search
HomeBackend DevelopmentPython TutorialROS multiple master message exchange

ROS multiple master message exchange

Nov 02, 2016 am 11:02 AM
python

Requirements

Sometimes we need to have several different masters, and they need to exchange topic content. At this time, we cannot use the method of setting the same master that comes with ros.

Our approach is to construct a The client and a server run under different masters. The client subscribes to topic1 under master1, and then sends it to the server under master2 through the tcp protocol (defines a message protocol format by itself), performs message parsing, and then publishes topic1 under master2. , so that we can achieve the propagation of topic1 messages from master1 to master2 without changing the topic framework that comes with ros.

What we implement below is to pass an amcl_pose topic, the message type is PoseWithCovarianceStamped from master1 to master2, and other topics The code is similar to

client’s code

#! /usr/bin/env python# -*- coding=utf-8 -*-import socketimport structimport rospyimport timefrom geometry_msgs.msg import PoseWithCovarianceStamped,PoseStamped#message proto# id |  length | datadef send_msg(sock, msg ,id):
    # Prefix each message with a 4-byte id and length (network byte order)
    msg = struct.pack('>I',int(id)) + struct.pack('>I', len(msg)) + msg
    sock.sendall(msg)def odomCallback(msg):
    global odom_socket

    data = ""

    id = msg.header.seq    print "send id: ",id
    x = msg.pose.pose.position.x
    y = msg.pose.pose.position.y    #orientation
    orien_z = msg.pose.pose.orientation.z
    orien_w = msg.pose.pose.orientation.w

    data += str(x) + "," + str(y)+ "," + str(orien_z)+ "," + str(orien_w)

    send_msg(odom_socket,data,id)


odom_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
odom_socket.connect(('127.0.0.1',8000))

rospy.init_node('server_node')

rospy.Subscriber('/amcl_pose',PoseWithCovarianceStamped,odomCallback)

rospy.spin()

server’s code

#! /usr/bin/env python# -*- coding=utf-8 -*-import socketimport time,os,fcntlimport structimport rospyfrom geometry_msgs.msg import PoseWithCovarianceStamped,PoseStamped#message proto# id | length | datadef recv_msg(sock):
    try:        # Read message length and unpack it into an integer
        raw_id = recvall(sock, 4)        if not raw_id:            return None
        id = struct.unpack('>I', raw_id)[0]        print "receive id: ",id
        raw_msglen = recvall(sock, 4)        if not raw_msglen:            return None
        msglen = struct.unpack('>I', raw_msglen)[0]        # Read the message data
        return recvall(sock, msglen)    except Exception ,e:        return Nonedef recvall(sock, n):
    # Helper function to recv n bytes or return None if EOF is hit
    data = ''
    while len(data) < n:
        packet = sock.recv(n - len(data))        if not packet:            return None
        data += packet    return data##把接受的数据重新打包成ros topic发出去def msg_construct(data):

    list = data.split(&#39;,&#39;)

    pose = PoseWithCovarianceStamped()
    pose.header.stamp = rospy.Time.now()
    pose.header.frame_id = "/map"
    pose.pose.pose.position.x = float(list[0])
    pose.pose.pose.position.y = float(list[1])
    pose.pose.pose.position.z = 0
    pose.pose.pose.orientation.x = 0
    pose.pose.pose.orientation.y = 0
    pose.pose.pose.orientation.z = float(list[2])
    pose.pose.pose.orientation.w = float(list[3])
    pose.pose.covariance = [0.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.06853891945200942]    return pose#初始化socket,监听8000端口odom_socket = socket.socket(socket.AF_INET,socket.SOCK_STREAM)
odom_socket.bind((&#39;&#39;,8000))
odom_socket.listen(10)

(client,address) = odom_socket.accept()

rospy.init_node("client_node")
odom_pub = rospy.Publisher("/amcl_pose",PoseWithCovarianceStamped,queue_size=30)
r = rospy.Rate(1000)#设置noblock,否则会阻塞在接听,下面while不会一直循环,只有在有数据才进行下一次循环fcntl.fcntl(client, fcntl.F_SETFL, os.O_NONBLOCK)while not rospy.is_shutdown():
    data = recv_msg(client)    if data:
        odom_pub.publish(msg_construct(data))
    r.sleep()

Conclusion

The above code has been tested in the local area network, and it can ensure that no data is lost when sending images and laser data.


Statement
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn
How are arrays used in scientific computing with Python?How are arrays used in scientific computing with Python?Apr 25, 2025 am 12:28 AM

ArraysinPython,especiallyviaNumPy,arecrucialinscientificcomputingfortheirefficiencyandversatility.1)Theyareusedfornumericaloperations,dataanalysis,andmachinelearning.2)NumPy'simplementationinCensuresfasteroperationsthanPythonlists.3)Arraysenablequick

How do you handle different Python versions on the same system?How do you handle different Python versions on the same system?Apr 25, 2025 am 12:24 AM

You can manage different Python versions by using pyenv, venv and Anaconda. 1) Use pyenv to manage multiple Python versions: install pyenv, set global and local versions. 2) Use venv to create a virtual environment to isolate project dependencies. 3) Use Anaconda to manage Python versions in your data science project. 4) Keep the system Python for system-level tasks. Through these tools and strategies, you can effectively manage different versions of Python to ensure the smooth running of the project.

What are some advantages of using NumPy arrays over standard Python arrays?What are some advantages of using NumPy arrays over standard Python arrays?Apr 25, 2025 am 12:21 AM

NumPyarrayshaveseveraladvantagesoverstandardPythonarrays:1)TheyaremuchfasterduetoC-basedimplementation,2)Theyaremorememory-efficient,especiallywithlargedatasets,and3)Theyofferoptimized,vectorizedfunctionsformathematicalandstatisticaloperations,making

How does the homogenous nature of arrays affect performance?How does the homogenous nature of arrays affect performance?Apr 25, 2025 am 12:13 AM

The impact of homogeneity of arrays on performance is dual: 1) Homogeneity allows the compiler to optimize memory access and improve performance; 2) but limits type diversity, which may lead to inefficiency. In short, choosing the right data structure is crucial.

What are some best practices for writing executable Python scripts?What are some best practices for writing executable Python scripts?Apr 25, 2025 am 12:11 AM

TocraftexecutablePythonscripts,followthesebestpractices:1)Addashebangline(#!/usr/bin/envpython3)tomakethescriptexecutable.2)Setpermissionswithchmod xyour_script.py.3)Organizewithacleardocstringanduseifname=="__main__":formainfunctionality.4

How do NumPy arrays differ from the arrays created using the array module?How do NumPy arrays differ from the arrays created using the array module?Apr 24, 2025 pm 03:53 PM

NumPyarraysarebetterfornumericaloperationsandmulti-dimensionaldata,whilethearraymoduleissuitableforbasic,memory-efficientarrays.1)NumPyexcelsinperformanceandfunctionalityforlargedatasetsandcomplexoperations.2)Thearraymoduleismorememory-efficientandfa

How does the use of NumPy arrays compare to using the array module arrays in Python?How does the use of NumPy arrays compare to using the array module arrays in Python?Apr 24, 2025 pm 03:49 PM

NumPyarraysarebetterforheavynumericalcomputing,whilethearraymoduleismoresuitableformemory-constrainedprojectswithsimpledatatypes.1)NumPyarraysofferversatilityandperformanceforlargedatasetsandcomplexoperations.2)Thearraymoduleislightweightandmemory-ef

How does the ctypes module relate to arrays in Python?How does the ctypes module relate to arrays in Python?Apr 24, 2025 pm 03:45 PM

ctypesallowscreatingandmanipulatingC-stylearraysinPython.1)UsectypestointerfacewithClibrariesforperformance.2)CreateC-stylearraysfornumericalcomputations.3)PassarraystoCfunctionsforefficientoperations.However,becautiousofmemorymanagement,performanceo

See all articles

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Tools

PhpStorm Mac version

PhpStorm Mac version

The latest (2018.2.1) professional PHP integrated development tool

Atom editor mac version download

Atom editor mac version download

The most popular open source editor

WebStorm Mac version

WebStorm Mac version

Useful JavaScript development tools

SecLists

SecLists

SecLists is the ultimate security tester's companion. It is a collection of various types of lists that are frequently used during security assessments, all in one place. SecLists helps make security testing more efficient and productive by conveniently providing all the lists a security tester might need. List types include usernames, passwords, URLs, fuzzing payloads, sensitive data patterns, web shells, and more. The tester can simply pull this repository onto a new test machine and he will have access to every type of list he needs.

EditPlus Chinese cracked version

EditPlus Chinese cracked version

Small size, syntax highlighting, does not support code prompt function