaly)))
if st.orientation == 'E':
successors.append(State(st, 'LEFT', st.xpos, st.ypos, 'N', passedPriorities[st.xpos//50][st.ypos//50], heuristicfn(st.xpos, st.ypos, goalx, goaly)))
successors.append(State(st, 'RIGHT', st.xpos, st.ypos, 'S', passedPriorities[st.xpos//50][st.ypos//50], heuristicfn(st.xpos, st.ypos, goalx, goaly)))
if st.orientation == 0:
successors.append(State(st, 'LEFT', st.xpos, st.ypos, 3, passedPriorities[st.xpos//50][st.ypos//50], heuristicfn(st.xpos, st.ypos, goalx, goaly)))
successors.append(State(st, 'RIGHT', st.xpos, st.ypos, 1, passedPriorities[st.xpos//50][st.ypos//50], heuristicfn(st.xpos, st.ypos, goalx, goaly)))
if st.xpos < FIELDWIDTH * (FIELDCOUNT - 1):
successors.append(State(st, 'FORWARD', st.xpos + FIELDWIDTH , st.ypos, 'E', passedPriorities[st.xpos//50][st.ypos//50], heuristicfn(st.xpos, st.ypos, goalx, goaly)))
successors.append(State(st, 'FORWARD', st.xpos + FIELDWIDTH , st.ypos, 0, passedPriorities[st.xpos//50 + 1][st.ypos//50], heuristicfn(st.xpos + 50, st.ypos, goalx, goaly)))
return successors

20
treelearn.py Normal file
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@ -0,0 +1,20 @@
import pandas as pd
from sklearn import tree
from sklearn.preprocessing import LabelEncoder
import graphviz
def treelearn():
train_data = pd.read_csv('./data_set.csv')
attributes = train_data.drop('collect', axis='columns')
e_type = LabelEncoder()
attributes['type_num'] = e_type.fit_transform(attributes['garbage_type'])
attr_encoded = attributes.drop(['garbage_type'], axis='columns')
attr_names = ['fuel','distance','space_occupied','days_since_last_collection','paid_on_time','odour_intensity','garbage_weight', 'garbage_type']
label_names = ['collect', 'no-collect']
label = train_data['collect']
classifier = tree.DecisionTreeClassifier()
classifier.fit(attr_encoded.values, label)
dot_data = tree.export_graphviz(classifier, out_file=None, feature_names=attr_names, class_names=label_names)
graph = graphviz.Source(dot_data)
graph.render('collect')
return classifier