粒子群算法路径规划gazebo代码

以下是使用粒子群算法进行路径规划的Gazebo仿真代码：

import rospy
from geometry_msgs.msg import Twist
from sensor_msgs.msg import LaserScan
from nav_msgs.msg import Odometry
from tf.transformations import euler_from_quaternion
import random
import math

class Particle:
    def __init__(self, x, y, yaw):
        self.x = x
        self.y = y
        self.yaw = yaw
        self.weight = 0.0

class PSOPathPlanning:
    def __init__(self):
        rospy.init_node('pso_path_planning')
        rospy.Subscriber('/scan', LaserScan, self.laser_callback)
        rospy.Subscriber('/odom', Odometry, self.odom_callback)
        self.pub = rospy.Publisher('/cmd_vel', Twist, queue_size=10)
        self.rate = rospy.Rate(10)
        self.scan = LaserScan()
        self.odom = Odometry()
        self.goal_x = 5.0
        self.goal_y = 5.0
        self.max_x = 10.0
        self.max_y = 10.0
        self.min_x = 0.0
        self.min_y = 0.0
        self.num_particles = 10
        self.num_iterations = 100
        self.particles = []
        self.global_best_particle = None
        self.global_best_fitness = float('inf')
        self.twist = Twist()
        self.twist.linear.x = 0.1
        self.twist.angular.z = 0.0
        self.current_yaw = 0.0
        self.current_x = 0.0
        self.current_y = 0.0

    def odom_callback(self, msg):
        self.odom = msg
        orientation_q = msg.pose.pose.orientation
        orientation_list = [orientation_q.x, orientation_q.y, orientation_q.z, orientation_q.w]
        (roll, pitch, yaw) = euler_from_quaternion (orientation_list)
        self.current_yaw = yaw
        self.current_x = msg.pose.pose.position.x
        self.current_y = msg.pose.pose.position.y

    def laser_callback(self, msg):
        self.scan = msg

    def euclidean_distance(self, x1, y1, x2, y2):
        return math.sqrt((x1-x2)**2 + (y1-y2)**2)

    def collision_check(self, x, y):
        index = len(self.scan.ranges)/2
        if self.scan.ranges[index] < 0.6:
            return True
        else:
            return False

    def fitness(self, x, y):
        goal_distance = self.euclidean_distance(x, y, self.goal_x, self.goal_y)
        obstacle_distance = 0.0
        for i in range(0, 360, 10):
            angle = math.radians(i)
            x1 = x + self.scan.ranges[i]*math.cos(angle)
            y1 = y + self.scan.ranges[i]*math.sin(angle)
            obstacle_distance += self.euclidean_distance(x1, y1, x, y)
        return goal_distance + obstacle_distance

    def initialize_particles(self):
        for i in range(self.num_particles):
            x = random.uniform(self.min_x, self.max_x)
            y = random.uniform(self.min_y, self.max_y)
            yaw = random.uniform(-math.pi, math.pi)
            particle = Particle(x, y, yaw)
            particle.weight = self.fitness(x, y)
            self.particles.append(particle)
            if particle.weight < self.global_best_fitness:
                self.global_best_particle = particle
                self.global_best_fitness = particle.weight

    def update_particles(self):
        for particle in self.particles:
            new_x = particle.x + random.uniform(-0.1, 0.1)
            new_y = particle.y + random.uniform(-0.1, 0.1)
            new_yaw = particle.yaw + random.uniform(-0.1, 0.1)
            if new_x < self.min_x:
                new_x = self.min_x
            elif new_x > self.max_x:
                new_x = self.max_x
            if new_y < self.min_y:
                new_y = self.min_y
            elif new_y > self.max_y:
                new_y = self.max_y
            if new_yaw < -math.pi:
                new_yaw = -math.pi
            elif new_yaw > math.pi:
                new_yaw = math.pi
            if not self.collision_check(new_x, new_y):
                new_weight = self.fitness(new_x, new_y)
                if new_weight < particle.weight:
                    particle.x = new_x
                    particle.y = new_y
                    particle.yaw = new_yaw
                    particle.weight = new_weight
                    if new_weight < self.global_best_fitness:
                        self.global_best_particle = particle
                        self.global_best_fitness = new_weight

    def run(self):
        self.initialize_particles()
        for i in range(self.num_iterations):
            self.update_particles()
            rospy.loginfo('Iteration: %d, Global Best Fitness: %f' % (i, self.global_best_fitness))
            self.twist.angular.z = 0.5*math.atan2(self.global_best_particle.y - self.current_y, self.global_best_particle.x - self.current_x) - self.current_yaw
            self.pub.publish(self.twist)
            self.rate.sleep()

if __name__ == '__main__':
    try:
        pso_path_planning = PSOPathPlanning()
        pso_path_planning.run()
    except rospy.ROSInterruptException:
        pass

该代码将机器人作为粒子，使用粒子群算法进行路径规划。机器人的运动通过发布Twist消息到/cmd_vel主题实现。在每次迭代中，机器人的目标角度被计算并设置为Twist消息的angular.z。机器人的位置和朝向通过订阅/odom主题和Odometry消息获取。机器人的激光扫描数据通过订阅/scan主题和LaserScan消息获取。