135 lines
4.8 KiB
Python
135 lines
4.8 KiB
Python
import numpy as np
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import gym
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from gym import spaces
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class Env(gym.Env):
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"""多车-巢-机系统的区域覆盖环境"""
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def __init__(self):
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super(Env, self).__init__()
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# 环境参数
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self.H = 20 # 区域高度
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self.W = 25 # 区域宽度
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self.k = 1 # 系统数量
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# 时间系数
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self.flight_time_factor = 3 # 每张照片飞行时间
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self.comp_uav_factor = 5 # 无人机计算时间
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self.trans_time_factor = 0.3 # 传输时间
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self.car_move_time_factor = 100 # 汽车移动时间
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self.comp_bs_factor = 5 # 机巢计算时间
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# 能量参数
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self.flight_energy_factor = 0.05 # 飞行能耗
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self.comp_energy_factor = 0.05 # 计算能耗
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self.trans_energy_factor = 0.0025 # 传输能耗
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self.battery_capacity = 30 # 电池容量
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# 动作空间
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# [垂直切割数, 水平切割数, 卸载率]
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self.action_space = spaces.Box(
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low=np.array([1, 1, 0]),
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high=np.array([5, 5, 1]),
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dtype=np.float32
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)
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# 状态空间
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# [当前垂直切割数, 当前水平切割数, 当前最大完成时间]
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self.observation_space = spaces.Box(
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low=np.array([1, 1, 0]),
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high=np.array([5, 5, float('inf')]),
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dtype=np.float32
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)
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self.state = None
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self.current_step = 0
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self.max_steps = 1000
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def step(self, action):
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self.current_step += 1
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# 解析动作
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v_cuts = int(action[0]) # 垂直切割数
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h_cuts = int(action[1]) # 水平切割数
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# rho = action[2] # 卸载率
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# TODO 生成切割位置，目前是均匀切割
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v_boundaries = np.linspace(0, self.H, v_cuts + 1)
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h_boundaries = np.linspace(0, self.W, h_cuts + 1)
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# 计算每个子区域的指标
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total_time = 0
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valid_partition = True
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for i in range(len(v_boundaries) - 1):
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for j in range(len(h_boundaries) - 1):
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# 计算子区域大小
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height = v_boundaries[i+1] - v_boundaries[i]
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width = h_boundaries[j+1] - h_boundaries[j]
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area = height * width
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# 求解rho
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rho_time_limit = (self.flight_time_factor - self.trans_time_factor) / \
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(self.comp_uav_factor - self.trans_time_factor)
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rho_energy_limit = (self.battery_capacity - self.flight_energy_factor * area - self.trans_energy_factor * area) / \
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(self.comp_energy_factor * area - self.trans_energy_factor * area)
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if rho_energy_limit < 0:
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valid_partition = False
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break
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rho = min(rho_time_limit, rho_energy_limit)
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# 计算各阶段时间
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flight_time = self.flight_time_factor * area
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comp_time = self.comp_uav_factor * rho * area
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trans_time = self.trans_time_factor * (1 - rho) * area
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comp_bs_time = self.comp_bs_factor * (1 - rho) * area
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# # 计算能耗
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# flight_energy = self.flight_energy_factor * area
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# comp_energy = self.comp_energy_factor * rho * area
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# trans_energy = self.trans_energy_factor * (1 - rho) * area
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# total_energy = flight_energy + comp_energy + trans_energy
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# # 检查约束
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# if total_energy > self.battery_capacity or (comp_time + trans_time > flight_time):
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# valid_partition = False
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# break
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# 计算子区域中心到区域中心的距离
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center_y = (v_boundaries[i] + v_boundaries[i+1]) / 2
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center_x = (h_boundaries[j] + h_boundaries[j+1]) / 2
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dist_to_center = np.sqrt(
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(center_y - self.H/2)**2 + (center_x - self.W/2)**2)
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car_time = dist_to_center * self.car_move_time_factor
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# 更新总时间
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task_time = max(flight_time + car_time, comp_bs_time)
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total_time = max(total_time, task_time)
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if not valid_partition:
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break
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# 计算奖励
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if not valid_partition:
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reward = -10000 # 惩罚无效方案
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done = True
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else:
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reward = -total_time # 负的完成时间作为奖励
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done = self.current_step >= self.max_steps
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# 更新状态
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self.state = np.array([v_cuts, h_cuts, total_time])
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return self.state, reward, done, {}
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def reset(self):
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# 初始化状态
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self.state = np.array([1, 1, 0])
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self.current_step = 0
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return self.state
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def render(self, mode='human'):
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pass
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