多无人机覆盖路径规划代码
时间: 2023-09-14 07:02:27 浏览: 64
以下是一个基于遗传算法的多无人机覆盖路径规划的简单实现代码,仅供参考:
```python
import math
import random
# 地图大小
map_width = 100
map_height = 100
# 障碍物数量
obstacles_num = 10
# 无人机数量
drones_num = 3
# 最大迭代次数
max_iter = 100
# 每个无人机的路径
drones_path = []
# 障碍物坐标列表
obstacles = []
# 随机生成障碍物
for i in range(obstacles_num):
obstacles.append((random.randint(0, map_width), random.randint(0, map_height)))
# 遗传算法参数
chromosome_length = 50
population_size = 100
crossover_rate = 0.8
mutation_rate = 0.1
# 计算两点间距离
def distance(pos1, pos2):
return math.sqrt((pos1[0] - pos2[0]) ** 2 + (pos1[1] - pos2[1]) ** 2)
# 计算路径长度
def path_length(path):
length = 0
for i in range(len(path) - 1):
length += distance(path[i], path[i + 1])
return length
# 计算多个路径的总长度
def total_length(paths):
length = 0
for path in paths:
length += path_length(path)
return length
# 随机生成一个染色体
def random_chromosome():
chromosome = []
for i in range(chromosome_length):
chromosome.append(random.randint(0, map_width))
chromosome.append(random.randint(0, map_height))
return chromosome
# 交叉操作
def crossover(chromosome1, chromosome2):
if random.random() < crossover_rate:
pos = random.randint(0, chromosome_length * 2)
return chromosome1[:pos] + chromosome2[pos:], chromosome2[:pos] + chromosome1[pos:]
else:
return chromosome1, chromosome2
# 变异操作
def mutation(chromosome):
if random.random() < mutation_rate:
pos = random.randint(0, chromosome_length * 2 - 1)
chromosome[pos] = random.randint(0, map_width)
chromosome[pos + 1] = random.randint(0, map_height)
return chromosome
# 生成下一代种群
def next_generation(population):
next_population = []
fitness_list = [(chromosome, fitness(chromosome)) for chromosome in population]
fitness_list.sort(key=lambda x: x[1])
best_chromosome = fitness_list[0][0]
next_population.append(best_chromosome)
while len(next_population) < population_size:
chromosome1 = select(fitness_list)
chromosome2 = select(fitness_list)
chromosome1, chromosome2 = crossover(chromosome1, chromosome2)
chromosome1 = mutation(chromosome1)
chromosome2 = mutation(chromosome2)
next_population.append(chromosome1)
if len(next_population) < population_size:
next_population.append(chromosome2)
return next_population
# 计算染色体的适应度
def fitness(chromosome):
paths = []
for i in range(drones_num):
path = []
for j in range(i * chromosome_length, (i + 1) * chromosome_length, 2):
pos = (chromosome[j], chromosome[j + 1])
path.append(pos)
paths.append(path)
return total_length(paths)
# 选择操作
def select(fitness_list):
total_fitness = sum([f for c, f in fitness_list])
rand = random.uniform(0, total_fitness)
for chromosome, fitness in fitness_list:
rand -= fitness
if rand < 0:
return chromosome
# 初始化种群
population = [random_chromosome() for i in range(population_size)]
# 迭代
for iteration in range(max_iter):
population = next_generation(population)
# 更新无人机路径
drones_path = []
for i in range(drones_num):
path = []
for j in range(i * chromosome_length, (i + 1) * chromosome_length, 2):
pos = (population[0][j], population[0][j + 1])
path.append(pos)
drones_path.append(path)
# 输出最优路径
print("Best path: ", drones_path)
```
该代码使用遗传算法来寻找多个无人机的最优路径,其中包含随机生成障碍物、计算两点距离、计算路径长度、交叉、变异、选择等操作。最终输出多个无人机的最优路径。由于只是一个简单实现,可能存在许多需要改进的地方。