updated agent to collect garbage

.idea/.gitignore

@@ -1,3 +0,0 @@
-# Default ignored files
-/shelf/
-/workspace.xml

astar.py

@@ -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
-
-

collect

@@ -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 ;
 }

garbage_truck.py

@@ -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
+    garbage_types = {'bio': 0, 'electronics': 1, 'mixed': 2, 'recyclable': 3}
-        self.tank = GarbageTank(15, 18000)
+
-        self.engine = Engine(400)
+    def __init__(self, dump_x, dump_y, rect, orientation, request_list: list, clf):
-        self.fuel = fuel_capacity
+        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:
@@ -33,4 +40,50 @@ class GarbageTruck:
         elif self.orientation == 2:
             self.rect.x -= FIELDWIDTH
         else:
-            self.rect.y -= FIELDWIDTH
+            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

home.py

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

litter.py

@@ -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

main.py

@@ -1,9 +1,5 @@
 import pygame
-import random
+from treelearn import treelearn
-import pandas as pd
-from sklearn import tree
-from sklearn.preprocessing import LabelEncoder
-import graphviz
 
 
 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
 
-
+GRASS_IMG = pygame.image.load("grass.png")
-class Agent:
+GRASS = pygame.transform.scale(GRASS_IMG, (50, 50))
-    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
-
-
 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')
+    clf = treelearn()
-    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')
     clock = pygame.time.Clock()
     run = True
-    x, y = [0, 0]
+    fields, priority_array, request_list = randomize_map()
-    agent = GarbageTruck(0, 0, pygame.Rect(x, y, 50, 50), 0)   # tworzenie pola dla agenta
+    agent = GarbageTruck(0, 0, pygame.Rect(0, 0, 50, 50), 0, request_list, clf)   # tworzenie pola dla agenta
-    fields, priority_array = randomize_map()
-    final_x, final_y = [100, 300]
     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()
 

map.py

@@ -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

request.py

@@ -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

succ.py

@@ -5,27 +5,27 @@ FIELDWIDTH, FIELDCOUNT = 50, 16
 def succ(st: State, passedPriorities, goalx, goaly):
     successors = []
 
-    if st.orientation == 'N':
+    if st.orientation == 3:
-        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, '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, 'E', 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':
+    if st.orientation == 1:
-        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, '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, 'W', 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':
+    if st.orientation == 2:
-        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, '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, 'N', 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':
+    if st.orientation == 0:
-        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, '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, 'S', 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

treelearn.py

@@ -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