129 lines
5.9 KiB
Python
129 lines
5.9 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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import random
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import pandas as pd
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import copy
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from aco import ACO
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from geographiclib.geodesic import Geodesic
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import math
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geod = Geodesic.WGS84
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class Env():
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def __init__(self, vehicle_num, target_num, map_size, visualized=True, time_cost=None, repeat_cost=None):
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self.vehicles_position = np.zeros(vehicle_num,dtype=np.int32) # 无人机位置
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self.vehicles_speed = np.zeros(vehicle_num,dtype=np.int32) # 无人机速度
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self.targets = np.zeros(shape=(target_num+1,4),dtype=np.int32) # 目标属性
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if vehicle_num==6:
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self.size='small'
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self.map_size = map_size
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self.speed_range = [10, 10, 10]
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#self.time_lim = 1e6
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self.time_lim = self.map_size / self.speed_range[1]
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self.vehicles_lefttime = np.ones(vehicle_num,dtype=np.float32) * self.time_lim # 剩余的可用时间
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self.distant_mat = np.zeros((target_num+1,target_num+1),dtype=np.float32)
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self.total_reward = 0
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self.reward = 0
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self.visualized = visualized
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self.time = 0
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self.time_cost = time_cost
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self.repeat_cost = repeat_cost
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self.end = False
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self.assignment = [[] for i in range(vehicle_num)] # 最终的分配路径
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self.task_generator()
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def task_generator(self):
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for i in range(self.vehicles_speed.shape[0]): # 确定每个无人机的速度
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choose = random.randint(0,2)
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self.vehicles_speed[i] = self.speed_range[choose]
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for i in range(self.targets.shape[0]-1): # 确定每个目标点的坐标位置和奖励、消耗值
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self.targets[i+1,0] = random.randint(1,self.map_size) - 0.5*self.map_size # x position
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self.targets[i+1,1] = random.randint(1,self.map_size) - 0.5*self.map_size # y position
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self.targets[i+1,2] = random.randint(1,10) # reward
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self.targets[i+1,3] = random.randint(5,10) # time consumption to finish the mission
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for i in range(self.targets.shape[0]): # 计算距离矩阵
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for j in range(self.targets.shape[0]):
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self.distant_mat[i,j] = np.linalg.norm(self.targets[i,:2]-self.targets[j,:2])
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self.targets_value = copy.deepcopy((self.targets[:,2]))
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def run(self, assignment, algorithm, play, rond):
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self.assignment = assignment
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self.algorithm = algorithm
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self.play = play
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self.rond = rond
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self.get_total_reward()
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if self.visualized:
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self.visualize()
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def reset(self):
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self.vehicles_position = np.zeros(self.vehicles_position.shape[0],dtype=np.int32)
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self.vehicles_lefttime = np.ones(self.vehicles_position.shape[0],dtype=np.float32) * self.time_lim
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self.targets[:,2] = self.targets_value
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self.total_reward = 0
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self.reward = 0
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self.end = False
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def get_total_reward(self):
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for i in range(len(self.assignment)):
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speed = self.vehicles_speed[i]
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for j in range(len(self.assignment[i])):
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position = self.targets[self.assignment[i][j],:4]
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self.total_reward = self.total_reward + position[2]
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if j == 0:
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self.vehicles_lefttime[i] = self.vehicles_lefttime[i] - np.linalg.norm(position[:2]) / speed - position[3]
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else:
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self.vehicles_lefttime[i] = self.vehicles_lefttime[i] - np.linalg.norm(position[:2]-position_last[:2]) / speed - position[3]
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position_last = position
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if self.vehicles_lefttime[i] > self.time_lim:
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self.end = True
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break
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if self.end:
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self.total_reward = 0
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break
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def visualize(self):
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if self.assignment == None:
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plt.scatter(x=0,y=0,s=200,c='k')
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plt.scatter(x=self.targets[1:,0],y=self.targets[1:,1],s=self.targets[1:,2]*10,c='r')
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plt.title('Target distribution')
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plt.savefig('task_pic/'+self.size+'/'+self.algorithm+ "-%d-%d.png" % (self.play,self.rond))
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plt.cla()
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else:
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plt.title('Task assignment by '+self.algorithm +', total reward : '+str(self.total_reward))
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plt.scatter(x=0,y=0,s=200,c='k')
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plt.scatter(x=self.targets[1:,0],y=self.targets[1:,1],s=self.targets[1:,2]*10,c='r')
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for i in range(len(self.assignment)):
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trajectory = np.array([[0,0,20]])
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for j in range(len(self.assignment[i])):
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position = self.targets[self.assignment[i][j],:3]
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trajectory = np.insert(trajectory,j+1,values=position,axis=0)
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plt.scatter(x=trajectory[1:,0],y=trajectory[1:,1],s=trajectory[1:,2]*10,c='b')
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plt.plot(trajectory[:,0], trajectory[:,1])
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plt.savefig('task_pic/'+self.size+'/'+self.algorithm+ "-%d-%d.png" % (self.play,self.rond))
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plt.cla()
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def evaluate(vehicle_num, target_num, map_size):
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if vehicle_num==6:
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size='small'
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re_aco=[[] for i in range(2)]
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for i in range(1):
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env = Env(vehicle_num,target_num,map_size,visualized=True)
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for j in range(1):
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aco = ACO(vehicle_num,target_num,env.vehicles_speed,env.targets,env.time_lim)
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path_new, time = aco.run()
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env.run(path_new,'ACO',i+1,j+1)
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re_aco[i].append((env.total_reward,time))
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env.reset()
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if __name__=='__main__':
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'''
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vehicle number: scalar
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speeds of vehicles: array
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target number: scalar
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targets: array, the first line is depot, the first column is x position, the second column is y position, the third column is reward and the forth column is time consumption to finish the mission
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time limit: scalar
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'''
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evaluate(6,30,1e3)
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