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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

15.ROS-TF transformation

15.ROS-TF transformation

TF transformation is coordinate transformation, including transformation of position and attitude. It uses a tree data structure to buffer and maintain coordinate transformation relationships between multiple coordinate systems based on time, which can help developers complete coordinate transformations such as points and vectors at any time and between coordinate systems. This course explains what are the commonly used TF tools in the ros system and how to broadcast/monitor transformations.

15.1 TF common tools

When introducing the TF tool, first understand two important concepts: source_frame and target_frame, also called parent_frame and child_frame. They each represent a coordinate system. When we broadcast or monitor the TF transformation, we are actually broadcasting and monitoring the relative pose transformation of the two.

15.1.1 tf_monitor

Used to print the release status of all coordinate systems in the TF tree, terminal input,

rosrun tf tf_monitor

After running, all the publishing status will be printed out in the running terminal. Of course, the publishing status of the specified coordinates can also be printed. You need to add the name of the specified coordinate system at the end and enter it in the terminal.

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rosrun tf tf_monitor source_frame target_frame

15.1.2 tf_echo

Used to view the transformation relationship between specified coordinate systems, terminal input,

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rosrun tf tf_echo source_frame target_frame

15.1.3、static_transform_publisher

Used to publish static coordinate transformations between two coordinate systems that do not undergo relative position changes. This tool needs to set the offset parameters and rotation parameters of the coordinates, and the publishing frequency is in ms. Terminal input,

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#Euler angle format display
#Euler angle yaw (yaw angle rotating around the z-axis) pitch (pitch angle rotating around the y-axis) roll (roll angle rotating around the x-axis)
rosrun tf static_transform_publisher x y x yaw pitch roll frame_id child_frame_id period_in_ms
#Quaternion format display
static_transform_publisher x y z  qx qy qz qw frame_id child_frame_id period_in_ms

15.1.4 rqt_tf_tree

This is a real-time tool that observes the coordinate system tree published on ros and uses the refresh button to update the contents of the tree. Terminal input,

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rosrun rqt_tf_tree rqt_tf_tree

15.2. TF transformation broadcast and monitoring programming implementation (C++)

Steps to create tf broadcaster:

  • Define tf broadcaster (TransformBroadcaster);
  • Initialize tf data and create coordinate transformation values;
  • Publish coordinate transformation (sendTransform);

Steps to create tf listener:

  • Define TF listener (TransformListener);
  • Look up coordinate transformation (waitForTransform, lookupTransform)

First create a function package, the command is learn_tf, enter in the terminal,

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cd ~/ros_ws/src
catkin_create_pkg learn_tf rospy roscpp turtlesim tf

After creation, return to the workspace and compile.

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cd ~/ros_ws
catkin_make

15.2.1. Create TF broadcaster

In the src folder of the function package learn_tf, create a c++ file (the file suffix is .cpp), name it turtle_tf_broadcaster.cpp, and copy the following code into it,

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#include <ros/ros.h>
#include <tf/transform_broadcaster.h>
#include <turtlesim/Pose.h>
std::string turtle_name;
void poseCallback(const turtlesim::PoseConstPtr& msg)
{
    static tf::TransformBroadcaster br;// Create a tf broadcaster
    // Initialize tf data
    tf::Transform transform;
    transform.setOrigin( tf::Vector3(msg->x, msg->y, 0.0) );//Set xyz coordinates
    tf::Quaternion q;
    q.setRPY(0, 0, msg->theta);//Set Euler angles: Rotate with x-axis, y-axis, z-axis
    transform.setRotation(q);
    br.sendTransform(tf::StampedTransform(transform, ros::Time::now(), "world",turtle_name));// Broadcast tf data between world and turtle coordinate systems
}
int main(int argc, char** argv)
{
    ros::init(argc, argv, "turtle_world_tf_broadcaster");// Initialize ROS node
    if (argc != 2)
    {
    ROS_ERROR("Missing a parameter as the name of the turtle!");
    return -1;
    }
    turtle_name = argv[1];// Enter the parameter as the name of the turtle
    // Subscribe to turtle position topics/pose
    ros::NodeHandle node;
    ros::Subscriber sub = node.subscribe(turtle_name+"/pose", 10,&poseCallback);
    // Loop waiting for callback function
    ros::spin();
    return 0;
};

Modify the CMakeLists.txt file and add the following content,

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add_executable(turtle_tf_broadcaster src/turtle_tf_broadcaster.cpp)
target_link_libraries(turtle_tf_broadcaster ${catkin_LIBRARIES})

Return to the workspace to compile,

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cd ~/ros_ws
catkin_make

Program flow chart,

image-20231025202728671

The code is analyzed as follows,

First, subscribe to the /pose topic of the little turtle. If the topic is published, then enter the callback function. In the callback function, first create the tf broadcaster, and then initialize the tf data. The value of the data is the data passed by the subscription/pose topic. Finally, the little turtle's transformation of the world coordinates is published through br.sendTransform. Here is the sendTransform function. There are 4 parameters. The first parameter represents the coordinate transformation of the tf::Transform type (that is, the previously initialized tf data).The second parameter is the timestamp, and the third and fourth are the source and target coordinate systems of the transformation.

15.2.2 Create TF listener

In the src folder of the function package learn_tf, create a c++ file (the file suffix is .cpp), name it turtle_tf_listener.cpp, and copy the following code into it,

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/**
* This routine monitors tf data, calculates and issues turtle2 speed instructions
turtle2->turtle1 = world->turtle*world->turtle2
*/
#include <ros/ros.h>
#include <tf/transform_listener.h>
#include <geometry_msgs/Twist.h>
#include <turtlesim/Spawn.h>
int main(int argc, char** argv)
{
    ros::init(argc, argv, "turtle1_turtle2_listener");// Initialize ROS node
    ros::NodeHandle node; // Create node handle
    // Request service produces turtle2
    ros::service::waitForService("/spawn");
    ros::ServiceClient add_turtle = node.serviceClient<turtlesim::Spawn>("/spawn");
    turtlesim::Spawn srv;
    add_turtle.call(srv);
    // Create a publisher that publishes turtle2 speed control instructions
    ros::Publisher vel = node.advertise<geometry_msgs::Twist>
    ("/turtle2/cmd_vel", 10);
    tf::TransformListener listener;// Create a tf listener
    ros::Rate rate(10.0);
    while (node.ok())
    {
        // Get the tf data between turtle1 and turtle2 coordinate systems
        tf::StampedTransform transform;
        try
        {
            listener.waitForTransform("/turtle2", "/turtle1", ros::Time(0),ros::Duration(3.0));
            listener.lookupTransform("/turtle2", "/turtle1", ros::Time(0),transform);
        }
         catch (tf::TransformException &ex)
        {
            ROS_ERROR("%s",ex.what());
            ros::Duration(1.0).sleep();
            continue;
        }
        // According to the positional relationship between turtle1 and turtle2 coordinate systems, the angular velocity and linear velocity are obtained through mathematical calculation formulas, and the speed control instructions of turtle2 are issued.
        geometry_msgs::Twist turtle2_vel_msg;
        turtle2_vel_msg.angular.z = 6.0 * atan2(transform.getOrigin().y(),transform.getOrigin().x());
        turtle2_vel_msg.linear.x = 0.8 * sqrt(pow(transform.getOrigin().x(), 2)+pow(transform.getOrigin().y(), 2));
        vel.publish(turtle2_vel_msg);
        rate.sleep();
    }
    return 0;
};

Modify the CMakeLists.txt file and add the following content,

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add_executable(turtle_tf_listener src/turtle_tf_listener.cpp)
target_link_libraries(turtle_tf_listener ${catkin_LIBRARIES})

Return to the workspace to compile,

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cd ~/ros_ws
catkin_make

Program flow chart,

image-20231025204311768

The code is analyzed as follows,

First, another little turtle turtle2 is generated through service calls, and then a turtle2 speed control publisher is created; then a listener is created to monitor and find the left transformation of turtle1 and tuetle2. This involves two functions, waitForTransform and lookupTransform.

waitForTransform(target_frame,source_frame,time,timeout): The two frames represent the target coordinate system and the source coordinate system respectively.Time represents the time to wait for the transformation between the two coordinate systems. Because the coordinate transformation is a blocking program, timeout needs to be set to represent the timeout time.

lookupTransform(target_frame,source_frame,time,transform): Given the source coordinate system (source_frame) and the target coordinate system (target_frame), obtain the coordinate transformation (transform) at the specified time (time) between the two coordinate systems.

After lookupTransform, we get the result of coordinate transformation, transform, and then get the values of x and y through transform.getOrigin().y(), transform.getOrigin().x(),Then the angular velocity angular.z and linear velocity linear.x are obtained through mathematical operations, and finally published to let turtle2 move.

15.3. Run the program

After starting roscore, open the little turtle node. At this time, a little turtle will appear in the terminal; then we publish two tf transformations, turtle1->world, turtle2->world, because we want to know the changes between turtle2 and turtle1, You need to know the transformation between them and the world;Then, start the tf listening program. At this time, you will find that another small turtle turtle2 is generated in the terminal, and turtle2 will move towards turtle1;Then, we turn on the keyboard control and control the movement of turtle1 by pressing the direction keys, and then turtle2 will chase turtle1 to move.

First open roscore and enter in the terminal,

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roscore

Run the little turtle node,

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rosrun turtlesim turtlesim_node

Run the broadcast tf program and broadcast turtle1->world,

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rosrun learn_tf turtle_tf_broadcaster __name:=turtle1_tf_broadcaster /turtle1

Run the broadcast tf program and broadcast turtle2->world,

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rosrun learn_tf turtle_tf_broadcaster __name:=turtle2_tf_broadcaster /turtle2

Run the listening tf program,

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rosrun learn_tf turtle_tf_listener

Run the keyboard to control the little turtle node,

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rosrun turtlesim turtle_teleop_key

image-20231025205236768

You can enter rosrun rqt_tf_tree rqt_tf_tree to view the TF tree and enter it in the terminal.

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rosrun rqt_tf_tree rqt_tf_tree

image-20231025205325362

As shown in the figure above, both turtle1 and turtle2 have a TF transformation relationship with the world. Therefore, when turtle1 undergoes relative coordinate transformation relative to the world, turtle2 knows where turtle1 is.Then calculate the relative posture of the two, adjust the linear velocity and angular velocity of turtle2, and move to the position of turtle1.

15.4 TF transformation broadcast and monitoring programming implementation (Python)

15.4.1 Create TF broadcaster

In the function package learn_tf, create a folder script, switch to the directory, create a new .py file, name it turtle_tf_broadcaster.py, copy and paste the following program code into the turtle_tf_broadcaster.py file,

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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import roslib
roslib.load_manifest('learn_tf')
import rospy
import tf
import turtlesim.msg
def handle_turtle_pose(msg, turtlename):
    br = tf.TransformBroadcaster()#Define a tf broadcast
    #tf transformation between broadcast world and input named turtle
    br.sendTransform((msg.x, msg.y, 0),tf.transformations.quaternion_from_euler(0, 0, msg.theta),rospy.Time.now(),turtlename,"world")
if __name__ == '__main__':
    rospy.init_node('turtle1_turtle2_tf_broadcaster')#Initialize ros node
    turtlename = rospy.get_param('~turtle',"turtle_name") #Get the turtle's name from the parameter server
    #Subscribe/pose topic data, which is the turtle’s pose information
    rospy.Subscriber('/%s/pose' % turtlename,turtlesim.msg.Pose,handle_turtle_pose,turtlename)
    rospy.spin()

Program flow chart,

image-20231025214045354

15.4.2 Create TF listener

In the function package learn_tf, create a folder script, switch to the directory, create a new .py file, name it turtle_tf_listener.py, copy and paste the following program code into the turtle_tf_listener.py file,

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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import rospy
import math
import tf
import geometry_msgs.msg
import turtlesim.srv
if __name__ == '__main__':
    rospy.init_node('turtle_tf_listener')#Initialize ros node
    listener = tf.TransformListener()#Initialize a listener
    rospy.wait_for_service('spawn')
    #Call the service to spawn another turtle turtle2
    spawner = rospy.ServiceProxy('spawn', turtlesim.srv.Spawn)
    spawner(8, 6, 0, 'turtle2')
    #Declare a publisher to publish the speed of turtle2
    turtle_vel = rospy.Publisher('turtle2/cmd_vel',geometry_msgs.msg.Twist,queue_size=1)
    rate = rospy.Rate(10.0)
    while not rospy.is_shutdown():
        try:
        #Find tf changes between turtle2 and turtle1
            (trans,rot) = listener.lookupTransform('/turtle2', '/turtle1',rospy.Time(0))
        except (tf.LookupException, tf.ConnectivityException,tf.ExtrapolationException):
            continue
    #Through mathematical calculations, calculate the linear velocity and angular velocity, and then publish them
        angular = 6.0 * math.atan2(trans[1], trans[0])
        linear = 0.8 * math.sqrt(trans[0] ** 2 + trans[1] ** 2)
        cmd = geometry_msgs.msg.Twist()
        cmd.linear.x = linear
        cmd.angular.z = angular
        turtle_vel.publish(cmd)
        rate.sleep()

Program flow chart,

image-20231025214746318

15.4.3. Write launch file to start

After the two python files are written, you need to give execution permission and enter in the terminal.

xxxxxxxxxx
cd ~/ros_ws/src/learn_tf/scripts
sudo chmod a+x turtle_tf_listener.py
sudo chmod a+x turtle_tf_broadcaster.py

In the function package directory, create a new folder launch, switch to launch, create a new launch file, name it start_tf_demo_py.launch, and copy the following content into it,

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<launch>
        <!-- Turtle Node-->
    <node pkg="turtlesim" type="turtlesim_node" name="sim"/>
        <!--broadcast turtle1->world-->
    <node name="turtle1_tf_broadcaster" pkg="learn_tf" type="turtle_tf_broadcaster.py" respawn="false" output="screen" >
        <param name="turtle" type="string" value="turtle1" />
    </node>
        <!--broadcast turtle2->world-->
    <node name="turtle2_tf_broadcaster" pkg="learn_tf" type="turtle_tf_broadcaster.py" respawn="false" output="screen" >
        <param name="turtle" type="string" value="turtle2" />
    </node>
        <!--listener-->
    <node pkg="learn_tf" type="turtle_tf_listener.py" name="listener" />
        <!--Turtle keyboard control node-->
    <node pkg="turtlesim" type="turtle_teleop_key" name="teleop" output="screen"/>
</launch>

15.5 Startup

Terminal starts,

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roslaunch learn_tf start_tf_demo_py.launch

After the program is running, click the mouse on the window running launch and press the arrow keys. Turtle2 will move with turtle1.

image-20231025215952443

ROS-launch file
ROS parameter service

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