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JetCobot📖
1.Introduction to the Jetcobot
  • 0.Unboxing
  • 1 Version Configuration Instructions
  • 2. Power adapter usage specifications
  • 3. Common Problems and Solutions
  • 4. First-time use instructions
  • 5. Get started
  • 6. Open and close the virtual desktop
2.Assembly and assembly tutorial
  • Installation Video
3.Linux operating system
  • 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
4.Jupyter Lab Basic Course
  • 1. Read the document before operation
  • 2. JupyterLab environment construction tutorial
  • 3. JupyterLab Basic Usage Tutorial
  • 4. JupyterWidgets Configuration Tutorial
  • 5. Getting Started Tutorial for JupyterWidgets Controls
5.Basic courses of main control board
  • 1. Introduction to the main control board
  • 2. Burn the system image
  • 3. Re-read the burned image
  • 4. System startup
  • 5. System and desktop introduction
  • 6. System expansion
  • 7. Network Configuration
  • 8. SSH remote login and file transfer
  • 9. VNC Remote Login
  • 10. System backup
  • 11. Increased swap space
  • 12. Use of Jtop
6.GPIO hardware control course
  • 1.GPIO library API usage
  • 2. Hardware library configuration
  • 3. Pin reading function
  • 4. Pin level output control
  • 5. Control LED
  • 6. External device serial communication
  • 7.I2C communication
7.Jetson-inference vision course
  • 1. Install Jetcam (optional)
  • 2. Install TensorFlow (optional)
  • 3. Jetson-inference environment construction (optional)
  • 4. Hello AI World
  • 5. Image Classification Reasoning
  • 6. Object Detection Reasoning
  • 7. Semantic Segmentation
8.Opencv basic 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. Picture cut
  • 8. Picture pan
  • 9. Picture mirroring
  • 10. Affine transformation
  • 11. Picture rotation
  • 12. Perspective transformation
  • 13. Grayscale processing
  • 14. Binary image
  • 15. Edge green detection
  • 16. Line segment drawing
  • 17. Rectangular circle drawing
  • 18. Text and picture drawing
9.ROS Basic Course
  • 1.ROS introduction
  • 2.ROS installation
  • 3.ROS common command tools
  • 4.ROS workspace
  • 5.ROS function package
  • 6.ROS node
  • 7.ROS topic publisher
  • 8.ROS topic subscribers
  • 9.ROS service client
  • 10.ROS service server
  • 11.ROS action client
  • 12.ROS action server
  • 13.ROS custom message reception
  • 14.ROS-launch file
  • 15.ROS-TF transformation
  • 16.ROS parameter service
  • 17.ROS-rviz use
  • 18.ROS-rqt tool usage
  • 19.Topic message recording and playback
  • 20.urdf model introduction
  • 21.gazebo introduction
  • 22.ROS distributed communication
10.ROS+OpenCV course
  • 1.QR code
  • 2.Human pose estimation
  • 3.Target Detection
  • 4.AR vision
  • 5.AR QR code
  • 6.ROS+Opencv basics
  • 7.Face recognition
  • 8.harris corner detection
  • 9.Target tracking algorithm
  • 10.Contour moment
  • 11.Polygon outline
  • 12.Discrete fourier transform algorithm
  • 13.Edge detection algorithm
  • 14.Face detection algorithm
  • 15.Optical flow detection algorithm
  • 16.Contour detection
  • 17.General contour detection
  • 18.Feature point tracking
  • 19.HLS color filtering
  • 20.Hough circle detection
  • 21.Hough linear detection
  • 22.HSV color filtering
  • 23.LK optical flow algorithm
  • 24.Detection algorithm
  • 25.Phase dependent displacement detection
  • 26.Image pyramid sampling algorithm
  • 27.RGB color filtering
  • 28.Clear background detection algorithm
  • 29.Simplified optical flow algorithm
  • 30.Simple filter
  • 31.Threshold image processing
  • 32.Watershed segmentation algorithm
11.Robotic arm control course
  • 1.Send data
  • 2.Read data
  • 3.Control robotic arm joint angle
  • 4.Control coordinates of robotic arm end
  • 5.Control robotic arm posture
  • 6.Handle control robotic arm
  • 7.Calibrate robotic arm joint
  • 8.Calibrate robotic arm gripper
12.AI visual basic ourse
  • 1.Camera driver
  • 2.Block color calibration
  • 3.Color recognition
  • 4.Face detection
  • 5.Gesture recognition
  • 6.Model training
  • 7.Garbage recognition
13.Mediapipe course
  • 1.Hand detection
  • 2.Posture detection
  • 3.Overall detection
  • 4.Face detection
  • 5.Face recognition
  • 6.Face effects
  • 7.3D object recognition
  • 8.Brush
  • 9.Finger control
  • 10.Gesture recognition
14.AI visual tracking course
  • 1.PID algorithm basics
  • 2.Color block positioning
  • 3.Color block tracking
  • 4.Face positioning
  • 5.Face tracking
  • 6.Fingertip trajectory recognition
  • 7.Mediapipe gesture control robotic arm action group
  • 8.Mediapipe arm posture control robotic arm
  • 9.Mediapipe palm arm target positioning
  • 10.Mediapipe palm recognition and tracking control robotic arm
  • 11.Apriltag tag code recognition
  • 12.Apriltag tag code tracking
  • 13.KCF object tracking
15.AI visual grabbing course
  • 1.Gesture recognition stacking blocks
  • 2.Color recognition grab blocks
  • 3.Apriltag recognition grabbing blocks
  • 4.Catch game
  • 5.Color sorting
  • 6.Color stacking
  • 7.apriltag sorting
  • 8.apriltag stacking
  • 9.Garbage sorting
  • 10.Garbage stacking
  • 11.Color interaction
16.URDF Model course
  • 1.URDF model of the robotic arm
  • 2.URDF model controls the real robotic arm
17.Kinesiology advanced course
  • 1.Movelt configuration
  • 2.MoveIt control the real machine
  • 3.Moves randomly
  • 4.Movelt kinematic design
  • 5.Movelt cartesian path
  • 6.Movelt collision detection
  • 7.Movelt scene design
18.Jetson Orin SUPER -Introduction to the Jetcobotnew
  • 1. Version Configuration Instructionsnew
  • 2. Power adapter usage specificationsnew
  • 3. Common Problems and Solutionsnew
  • 4. First-time use instructionsnew
  • 5. Get startednew
  • 6. Open and close the virtual desktopnew
19.Jetson Orin SUPER -Assembly and assembly tutorial
  • Installation Videonew
20.Jetson Orin SUPER -Linux operating systemnew
  • 1.Introduction to Linux systemnew
  • 2.Ubuntu file systemnew
  • 3.Ubuntu common commandsnew
  • 4.Ubuntu common editorsnew
  • 5.Ubuntu software operation commandsnew
  • 6.Virtual machine installationnew
  • 7.SSH remote controlnew
  • 8.VNC remote controlnew
  • 9.Transfer files remotelynew
  • 10.Driver library and communicationnew
  • 11.Static IP and hotspot modenew
  • 12.Bind device IDnew
  • 13.Capacity expansion and resource allocationnew
  • 14.Update system software sourcesnew
  • 15.Set root user passwordnew
  • 16.sudo free passwordnew
  • 17.Connect to WiFi networknew
  • 18.View system versionnew
  • 19.Customized service managementnew
  • 20.Back up system imagenew
21.Jetson Orin SUPER -Jupyter Lab Basic Course
  • 1. Read the document before operationnew
  • 2. JupyterLab environment construction tutorialnew
  • 3. JupyterLab Basic Usage Tutorialnew
  • 4. JupyterWidgets Configuration Tutorialnew
  • 5. Getting Started Tutorial for JupyterWidgets Controlsnew
22.Jetson Orin SUPER -Basic motherboard usage course
  • 1. Motherboard Introductionnew
  • 2. Restore the factory image systemnew
  • 3. System startupnew
  • 4. System and desktop introductionnew
  • 5.VNC Remote Controlnew
  • 6. System expansionnew
  • 7. Network Configurationnew
  • 8.SSH remote loginnew
  • 9. Remote file transfernew
  • 10. Chinese input methodnew
  • 11. Use of Jtop toolnew
  • 12. Swap space expansionnew
  • 13.Backup SSD systemnew
  • 14.Jetpack old system upgraded to SUPER system
23.Jetson Orin SUPER -GPIO hardware control course
  • 1.GPIO library API usagenew
  • 2.Hardware library configurationnew
  • 3.Pin reading functionnew
  • 4.Pin level output controlnew
  • 5.Control LEDnew
  • 6.External device serial communicationnew
  • 7.I2C communicationnew
24.Jetson Orin SUPER -YOLO11 development
  • 01.YOLO environment constructionnew
  • 02.CLI usenew
  • 03.Object detection
  • 04.Instance segmentationnew
  • 05.Pose estimationnew
  • 06.Image classificationnew
  • 07.Oriented object detectionnew
  • 08.Model conversionnew
  • 09.Dataset annotationnew
  • 10.Model training conversionnew
  • 11.Model predictionnew
25.Jetson Orin SUPER -OpenCV course
  • 1. Introduction to Open Source CVnew
  • 2. Image reading and displaynew
  • 3. Image writingnew
  • 4. Image qualitynew
  • 5. Pixel Operationnew
  • 6. Image zoomnew
  • 7. Picture cutnew
  • 8. Picture pannew
  • 9. Picture mirroringnew
  • 10. Affine transformationnew
  • 11. Picture rotationnew
  • 12. Perspective transformationnew
  • 13. Grayscale processingnew
  • 14. Binary imagenew
  • 15. Edge green detectionnew
  • 16. Line segment drawingnew
  • 17. Rectangular circle drawingnew
  • 18. Text and picture drawingnew
26.Jetson Orin SUPER -ROS2 basic course
  • 1. Introduction to ROS2new
  • 2.ROS2 install Humblenew
  • 3.ROS2 development environmentnew
  • 4.ROS2 workspacenew
  • 5.ROS2 function packagenew
  • 6.ROS2 nodenew
  • 7.ROS2 topic communicationnew
  • 8.ROS2 service communicationnew
  • 9.ROS2 action communicationnew
  • 10.ROS2 custom interface messagenew
  • 11.ROS2 parameter service casenew
  • 12.ROS2 meta-function packagenew
  • 13.ROS2 distributed communicationnew
  • 14.ROS2 DDSnew
  • 15.ROS2 time related APInew
  • 16.ROS2 common command toolsnew
  • 17.ROS2 rviz2 usenew
  • 18.ROS2 rqt toolboxnew
  • 19.ROS2 Launch startup file configurationnew
  • 20.ROS2 recording and playback toolnew
  • 21.ROS2 URDF modelnew
  • 22.ROS2 Gazebo simulation platformnew
  • 23.ROS2 TF2 coordinate transformationnew
27.Jetson Orin SUPER -Advanced vision course
  • 1.QR codenew
  • 2.Human pose estimationnew
  • 3.Target Detectionnew
  • 4.AR visionnew
  • 5.ROS+Opencv Basicsnew
28.Jetson Orin SUPER -Robotic Arm Control Course
  • 1. Sending Datanew
  • 2. Read datanew
  • 3. Control the joint angle of the robotic armnew
  • 4. Control the coordinates of the end of the robot armnew
  • 5. Control the posture of the robotic armnew
  • 6.Handle remote control robotic armnew
  • 7. Calibrate the robot arm jointsnew
  • 8. Calibrate the robot grippernew
29.Jetson Orin SUPER -AI Vision Basic Course
  • 1. Camera drivernew
  • 2. Color threshold adjustment and color block calibrationnew
  • 3. Color recognitionnew
  • 4. Face Detectionnew
  • 5. Gesture Recognitionnew
  • 6. Garbage Identificationnew
30.Jetson Orin SUPER -Mediapipe Development Course
  • 1. Hand detectionnew
  • 2. Posture detectionnew
  • 3. Overall detectionnew
  • 4.FaceMeshnew
  • 5. Face Detectionnew
  • 6. Face effectsnew
  • 7. 3D Object Recognitionnew
  • 8. Brushnew
  • 9. Finger controlnew
  • 10. Gesture Recognitionnew
31.Jetson Orin SUPER -AI visual tracking course
  • 1.PID algorithm basicsnew
  • 2. Color block positioning experimentnew
  • 3. Color block tracking experimentnew
  • 4. Face location experimentnew
  • 5. Face tracking experimentnew
  • 6. Fingertip trajectory recognitionnew
  • 7.Mediapipe gesture control robotic arm action groupnew
  • 8.Mediapipe arm posture control robotic armnew
  • 9. Mediapipe palm arm target positioningnew
  • 10. Mediapipe identifies and tracks your palm to control the robotic armnew
  • 11. Apriltag label code recognitionnew
  • 12. Apriltag label code trackingnew
  • 13.KCF Object Trackingnew
32.Jetson Orin SUPER -AI Visual Capture Course
  • 1. Gesture recognition stacking blocksnew
  • 2. Color recognition grab blocksnew
  • 3. Apriltag recognition and grabbing building blocksnew
  • 4.Catch gamenew
  • 5. Color sortingnew
  • 6. Color stackingnew
  • 7. Apriltag sortingnew
  • 8. Apriltag stackingnew
  • 9. Garbage sortingnew
  • 10. Garbage dumpnew
  • 11.Color interactionnew
33.Jetson Orin SUPER -Robotic Arm URDF Model Course
  • 1. Robotic arm URDF modelnew
  • 2. URDF model controls the real robot armnew
34.Jetson Orin SUPER -Moveit advanced kinematics course
  • 1.Movelt Configurationnew
  • 2. Use of MoveItnew
  • 3. MoveIt drives the real machinenew
  • 4. MoveIt Random Movenew
  • 5.Movelt positive kinematics designnew
  • 6.Movelt inverse kinematics designnew
  • 7.Movelt Cartesian pathnew
  • 8.Movelt scene designnew
  • 9.Movelt Collision Detectionnew
35.FAQnew
  • 1.Using your own Jetson board, JetCobot cannot start normally
Download
  • Instruction_Manual
  • SystemFile_for_NANO4GB
  • SystemFile_For_OrinNANO
  • SystemFile_For_OrinNX
  • VM_File
  • Code
  • 3D Model File
  • Jetson_Boot_USB.tar.gz
  • ROS2_Basic_VM
Download ZIP

Welcome to JetCobot repository

14.ROS2 DDS

14. ROS2 DDS

1. Introduction to DDS

DDS stands for Data Distribution Service. Published and maintained by the Object Management Group (OMG) in 2004, it is a data distribution/subscription standard designed specifically for real-time systems. It was first adopted by the US Navy to address compatibility issues with large-scale software upgrades within the complex network environments of ships. It has now become a mandatory standard.

DDS emphasizes data-centricity and provides a rich set of quality-of-service policies to ensure real-time, efficient, and flexible data distribution, meeting the needs of various distributed real-time communication applications.

References

  • Fast DDS Official Documentation: 3.3.0
  • ROS2 official document DDS advanced functions: Using Fast DDS Discovery Server as discovery protocol community-contributed] — ROS 2 Documentation: Humble documentation

2. Communication Model

The topics, services, and actions we learned about in the previous course all rely on DDS for their underlying communication implementation. DDS is equivalent to the neural network in the ROS robot system.

The core of DDS is communication. Numerous models and software frameworks exist to implement this communication. Here we list four commonly used models.

image-20220528020740057

  • The first, the Peer-to-Peer model, involves many clients connecting to a single server. Each communication requires a connection between the two parties. As the number of communicating nodes increases, the number of connections also increases. Furthermore, each client needs to know the server's specific address and the services it provides. If the server's address changes, all clients are affected.
  • The second, the Broker model, optimizes the peer-to-peer model. The broker centrally processes all requests and then identifies the roles that can truly respond to the service. This eliminates the need for clients to worry about the server's specific address. However, the problem is obvious: as the core, the broker's processing speed affects the efficiency of all nodes. Once the system scales to a certain level, the broker can become a performance bottleneck for the entire system. Furthermore, if the broker fails, the entire system can cease to function properly. The previous ROS1 system used a similar architecture.
  • The third model is the Broadcast model, in which all nodes can broadcast messages on a channel and receive them. This model solves the server address problem and eliminates the need for separate connections between communicating parties. However, the sheer number of messages on the broadcast channel forces all nodes to pay attention to every message, many of which are irrelevant to them.
  • The fourth model is the data-centric DDS model. This model is somewhat similar to the broadcast model, in which all nodes can publish and subscribe to messages on the DataBus. However, its advantage lies in the fact that communication involves multiple parallel pathways, allowing each node to focus only on messages of interest and ignore those it doesn't. It's a bit like a rotating hot pot, with all the delicious food being transferred across the DataBus. Each node only needs to take what it wants; the rest is irrelevant to it.

Of these communication models, DDS has a clear advantage.

3. Application of DDS in ROS2

DDS plays a crucial role in the ROS2 system. All upper-layer infrastructure is built on DDS. In this ROS2 architecture diagram, the blue and red components represent DDS.

image-20220528020937717

Among the four major components of ROS, the addition of DDS greatly enhances the comprehensive capabilities of the distributed communication system. This allows us to avoid worrying about communication issues during robot development and focus more time on other application development components.

4. Quality of Service (QoS)

The basic structure in DDS is the domain. A domain binds applications together for communication. Recall that when we configured communication between the Raspberry Pi and the computer, the domain ID we configured earlier defines the grouping of the global data space. Only nodes within the same domain group can communicate with each other. This prevents useless data from occupying resources.

Another important feature of DDS is the quality of service (QoS).

QoS is a network transmission policy. Applications specify the desired network transmission quality behavior, and the QoS service implements this behavior to meet the client's communication quality requirements as much as possible. It can be thought of as a contract between data providers and receivers.

The policies are as follows:

  • DEADLINE policy: This policy requires data to be transmitted within a certain deadline.
  • HISTORY policy: This policy specifies the cache size for historical data.
  • RELIABILITY policy: This policy specifies the data communication mode. Configuring BEST_EFFORT ensures smooth data transmission even when network conditions are poor, which may result in data loss. Configuring RELIABLE ensures reliable communication and maximizes image integrity. We can choose the appropriate communication mode based on the application scenario.
  • DURABILITY policy: This policy ensures that a certain amount of historical data is sent to late-joining nodes, allowing new nodes to quickly adapt to the system.

5. Test Cases

5.1 Case 1—Configuring DDS via the Command Line

  • Open the first terminal and publish the topic using the following command:
xxxxxxxxxx
ros2 topic pub /chatter std_msgs/msg/Int32 "data: 66" --qos-reliability best_effort

image-20250827115245194

  • Open another terminal and print the topic using a different QoS. If the QoS policy used differs from the publisher's, a warning message will appear indicating that the topic data cannot be received properly:
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ros2 topic echo /chatter --qos-reliability reliable

image-20250827115358309

  • We can receive topic data using the same QoS policy as the topic publisher.
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ros2 topic echo /chatter --qos-reliability best_effort

image-20250827120250112

5.2 Example 2—Configuring QoS Service Policies for Topic Nodes

  • Create a new function package using the following command
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ros2 pkg create learning_dds --build-type ament_python --dependencies rclpy std_msgs
  • Create a new file, dds_controller_pub.py, as the publisher for topic communication. Fill in the following content:
xxxxxxxxxx
import rclpy
from rclpy.node import Node
from std_msgs.msg import String
# Import QoS related classes
from rclpy.qos import QoSProfile, QoSReliabilityPolicy, QoSHistoryPolicy
​
class ControllerPublisher(Node):
    def __init__(self, name):
        super().__init__(name)
        #1. Configure QoS policy: reliable transmission, retain the last historical data
        self.qos_profile = QoSProfile(
            reliability=QoSReliabilityPolicy.RELIABLE,  # Reliable transmission (retransmission of lost data)
            history=QoSHistoryPolicy.KEEP_LAST,         # Keep the last N pieces of data
            depth=1                                     # Keep 1 historical data
        )
        # 2. Create a publisher: topic name /robot_cmd, message type String, QoS policy
        self.publisher = self.create_publisher(
            String, 
            "/robot_cmd", 
            self.qos_profile
        )
        # 3. Create a timer: send a command once per second
        self.timer = self.create_timer(1.0, self.timer_callback)
        self.cmd_list = ["forward", "backward", "stop"]  # Instruction List
        self.cmd_index = 0  #Instruction index, loop switching
​
​
    def timer_callback(self):
        # Cycle switching instructions (forward → backward → stop → forward...)
        current_cmd = self.cmd_list[self.cmd_index % 3]
        # Create a message and fill it with data
        msg = String()
        msg.data = current_cmd
        # Publish a message
        self.publisher.publish(msg)
        # Print log (shows issued commands)
        self.get_logger().info(f"发布控制指令:{msg.data}")
        # Update command index
        self.cmd_index += 1
​
​
def main(args=None):
    # Initializing ROS2
    rclpy.init(args=args)
    # Create a publisher node
    node = ControllerPublisher("robot_controller_pub")
    # Looping nodes
    rclpy.spin(node)
    # Destroy the node and shut down ROS2
    node.destroy_node()
    rclpy.shutdown()
​
​
if __name__ == "__main__":
    main()

 

  • To implement the subscriber code, create a new dds_robot_sub.py file and fill in the following subscriber implementation code
xxxxxxxxxx
import rclpy
from rclpy.node import Node
from std_msgs.msg import String
from rclpy.qos import QoSProfile, QoSReliabilityPolicy, QoSHistoryPolicy
​
class RobotSubscriber(Node):
    def __init__(self, name):
        super().__init__(name)
        # 1. Configure a QoS policy compatible with the publisher
        self.qos_profile = QoSProfile(
            reliability=QoSReliabilityPolicy.BEST_EFFORT,
            history=QoSHistoryPolicy.KEEP_LAST,
            depth=1
        )
        # 2. Create a subscriber: topic name/robot_cmd, callback function, QoS policy
        self.subscription = self.create_subscription(
            String,
            "/robot_cmd",
            self.cmd_callback,  # Callback function executed after receiving data
            self.qos_profile
        )
​
    def cmd_callback(self, msg):
        # Callback function: Processes received instructions
        self.get_logger().info(f"接收控制指令:{msg.data} → 执行对应动作")
​
​
def main(args=None):
    rclpy.init(args=args)
    node = RobotSubscriber("robot_subscriber")
    rclpy.spin(node)
    node.destroy_node()
    rclpy.shutdown()
​
​
if __name__ == "__main__":
    main()
  • Configure the compilation file (setup.py)
xxxxxxxxxx
entry_points={
    'console_scripts': [
        # Publisher node: command name = package name. file name: main function
        'dds_controller_pub = learning_dds.dds_controller_pub:main',
        # Subscriber Node
        'dds_robot_sub = learning_dds.dds_robot_sub:main',
    ],
},

image-20250905153513257

 

  • Open the terminal in the workspace directory and compile the function package
xxxxxxxxxx
colcon build --packages-select learning_dds

image-20250905153611086

 

  • Set environment variables. You need to set environment variables every time you recompile.
xxxxxxxxxx
source install/setup.bash
  • Running publisher and subscriber nodes
xxxxxxxxxx
ros2 run learning_dds dds_controller_pub
xxxxxxxxxx
ros2 run learning_dds dds_robot_sub

image-20250905153734043

 

Advanced Robot Development:

  • Typically, publishers and subscribers of topic communications should maintain the same QoS communication strategy to avoid potential communication layer issues.
  • QoS communication strategy settings should be selected based on the specific application scenario.
  • If the application scenario involves drone communication, encrypted communication, real-time communication, or other scenarios with special communication layer requirements, the official Fast-DDS manual is included in the tutorial introduction for a more detailed description of DDS features.

image-20250905155146210

ROS2 distributed communication
ROS2 time related API

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