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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

11.ROS action client

11.ROS action client

11.1 action overview

Action is a commonly used communication method in ros. There is an action client and an action server. It should be mentioned here that action and servcie are both communication methods with feedback, so what is the difference between them? An obvious difference is that action can feedback the current status at a certain frequency, while service can only feedback once.This is a very important point. For example, when executing a long-term task, you can use the action communication method to check the progress at any time, and you can also terminate the request.

11.2 action communication principle

Let’s look at the picture of ros-wiki,

image-20231026111456240

Here it shows that the client sends a goal, which is sendGoal, and the server executes the corresponding program after receiving it, which is excuteGoal. This is a simple process, but in it, the client may also send feedback instructions according to the set frequency, and the server will send the feedback results back to the client after receiving it.

There are a few important concepts to know here:

  • Goal

    When a robot performs an action, it should have clear moving target information, telling the server "where I want to go" or "what I want to do", including some parameter settings, direction, angle, speed, etc. This enables the robot to complete action tasks.

  • Feedback

    During the action, the server should feed back real-time status information to the client, telling the client "where are we now" or "where have I done". It is also convenient for the client to choose to continue or give other instructions.

  • Result

    When the movement is completed, the server sends the result data to the client, telling the client "I have arrived now" or "I have completed the action now", so that the client can obtain all the information of this action.For example, it may include the robot's movement duration, final posture, etc.

image-20231026114424613

11.3 action file

Behavior specifications use .action files. The .action file has the goal definition, then the result definition, then the feedback definition, with each part separated by 3 hyphens (---). In the src directory of the workspace, create a new learn_action function package and enter the following instructions:

catkin_create_pkg learn_action std_msgs rospy roscpp actionlib actionlib_msgs

Then go back to the workspace to compile,

xxxxxxxxxx
cd ~/ros_ws
catkin_make

Then create a new folder under the learn_action folder, named action, to store the .action file we defined and input from the terminal.

xxxxxxxxxx
cd ~/ros_ws/src/learn_action
mkdir action

In the action folder, create a new file named DoDishes.action and copy the following code into it.

xxxxxxxxxx
# Define the goal
uint32 dishwasher_id  # Specify which dishwasher we want to use
---
# Define the result
uint32 total_dishes_cleaned
---
# Define a feedback message 
float32 percent_complete

Modify the CMakeLists.txt file to the following content,

xxxxxxxxxx
find_package(catkin REQUIRED COMPONENTS
  actionlib
  actionlib_msgs
  roscpp
  rospy
  std_msgs
  genmsg
)
add_action_files(
   DIRECTORY
   action
   FILES
   DoDishes.action
)
generate_messages(
   DEPENDENCIES
   actionlib_msgs
 )

Then, we go back to the workspace to compile and enter in the terminal,

xxxxxxxxxx
cd ~/ros_ws
catkin_make

After successful compilation, for DoDishes.action, the following message is generated through genaction.py to generate the relevant msg file, which can be seen through rosmsg list and entered in the terminal,

xxxxxxxxxx
rosmsg list

image-20231026120635055

As shown in the figure above, it is the msg file related to the generated DoDishes.action.

11.4 Write action client code

Under the learn_action function package, create a new file named action_client.cpp and copy the following content into it.

​x
#include <actionlib/client/simple_action_client.h>
#include "learn_action/DoDishesAction.h"

typedef actionlib::SimpleActionClient<learn_action::DoDishesAction> Client;

// When the action is completed, the callback function will be called once
void doneCb(const actionlib::SimpleClientGoalState& state,
        const learn_action::DoDishesResultConstPtr& result)
{
    ROS_INFO("Yay! The dishes are now clean");
    ros::shutdown();
}

// When the action is activated, the callback function will be called once.
void activeCb()
{
    ROS_INFO("Goal just went active");
}

// Callback function called after receiving feedback
void feedbackCb(const learn_action::DoDishesFeedbackConstPtr& feedback)
{
    ROS_INFO(" percent_complete : %f ", feedback->percent_complete);
}

int main(int argc, char** argv)
{
    ros::init(argc, argv, "do_dishes_client");

    // Define a client
    Client client("do_dishes", true);

    // Waiting for server side
    ROS_INFO("Waiting for action server to start.");
    client.waitForServer();
    ROS_INFO("Action server started, sending goal.");

    // Create an action goal
    learn_action::DoDishesGoal goal;
    goal.dishwasher_id = 1;

    // Send the action goal to the server and set the callback function
    client.sendGoal(goal,  &doneCb, &activeCb, &feedbackCb);

    ros::spin();

    return 0;
}

Modify CMakeLists.txt file and add the following content,

xxxxxxxxxx
add_executable(action_client src/action_client.cpp)
target_link_libraries( action_client ${catkin_LIBRARIES})
add_dependencies(action_client ${${PROJECT_NAME}_EXPORTED_TARGETS})

Return to the workspace to compile,

xxxxxxxxxx
cd ~/ros_ws
catkin_make

After the compilation is completed, reopen the terminal or source, and then enter the following command to run,

xxxxxxxxxx
rosrun learn_action action_client

image-20231026122446332

Because the server is not running, no corresponding services are provided, or corresponding actions and feedback are performed. In the next section, we learn how to write the server side of the action. Let's look at the core code now.

xxxxxxxxxx
// Define a client
Client client("do_dishes", true);

Here we first define a client with two pieces of information. The first is that the service sought by the client is "do_dishes", and the second is true which means starting a thread to provide services for the subscription of this operation.

xxxxxxxxxx
client.waitForServer();

Waiting for the "do_dishes" service to start

xxxxxxxxxx
learn_action::DoDishesGoal goal;
goal.dishwasher_id = 1;
// Send the action goal to the server and set the callback function
client.sendGoal(goal,  &doneCb, &activeCb, &feedbackCb);

Create a goal. The goal member is dishwasher_id, assign it a value of 1, and then sendGoal sends the goal to the server. It includes goal and three callback functions, namely doneCb, activeCb and feedbackCb.

xxxxxxxxxx
// When the action is completed, the callback function will be called once
void doneCb(const actionlib::SimpleClientGoalState& state,
        const learn_action::DoDishesResultConstPtr& result)
{
    ROS_INFO("Yay! The dishes are now clean");
    ros::shutdown();
}

// When the action is activated, the callback function will be called once.
void activeCb()
{
    ROS_INFO("Goal just went active");
}

// callback function called after receiving feedback
void feedbackCb(const learn_action::DoDishesFeedbackConstPtr& feedback)
{
    ROS_INFO(" percent_complete : %f ", feedback->percent_complete);
}

Here is the feedback member percen_complete. We can use rosmsg info to query the specific members and enter it in the terminal.

xxxxxxxxxx
rosmsg info learn_action/DoDishesFeedback

image-20231026152330110

The same applies to the goal above. Enter in the terminal.

xxxxxxxxxx
rosmsg info learn_action/DoDishesGoal

image-20231026152502874

Summary: First create a client to call the service, then wait for the service to start. After the service starts, define a goal, and then send the goal to the server. Please refer to the official documentation for the parameter API interface:

actionlib: actionlib::SimpleActionClient< ActionSpec > Class Template Reference (ros.org)

 

ROS service server
ROS action server

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