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ROSMASTER M3 PRO📖
0.Configuration and Operation Guide
  • 1. Quick start handle to control the car
  • 2. Log in to the car and view the code
  • 3. Robotic Arm Calibration
  • 4. Enter the Docker (Jetson-Nano and Raspberry Pi 5 users, see here)
  • 5. Assembly video
  • 6. Modify the firmware parameters of the car control board
  • 7. Frequently Asked Questions and Precautions
  • 8. Precautions for DIY Robotic Arm
1.Chassis Control Course
  • 1.ROS control
  • 2.Handle control
  • 3.Keyboard control
  • 4.Angular velocity calibration
  • 5.Line velocity calibration
  • 6.Line patrol automatic driving
  • 7.Patrol
2.Lidar Course
  • 1.Lidar introduction and use
  • 2.Dual Lidar fusion
  • 3.Lidar obstacle avoidance
  • 4.Lidar tracking
  • 5.Lidar guard
  • 6.Gmapping-SLAM mapping
  • 7.Cartographer-SLAM mapping
  • 8.slam_toolbox mapping
  • 9.Navigation2 single-point navigation avoid
  • 10.Navigation2 multi-point navigation avoid
  • 11.Repositioning navigation
  • 12.RTAB-Map mapping
  • 13.RTAB-Map navigation
  • 14.APP mapping navigation
  • 15.Machine code handling
  • 16.Color block transport
  • 17.Navigation via Pose-Constrained Visual Relocalizationnew
3.Depth Camera Course
  • 1.Dabai_DCW2 camera introduction
  • 2.Depth pseudo-color image
  • 3.Depth camera distance measurement
  • 4.Wood block volume measurement
  • 5.Mediapipe gesture recognition
  • 6.Edge detection
  • 7.YOLOv8 object detection
  • 8.Deep learning object tracking
  • 9. Transformer Model Tracking Algorithmnew
4.Robotic Arm and 3D Space Gripping Course
  • 1.Robotic arm solution
  • 2.Robotic arm chassis linkage control
  • 3.Machine code ID sorting
  • 4.Sorting height abnormality machine code
  • 5.Tracking and gripping machine code
  • 6.Color block color sorting
  • 7.Sorting height abnormality color block
  • 8.Tracking and gripping color block
  • 9.Wood block shape sorting
  • 10.KCF tracking and gripping objects
  • 11.Medipipe gesture ID sorting machine code
  • 12.Mediapipe gesture height sorting machine code
  • 13.Desktop tracking and gripping machine code
  • 14.3D tracking machine code
  • 15.Line patrol and obstacle removal
5.Road network planning and navigationnew
  • 1. Introduction To Road Network Planning
  • 2. Build A Pose Map
  • 3. Road Network Annotation
  • 4. Planning And Navigation Between Waypoints
  • 5. Road Network Navigation In Any Pose
  • 6. Path Re-planning
6.OpenClaw Basic Coursenew
  • 1. Introduction to OpenClaw
  • 2. OpenClaw Core Concepts
  • 3. OpenClaw API-KEY Configuration
  • 4. OpenClaw Switch Models
  • 5. OpenClaw System Prompt Words
  • 6. OpenClaw Common CLI Commands
7.OpenClaw Interaction Methodnew
  • 1. OpenClaw Webchat Interaction
  • 2. OpenClaw Tui Interaction
  • 3. OpenClaw Voice Dialogue Interaction
  • 4. OpenClaw WhatsApp Interaction
8.OpenClaw Developmentnew
  • 1. OpenClaw connects to the MCP interface of the robot
  • 2. Robot CLI Command Tool
  • 3. Robot control plugin tool
  • 4. Reply bridge plugin tool
  • 5. OpenClaw multimodal vision
  • 6. OpenClaw chassis control
  • 7. OpenClaw robotic arm control
  • 8. OpenClaw skills development
  • 9. OpenClaw timed tasks
  • 10. OpenClaw multi-agent intelligent agent
  • 11. OpenClaw active interaction
  • 12. OpenClaw vector memory
9.OpenClaw Embodied intelligent applicationnew
  • 1. OpenClaw target tracking
  • 2. OpenClaw robotic arm tracking and grasping
  • 3. OpenClaw robotic arm fixed-point placement
  • 4. OpenClaw garbage sorting
  • 5. OpenClaw slam mapping and navigation
  • 6. OpenClaw navigation transfer
  • 7. OpenClaw machine code clip
  • 8. OpenClaw color line patrol
  • 9. OpenClaw visual distance measurement
10.OpenClaw scenario sandboxnew
  • 1. Map description and prop placement
  • 2. Build sand table map
  • 3. Visual relocation markers
  • 4. Map mapping markers
  • 5. OpenClaw scenario sandbox comprehensive case
11.AI Model Basics
  • 1.AI large model types and principles
  • 2.RAG retrieval enhancement and model training samples
  • 3.Embodied intelligent robot system architecture
  • 4.Embodied intelligent functions core source code
12.AI Model Development
  • 01.Register a model service provider account
  • 02. Configuring API-KEY
  • 03. Introduction to Dify
  • 04. Basic Dify Features
  • 05. AI Large Model Development - chatbot
  • 06. Deploy the RAG knowledge base
  • 07. RAG knowledge base + chatbot
  • 08. AI agent workflow
  • 09. Custom wake-up response
  • 10. Core module testing tools
13.AI Large Model (Text Version)
  • 1.Semantic understand instruction following
  • 2.Multimodal visual understand
  • 3.Multimodal visual understand+robotic arm grab
  • 4.Multimodal visual understand+SLAM navigation
  • 5.Robotic arm grab+Multimodal visual understand+SLAM navigation
  • 6.Intention estimation
14.AI Large Model(Voice Version)
  • 1.Semantic understand instruction following
  • 2.Multimodal visual understand
  • 3.Multimodal visual understand+robotic arm grab
  • 4.Multimodal visual understand+SLAM navigation
  • 5.Robotic arm grab+Multimodal visual understand+SLAM navigation
  • 6.Intention estimation
15.Mediapipe Visual Course
  • 1.Hand detection
  • 2.Posture detection
  • 3.Overall detection
  • 4.Facial Landmark Detection
  • 5.Face detection
  • 6.Face special effects
  • 7.3D object recognition
  • 8.Brush
  • 9.Finger control
  • 10.Palm target positioning
  • 11.Fingertip trajectory recognition
  • 12.Fingertip gesture control robotic arm
  • 13.Gesture grabbing and releasing objects
  • 14.Finger control robotic arm
  • 15.Medipipe gesture control robotic arm action group
16.MoveIt2 Simulation Course
  • 1.MovelT2 configuration
  • 2.MovelT2 simulation-reality linkage
  • 3.Random movement
  • 4.Forward kinematics design
  • 5.Inverse kinematics design
  • 6.Cartesian path
  • 7.Trajectory planning
  • 8.Collision detection
  • 9.Scene design
17.Multi-vehicle Course
  • 1.Multi-vehicle chassis control
  • 2.Multi-vehicle robotic arm control
  • 3.Multi-vehicle navigation
  • 4.Multi-vehicle formation
18.Control Board Course
  • 1.Development environment setup
  • 2.Control board STM32 basic routine
  • 3.Control board micro_ros basic routine
19.Docker Course
  • 1、Docker overview and docker installation
  • 2、Common commands for docker image containers
  • 3、Docker images deeply understand and publish images
  • 4、Docker hardware interaction and data processing
  • 5、Enter the bot's docker container
  • 6、Robot development environment construction in Docker
20.Image Processing Basics Course
  • 1. Introduction to Open Source CV
  • 2. Image reading and display
  • 3. Image writing
  • 4. Image quality
  • 5. Pixel Operation
  • 6. Image zoom
  • 7. Image cutting
  • 8. Image translation
  • 9. Image Mirroring
  • 10. Affine Transformation
  • 11. Image Rotation
  • 12. Perspective Transformation
  • 13. Grayscale processing
  • 14. Image Binarization
  • 15. Edge Detection
  • 16. Line drawing
  • 17. Rectangle and circle drawing
  • 18. Drawing text and pictures
21.Main Control Course
Jetson Nano B01
  • 1. Introduction to the Jetson Nano B01 SUB Board
  • 2. Burn the system image
  • 3. Re-read the memory card or USB drive that has been burned
  • 4. Startup system
  • 5. Jetson Nano B01 System and Desktop Introduction
  • 6. Memory card or USB flash drive expansion
  • 7. Network Configuration
  • 8. SSH remote login & file transfer
  • 9. VNC Remote Login
  • 10. Jetson Nano B01 system backup
  • 11. Jetson Nano Swap space increased
  • 12. Installation and use of Jtop
Jetson Orin Nano_NX
  • 1.Jetson Orin board introduction
  • 2.Restore the factory image system
  • 3.Backup SSD system
  • 4.SSD expansion
  • 5.Network configuration
  • 6.SSH remote login
  • 7.VNC remote control
  • 8.Remote file transfer
  • 9.Jtop tool
  • 10.Exchange space expansion
Raspberry Pi
  • 1. Introduction to Raspberry PI 5
  • 2. Raspberry PI system installation and backup
  • 3. Powering the Raspberry PI 5
  • 4. Startup of Raspberry PI 5
  • 5. Update and upgrade operating system
  • 6. Introduction to raspi-config tool
  • 7. config.txt file description
  • 8. Network Configuration
  • 9. remote access
  • 10. Transfer files remotely
  • 11. Set display resolution and rotation
  • 12. Set screen to sleep
  • 13. Play audio and video
  • 14. Using USB camera
  • 15. Using MIPI camera
  • 16. Get real-time temperature of Raspberry Pi
22.Linux System Course
  • 1.Introduction to Linux system
  • 2.Ubuntu file system
  • 3.Ubuntu common commands
  • 4.Ubuntu common editors
  • 5.Ubuntu software operation commands
  • 6.Virtual machine installation
  • 7.SSH remote control
  • 8.VNC remote control
  • 9.Transfer files remotely
  • 10.Driver library and communication
  • 11.Static IP and hotspot mode
  • 12.Bind device ID
  • 13.Capacity expansion and resource allocation
  • 14.Update system software sources
  • 15.Set root user password
  • 16.sudo free password
  • 17.Connect to WiFi network
  • 18.View system version
  • 19.Customized service management
  • 20.Back up system image
23.ROS2 Basic Course
  • 1.Introduction to ROS2
  • 2.ROS2 install Humble
  • 3.ROS2 development environment
  • 4.ROS2 workspace
  • 5.ROS2 function package
  • 6.ROS2 node
  • 7.ROS2 topic communication
  • 8.ROS2 service communication
  • 9.ROS2 action communication
  • 10.ROS2 custom interface message
  • 11.ROS2 parameter service case
  • 12.ROS2 meta-function package
  • 13.ROS2 distributed communication
  • 14.ROS2 DDS
  • 15.ROS2 time related API
  • 16.ROS2 common command tools
  • 17.ROS2 rviz2 use
  • 18.ROS2 rqt toolbox
  • 19.ROS2 Launch startup file configuration
  • 20.ROS2 recording and playback tool
  • 21.ROS2 URDF model
  • 22.ROS2 Gazebo simulation platform
  • 23.ROS2 TF2 coordinate transformation
Download
  • Mapping_APP(iOS_Android)
  • Robot_Image_System_File
  • Code-Firmware
  • Instruction_Manual
  • VM_File
  • Software_Tools
  • Firmware_DriverFile
  • 3D_Model_File
  • ROS2_Basic_VM
  • Other_Annex
Download ZIP

Welcome to ROSMASTER M3 PRO repository

9.ROS2 action communication

9.ROS2 action communication

 

1. Introduction to Action Communication

Action communication is a communication model with continuous feedback. Between the two communicating parties, the client sends request data to the server, and the server responds to the client. However, from the time the server receives the request to the time it generates the final response, it sends continuous feedback to the client.

The action communication client/server model is as follows:

image8

2. Example Introduction

The action client submits an integer data type N. The action server receives the request data and accumulates all integers between 1 and N, returning the final result to the action client. Each addition calculates the current computation progress and provides feedback to the action client.

3. Create a new function package

3.1. Create an action communication interface function package

  1. Action communication requires creating the action communication interface first.
  • Create a new pkg_interfaces function package in the src directory of the workspace.
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ros2 pkg create --build-type ament_cmake pkg_interfaces
  1. Next, create an action folder under the pkg_interfaces function package and create a new file called [Progress.action] within the action folder. The file contents are as follows:
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int64 num
---
int64 sum
---
float64 progress

image-20231030154108201

  1. Add some dependency packages to package.xml. The specific content is as follows:
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<buildtool_depend>rosidl_default_generators</buildtool_depend>
<exec_depend>rosidl_default_runtime</exec_depend>
<depend>action_msgs</depend>
<member_of_group>rosidl_interface_packages</member_of_group>
  1. Add the following configuration to CMakeLists.txt:
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find_package(rosidl_default_generators REQUIRED)
​
rosidl_generate_interfaces(${PROJECT_NAME}
  "action/Progress.action"
)

image-20231030154828498

  1. Compile the package:
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colcon build --packages-select pkg_interfaces

image-20250905142323197

  1. After compilation is complete, the C++ and Python files corresponding to the Progress.action file will be generated in the install directory of the workspace. You can also access the workspace in a terminal and verify the file definitions and compilation by running the following command:
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source install/setup.bash
ros2 interface show pkg_interfaces/action/Progress

Under normal circumstances, the terminal will output the same content as the Progress.action file.

image-20250820112138938

3.2. Creating the Action Communication Function Package

Create a new package called pkg_action in the src directory of the workspace.

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ros2 pkg create pkg_action --build-type ament_python --dependencies rclpy pkg_interfaces --node-name action_server_demo

After executing the above command, the pkg_action function package will be created, along with an action_server_demo node and the relevant configuration files.

image-20231030155311734

4. Server Implementation

4.1 Creating the Server

Next, edit [action_server_demo.py] to implement the server functionality and add the following code:

xxxxxxxxxx
import time
import rclpy
from rclpy.action import ActionServer
from rclpy.node import Node
​
from pkg_interfaces.action import Progress
​
​
class Action_Server(Node):
​
    def __init__(self):
        super().__init__('progress_action_server')
        # Creating an action server
        self._action_server = ActionServer(
            self,
            Progress,
            'get_sum',
            self.execute_callback)
        self.get_logger().info('动作服务已经启动!')
​
    def execute_callback(self, goal_handle):
        self.get_logger().info('开始执行任务....')
​
​
        # Generate continuous feedback;
        feedback_msg = Progress.Feedback()
​
        sum = 0
        for i in range(1, goal_handle.request.num + 1):
            sum += i
            feedback_msg.progress = i / goal_handle.request.num
            self.get_logger().info('连续反馈: %.2f' % feedback_msg.progress)
            goal_handle.publish_feedback(feedback_msg)
            time.sleep(1)
​
        # Generate the final response.
        goal_handle.succeed()
        result = Progress.Result()
        result.sum = sum
        self.get_logger().info('任务完成!')
​
        return result
​
​
def main(args=None):
​
    rclpy.init(args=args)
    # Call the spin function and pass in the node object
    Progress_action_server = Action_Server()
    rclpy.spin(Progress_action_server)
    Progress_action_server.destroy_node() 
    # Release resources
    rclpy.shutdown()

 

4.2 Edit the Configuration File

  • Open setup.py and add the following line to the console_scripts list:
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'action_server_demo = pkg_action.action_server_demo:main',

image-20231030164238858

4.3 Compile the Package

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colcon build --packages-select pkg_action

4.4 Run the Program

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ros2 run pkg_action action_server_demo

image-20231030164959631

In another terminal, enter:

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ros2 action list

image-20231030165029444

/get_sum is the action we need to call. To do so, enter the following command in the terminal:

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ros2 action send_goal /get_sum pkg_interfaces/action/Progress "{num: 10}"

Here we calculate the sum of the values ​​from 1 to 10:

image-20231030165504071

The upper part of the image above shows the server, and the lower part shows the client. You can see that the server continuously reports progress while calculating the sum from 1 to 10. Finally, the task is completed, and the client receives feedback that the sum is 55.

5. Client Implementation

5.1 Creating the Client

Create a new file, action_client_demo.py, in the same directory as action_server_demo.py.

image-20231030170243256

Next, edit action_client_demo.py to implement the server functionality and add the following code:

xxxxxxxxxx
import rclpy
from rclpy.action import ActionClient
from rclpy.node import Node
from pkg_interfaces.action import Progress
​
class Action_Client(Node):
    def __init__(self):
        super().__init__('progress_action_client')
        # Create an action client;
        self._action_client = ActionClient(self, Progress, 'get_sum')
​
    def send_goal(self, num):
        # Send a request;
        goal_msg = Progress.Goal()
        goal_msg.num = num
        self._action_client.wait_for_server()
        self._send_goal_future = self._action_client.send_goal_async(goal_msg, feedback_callback=self.feedback_callback)
        self._send_goal_future.add_done_callback(self.goal_response_callback)
​
    def goal_response_callback(self, future):
        # Process the feedback after the target sends it;
        goal_handle = future.result()
        if not goal_handle.accepted:
            self.get_logger().info('请求被拒绝')
            return
​
        self.get_logger().info('请求被接收,开始执行任务!')
​
        self._get_result_future = goal_handle.get_result_async()
        self._get_result_future.add_done_callback(self.get_result_callback)
​
    #Process the final response.
    def get_result_callback(self, future):
        result = future.result().result
        self.get_logger().info('最终计算结果:sum = %d' % result.sum)
        # 5. Release resources.
        rclpy.shutdown()
​
    # Processing continuous feedback;
    def feedback_callback(self, feedback_msg):
        feedback = (int)(feedback_msg.feedback.progress * 100)
        self.get_logger().info('当前进度: %d%%' % feedback)
​
​
def main(args=None):
    rclpy.init(args=args)
    action_client = Action_Client()
    action_client.send_goal(10)
    rclpy.spin(action_client)

 

5.2 Edit the Configuration File

  • Open setup.py and add the following line to the console_scripts list:
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'action_client_demo = pkg_action.action_client_demo:main'

image-20231030171045187

5.3 Compile the Package

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colcon build --packages-select pkg_action

image-20250905143944896

5.4 Run the Program

Execute the following command in a separate terminal:

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# Start the server node
ros2 run pkg_action action_server_demo
# Start the client node
ros2 run pkg_action action_client_demo

image-20231030171150033

The image above shows the server on the top and the client on the bottom. Here, we're calculating the sum of 1 to 10. You can see that the server continuously reports progress during the calculation. Finally, the task is completed, and the client receives feedback that the sum is 55.

ROS2 service communication
ROS2 custom interface message

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