updated agent to collect garbage
This commit is contained in:
Mateusz
parent
63596f20f2
commit
55baf24513
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@ -1,3 +0,0 @@
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# Default ignored files
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/shelf/
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/workspace.xml
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23
astar.py
23
astar.py
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@ -36,26 +36,3 @@ def astar(istate, goalx, goaly, passedFields):
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element.priority = value.priority
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return False
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# def bfs(istate, goalx, goaly, passedFields):
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# fringe = [istate]
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# explored = []
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# steps = []
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# while fringe:
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# state = fringe.pop(0)
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# if state.xpos == goalx and state.ypos == goaly:
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# steps.insert(0, state)
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# while (state.parent != None):
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# state = state.parent
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# steps.insert(0, state)
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# return steps
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# element = successors(state, passedFields)
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# explored.append((state.xpos, state.ypos, state.orientation))
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# for value in element:
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# val = (value.xpos, value.ypos, value.orientation)
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# if val not in explored and value not in fringe:
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# fringe.append(value)
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# return False
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22
collect
22
collect
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@ -24,7 +24,7 @@ edge [fontname="helvetica"] ;
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6 -> 10 ;
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11 [label="garbage_weight <= 0.612\ngini = 0.094\nsamples = 61\nvalue = [3, 58]\nclass = no-collect"] ;
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10 -> 11 ;
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12 [label="odour_intensity <= 5.682\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = collect"] ;
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12 [label="distance <= 10.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = collect"] ;
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11 -> 12 ;
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13 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = collect"] ;
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12 -> 13 ;
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@ -36,7 +36,7 @@ edge [fontname="helvetica"] ;
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15 -> 16 ;
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17 [label="garbage_weight <= 15.925\ngini = 0.26\nsamples = 13\nvalue = [2, 11]\nclass = no-collect"] ;
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15 -> 17 ;
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18 [label="fuel <= 13561.0\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = collect"] ;
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18 [label="odour_intensity <= 5.724\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = collect"] ;
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17 -> 18 ;
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19 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = collect"] ;
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18 -> 19 ;
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@ -54,11 +54,11 @@ edge [fontname="helvetica"] ;
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23 -> 25 ;
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26 [label="gini = 0.0\nsamples = 6\nvalue = [6, 0]\nclass = collect"] ;
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25 -> 26 ;
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27 [label="distance <= 7.0\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = collect"] ;
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27 [label="space_occupied <= 0.936\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = collect"] ;
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25 -> 27 ;
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28 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = collect"] ;
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28 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = no-collect"] ;
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27 -> 28 ;
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29 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = no-collect"] ;
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29 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = collect"] ;
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27 -> 29 ;
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30 [label="odour_intensity <= 7.156\ngini = 0.292\nsamples = 107\nvalue = [88, 19]\nclass = collect"] ;
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0 -> 30 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
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@ -88,14 +88,18 @@ edge [fontname="helvetica"] ;
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40 -> 42 ;
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43 [label="gini = 0.0\nsamples = 8\nvalue = [0, 8]\nclass = no-collect"] ;
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42 -> 43 ;
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44 [label="distance <= 24.0\ngini = 0.48\nsamples = 10\nvalue = [4, 6]\nclass = no-collect"] ;
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44 [label="days_since_last_collection <= 20.0\ngini = 0.48\nsamples = 10\nvalue = [4, 6]\nclass = no-collect"] ;
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42 -> 44 ;
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45 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = collect"] ;
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44 -> 45 ;
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46 [label="space_occupied <= 0.243\ngini = 0.375\nsamples = 8\nvalue = [2, 6]\nclass = no-collect"] ;
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46 [label="paid_on_time <= 0.5\ngini = 0.375\nsamples = 8\nvalue = [2, 6]\nclass = no-collect"] ;
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44 -> 46 ;
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47 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = collect"] ;
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47 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = collect"] ;
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46 -> 47 ;
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48 [label="gini = 0.0\nsamples = 6\nvalue = [0, 6]\nclass = no-collect"] ;
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48 [label="space_occupied <= 0.243\ngini = 0.245\nsamples = 7\nvalue = [1, 6]\nclass = no-collect"] ;
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46 -> 48 ;
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49 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = collect"] ;
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48 -> 49 ;
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50 [label="gini = 0.0\nsamples = 6\nvalue = [0, 6]\nclass = no-collect"] ;
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48 -> 50 ;
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}
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BIN
collect.pdf
BIN
collect.pdf
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@ -1,31 +1,38 @@
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from heuristicfn import heuristicfn
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FIELDWIDTH = 50
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TURN_FUEL_COST = 10
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MOVE_FUEL_COST = 200
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MAX_FUEL = 20000
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MAX_SPACE = 5
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MAX_WEIGHT = 200
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class GarbageTank:
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def __init__(self, volume_capacity, mass_capacity):
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self.vcapacity = volume_capacity #m^3
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self.mcapacity = mass_capacity #kg
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class Engine:
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def __init__(self, power):
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self.power = power #HP
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class GarbageTruck:
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def __init__(self, dump_location, fuel_capacity, rect, orientation):
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self.dump_location = dump_location
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self.tank = GarbageTank(15, 18000)
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self.engine = Engine(400)
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self.fuel = fuel_capacity
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garbage_types = {'bio': 0, 'electronics': 1, 'mixed': 2, 'recyclable': 3}
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def __init__(self, dump_x, dump_y, rect, orientation, request_list: list, clf):
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self.dump_x = dump_x
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self.dump_y = dump_y
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self.fuel = MAX_FUEL
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self.free_space = MAX_SPACE
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self.weight_capacity = MAX_WEIGHT
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self.rect = rect
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self.orientation = orientation
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self.houses = [] #lista domów do odwiedzenia
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self.request_list = request_list #lista domów do odwiedzenia
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self.clf = clf
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def turn_left(self):
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self.orientation = (self.orientation - 1) % 4
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self.fuel -= TURN_FUEL_COST
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def turn_right(self):
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self.orientation = (self.orientation + 1) % 4
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self.fuel -= TURN_FUEL_COST
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def forward(self):
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self.fuel -= MOVE_FUEL_COST
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if self.orientation == 0:
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self.rect.x += FIELDWIDTH
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elif self.orientation == 1:
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@ -33,4 +40,50 @@ class GarbageTruck:
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elif self.orientation == 2:
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self.rect.x -= FIELDWIDTH
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else:
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self.rect.y -= FIELDWIDTH
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self.rect.y -= FIELDWIDTH
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def next_destination(self):
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if self.fuel <= 0 or not self.request_list:
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return self.dump_x, self.dump_y
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for i in range(len(self.request_list)):
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request = self.request_list[i]
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#nie ma miejsca w zbiorniku lub za ciężkie śmieci
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if request.volume > self.free_space or request.weight > self.weight_capacity:
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continue
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#nie straczy paliwa na dojechanie i powrót na wysypisko
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if heuristicfn(request.x_pos, request.y_pos, self.dump_x, self.dump_y) / 50 * 200 > self.fuel:
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continue
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distance = heuristicfn(self.rect.x, self.rect.y, request.x_pos, request.y_pos) / 50
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r = [
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self.fuel,
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distance,
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request.volume,
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request.last_collection,
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request.is_paid,
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request.odour_intensity,
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request.weight,
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request.type
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]
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if self.clf.predict([r]) == True:
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self.request_list.pop(i)
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self.free_space -= request.volume
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self.weight_capacity -= request.weight
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return request.x_pos, request.y_pos
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return self.dump_x, self.dump_y
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def collect(self):
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if self.rect.x == self.dump_x and self.rect.y == self.dump_y:
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self.fuel = MAX_WEIGHT
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self.free_space = MAX_SPACE
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self.weight_capacity = MAX_WEIGHT
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print(f'agent at ({self.rect.x}, {self.rect.y}); fuel: {self.fuel}; free space: {self.free_space}; weight capacity: {self.weight_capacity}')
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pass
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4
home.py
4
home.py
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@ -1,4 +0,0 @@
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class Home:
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def __init__(self, coord):
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self.coord = coord
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self.collect_request = False
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@ -1,8 +0,0 @@
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class Litter:
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types = ['PAPER', 'GLASS', 'PLASTIC', 'METAL', 'BIO', 'MUNICIPAL', 'ELECTRONICS']
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def __init__(self, type, volume, mass):
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self.type = type
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self.volume = volume
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self.mass = mass
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87
main.py
87
main.py
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@ -1,9 +1,5 @@
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import pygame
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import random
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import pandas as pd
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from sklearn import tree
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from sklearn.preprocessing import LabelEncoder
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import graphviz
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from treelearn import treelearn
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from astar import astar
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@ -11,6 +7,7 @@ from state import State
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import time
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from garbage_truck import GarbageTruck
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from heuristicfn import heuristicfn
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from map import randomize_map
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pygame.init()
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WIDTH, HEIGHT = 800, 800
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@ -18,52 +15,12 @@ window = pygame.display.set_mode((WIDTH, HEIGHT))
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pygame.display.set_caption("Intelligent Garbage Collector")
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AGENT_IMG = pygame.image.load("garbage-truck-nbg.png")
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AGENT = pygame.transform.scale(AGENT_IMG, (50, 50))
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DIRT_IMG = pygame.image.load("dirt.jpg")
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DIRT = pygame.transform.scale(DIRT_IMG, (50, 50))
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GRASS_IMG = pygame.image.load("grass.png")
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GRASS = pygame.transform.scale(GRASS_IMG, (50, 50))
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SAND_IMG = pygame.image.load("sand.jpeg")
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SAND = pygame.transform.scale(SAND_IMG, (50, 50))
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COBBLE_IMG = pygame.image.load("cobble.jpeg")
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COBBLE = pygame.transform.scale(COBBLE_IMG, (50, 50))
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FPS = 10
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FIELDCOUNT = 16
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FIELDWIDTH = 50
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class Agent:
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def __init__(self, rect, direction):
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self.rect = rect
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self.direction = direction
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def randomize_map(): # tworzenie mapy z losowymi polami
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field_array_1 = []
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field_array_2 = []
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field_priority = []
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for i in range(16):
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temp_priority = []
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for j in range(16):
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if i in (0, 1) and j in (0, 1):
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field_array_2.append(GRASS)
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temp_priority.append(1)
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else:
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prob = random.uniform(0, 100)
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if 0 <= prob <= 12:
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field_array_2.append(COBBLE)
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temp_priority.append(3)
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elif 12 < prob <= 24:
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field_array_2.append(SAND)
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temp_priority.append(2)
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else:
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field_array_2.append(GRASS)
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temp_priority.append(1)
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field_array_1.append(field_array_2)
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field_array_2 = []
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field_priority.append(temp_priority)
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return field_array_1, field_priority
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GRASS_IMG = pygame.image.load("grass.png")
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GRASS = pygame.transform.scale(GRASS_IMG, (50, 50))
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def draw_window(agent, fields, flip):
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if flip:
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direction = pygame.transform.flip(AGENT, True, False)
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@ -77,35 +34,22 @@ def draw_window(agent, fields, flip):
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def main():
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train_data = pd.read_csv('./data_set.csv')
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attributes = train_data.drop('collect', axis='columns')
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e_type = LabelEncoder()
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attributes['type_num'] = e_type.fit_transform(attributes['garbage_type'])
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attr_encoded = attributes.drop(['garbage_type'], axis='columns')
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attr_names = ['fuel','distance','space_occupied','days_since_last_collection','paid_on_time','odour_intensity','garbage_weight', 'garbage_type']
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label_names = ['collect', 'no-collect']
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label = train_data['collect']
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print(attr_encoded)
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print(label)
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classifier = tree.DecisionTreeClassifier()
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classifier.fit(attr_encoded, label)
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dot_data = tree.export_graphviz(classifier, out_file=None, feature_names=attr_names, class_names=label_names)
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graph = graphviz.Source(dot_data)
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graph.render('collect')
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clf = treelearn()
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clock = pygame.time.Clock()
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run = True
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x, y = [0, 0]
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agent = GarbageTruck(0, 0, pygame.Rect(x, y, 50, 50), 0) # tworzenie pola dla agenta
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fields, priority_array = randomize_map()
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final_x, final_y = [100, 300]
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fields, priority_array, request_list = randomize_map()
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agent = GarbageTruck(0, 0, pygame.Rect(0, 0, 50, 50), 0, request_list, clf) # tworzenie pola dla agenta
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while run:
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clock.tick(FPS)
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for event in pygame.event.get():
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if event.type == pygame.QUIT:
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run = False
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# keys_pressed = pygame.key.get_pressed()
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draw_window(agent, fields, False) # false = kierunek east (domyslny), true = west
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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)
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x, y = agent.next_destination()
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if x == agent.rect.x and y == agent.rect.y:
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print('out of jobs')
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break
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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)
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for interm in steps:
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if interm.action == 'LEFT':
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agent.turn_left()
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@ -121,10 +65,11 @@ def main():
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draw_window(agent, fields, True)
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else:
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draw_window(agent, fields, False)
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time.sleep(0.5)
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time.sleep(0.3)
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agent.collect()
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fields[agent.rect.x//50][agent.rect.y//50] = GRASS
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time.sleep(0.5)
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while True:
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pass
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pygame.quit()
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@ -0,0 +1,44 @@
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import pygame, random
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from request import Request
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DIRT_IMG = pygame.image.load("dirt.jpg")
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DIRT = pygame.transform.scale(DIRT_IMG, (50, 50))
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GRASS_IMG = pygame.image.load("grass.png")
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GRASS = pygame.transform.scale(GRASS_IMG, (50, 50))
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SAND_IMG = pygame.image.load("sand.jpeg")
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SAND = pygame.transform.scale(SAND_IMG, (50, 50))
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COBBLE_IMG = pygame.image.load("cobble.jpeg")
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COBBLE = pygame.transform.scale(COBBLE_IMG, (50, 50))
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def randomize_map(): # tworzenie mapy z losowymi polami
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request_list = []
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field_array_1 = []
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field_array_2 = []
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field_priority = []
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for i in range(16):
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temp_priority = []
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for j in range(16):
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if i in (0, 1) and j in (0, 1):
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field_array_2.append(GRASS)
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temp_priority.append(1)
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else:
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prob = random.uniform(0, 100)
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if 0 <= prob <= 12:
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field_array_2.append(COBBLE)
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temp_priority.append(100)
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request_list.append(Request(
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i*50,j*50, #lokacja
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random.randint(0,3), #typ śmieci
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random.random(), #objętość śmieci
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random.randint(0,30), #ostatni odbiór
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random.randint(0,1), #czy opłacone w terminie
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random.random() * 10, #intensywność odoru
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random.random() * 50 #waga śmieci
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))
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else:
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field_array_2.append(GRASS)
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temp_priority.append(1)
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field_array_1.append(field_array_2)
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field_array_2 = []
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field_priority.append(temp_priority)
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return field_array_1, field_priority, request_list
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@ -0,0 +1,13 @@
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from dataclasses import dataclass
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@dataclass
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class Request:
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def __init__(self, x_pos, y_pos, type, volume, last_collection, is_paid, odour_intensity, weight):
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self.x_pos = x_pos
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self.y_pos = y_pos
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self.type = type
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self.volume = volume
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self.last_collection = last_collection
|
||||
self.is_paid = is_paid
|
||||
self.odour_intensity = odour_intensity
|
||||
self.weight = weight
|
||||
32
succ.py
32
succ.py
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -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
|
||||
Loading…
Reference in New Issue