粒子群算法路径规划gazebo代码

粒子群算法（Particle Swarm Optimization, PSO）是一种基于群体智能的优化算法，常用于路径规划问题。在Gazebo中，可以使用ROS（Robot Operating System）来实现路径规划。

以下是一个使用PSO算法实现路径规划的Gazebo示例代码：

#!/usr/bin/env python

import rospy
import numpy as np
import random
import math
from geometry_msgs.msg import Twist
from nav_msgs.msg import Odometry

class Particle:
    def __init__(self):
        self.position = np.array([random.uniform(-5, 5), random.uniform(-5, 5)])
        self.velocity = np.array([random.uniform(-1, 1), random.uniform(-1, 1)])
        self.best_position = self.position
        self.best_fitness = float('inf')

    def update_velocity(self, global_best_position, w, c1, c2):
        r1 = np.random.rand()
        r2 = np.random.rand()
        cognitive = c1 * r1 * (self.best_position - self.position)
        social = c2 * r2 * (global_best_position - self.position)
        self.velocity = w * self.velocity + cognitive + social

    def update_position(self):
        self.position = self.position + self.velocity

    def evaluate_fitness(self, goal):
        error = math.sqrt((self.position[0] - goal[0])**2 + (self.position[1] - goal[1])**2)
        fitness = error
        if fitness < self.best_fitness:
            self.best_fitness = fitness
            self.best_position = self.position
        return fitness

class PSO:
    def __init__(self):
        self.goal = np.array([5, 5])
        self.particles = [Particle() for _ in range(10)]
        self.global_best_position = np.array([random.uniform(-5, 5), random.uniform(-5, 5)])
        self.global_best_fitness = float('inf')
        self.vel_msg = Twist()
        self.cmd_vel_pub = rospy.Publisher('cmd_vel', Twist, queue_size=10)
        self.odom_sub = rospy.Subscriber('odom', Odometry, self.get_odom)

    def get_odom(self, odom):
        x = odom.pose.pose.position.x
        y = odom.pose.pose.position.y
        position = np.array([x, y])
        fitness = self.evaluate_global_best(position)
        self.update_particles(fitness)
        self.update_velocity()

    def evaluate_global_best(self, position):
        fitness = math.sqrt((position[0] - self.goal[0])**2 + (position[1] - self.goal[1])**2)
        if fitness < self.global_best_fitness:
            self.global_best_fitness = fitness
            self.global_best_position = position
        return fitness

    def update_particles(self, fitness):
        for particle in self.particles:
            particle.evaluate_fitness(self.goal)
            if particle.best_fitness < fitness:
                fitness = particle.best_fitness
                self.global_best_position = particle.best_position

    def update_velocity(self):
        w = 0.5
        c1 = 0.5
        c2 = 0.5
        for particle in self.particles:
            particle.update_velocity(self.global_best_position, w, c1, c2)
            particle.update_position()
        self.move()

    def move(self):
        self.vel_msg.linear.x = 0.5
        self.vel_msg.angular.z = 0.0
        self.cmd_vel_pub.publish(self.vel_msg)

if __name__ == '__main__':
    rospy.init_node('pso_path_planning')
    pso = PSO()
    rospy.spin()

在这个示例代码中，我们定义了一个粒子类（Particle）和一个PSO类（PSO）。每个粒子都有一个位置（position）和速度（velocity），并维护着自己的最佳位置（best_position）和适应度（best_fitness）。在PSO类中，我们创建了一组粒子，并维护了全局最佳位置（global_best_position）和适应度（global_best_fitness）。在ROS节点初始化后，我们在回调函数中获取机器人当前位置，并使用PSO算法更新粒子的位置和速度。最后，我们发布机器人运动的速度控制命令。

请注意，这只是一个示例代码，您需要根据您的具体情况进行修改和调整。