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ROSMASTER R2📖
1.Preparation and First Trial
  • 1.1 Accessories introduction
  • 1.2 Precautions for battery
  • 1.3 First Trial
2.Assembly steps
  • Installaction Video
3.ROS1-Remote control course
  • 3.1 APP remote control
  • 3.2 Model aircraft remote control
  • 3.3 Handle remote control
4.ROS1-Hardware course
  • 4.1 About expansion board_V3.0
  • 4.1 About expansion board_V1.0
  • 4.2 STM32 Development environment
  • 4.3 Button to control the buzzer to whistle
  • 4.4 FreeRTOS application
  • 4.5 Serial communication
  • 4.6 SBUS model aircraft remote control
  • 4.7 CAN bus communication
  • 4.8 RGB colorful light bar
  • 4.9 Timer interrupt to control PWM servo
  • 4.10 Control serial port servos
  • 4.11 Nine-axis attitude sensor to obtain data
  • 4.12 Control motor
  • 4.13 Timer captures the encoder data
  • 4.14.1 Kinematic Analysis of Mecanum Wheel
  • 4.14.2 Kinematics Analysis of Ackermann Car
  • 4.15 PID control robot movement
5.ROS1-Basic course
  • 5.1 Update the firmware
  • 5.2 Turn off the self-starting process at startup
  • 5.3 Install the Rosmaster driver library
  • 5.4 Buzzer whistle
  • 5.5 Control PWM servo
  • 5.6 RGB colorful light bar special effects display
  • 5.7 Control motor
  • 5.8 Control robot movement
  • 5.9 Control Serial Servo
6.ROS1-Linux operating system
  • 6.1 Virtual machine installation and use
  • 6.2 Linux basics
  • 6.3 Remote control
  • 6.4 Multi-machine communication configuration
  • 6.5 Static IP and hotspot mode
  • 6.6 Bind the device ID
  • 6.7 Web page real-time monitoring
  • 6.8 Expansion tutorial
  • 6.9.1 Jetson Nano write sytem
  • 6.9.2 Jetson Xavier NX write system
  • 6.9.3 Jetson TX2 NX write system
  • 6.9.4 Raspberry Pi write system
7.ROS1-Docker-pi5
  • 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
  • 7、How customers can update docker images in the future
8.ROS1-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
9.ROS1-OpenCV courses
  • 9.1 Getting started with Open Source CV
  • 9.2 Geometric transformation
  • 9.3 Image processing and drawing text line segments
  • 9.4 Image beautification
  • 9.5 Opencv application
  • 9.6 AR vision
  • 9.7 AR QR code
  • 9.8 ROS+Opencv foundation
  • 9.9 ROS+Opencv application
  • 9.10 Data transformation and point cloud
  • 9.11 MediaPipe development
10.ROS1-Robot control course
  • 10.1 PID algorithm theory
  • 10.2 Robot PID debugging
  • 10.3 Robot information release
  • 10.4 Robot keyboard control
  • 10.5 Robot handle control
  • 10.6 Robot state estimation
  • 10.7 Robot calibration
  • 10.8 Robot URDF model
11.ROS1-Depth camera course
  • 11.1 How to use the Astra camera
  • 11.2 Astra camera calibration
  • 11.3 Astra Color Tracking
  • 11.4 Astra Object Tracking
  • 11.5 Visual tracking autopilot
  • 11.6 Pure visual 2D mapping and navigation
  • 11.7 RTAB-Map 3D mapping navigation
  • 11.8 ORB_SLAM2 basics
  • 11.9 ORB_SLAM2_Octomap
12.ROS1-Lidar course
  • 11.1 Lidar basic-SLAM
  • 11.1 Lidar basic-EAI
  • 11.2 Lidar avoiding
  • 11.3 Lidar guard
  • 11.4 Lidar follow
  • 11.5 Patrol game
  • 11.6 gmapping mapping
  • 11.7 hector mapping
  • 11.8 karto mapping
  • 11.9 cartographer mapping
  • 11.10 rrt_exploration mappin
  • 11.11 AMCL
  • 11.12 Navigation and avoiding
  • 11.13 APP mapping and navigation
  • 11.14 TEB path planning algorithm
13.ROS1-Multi-robot control course
  • 13.1 Multi-machine handle control
  • 13.2 Multi-machine navigation
  • 13.3 Multi-machine formation
  • 13.4 Multi-machine surround
14.ROS1-Deep learning courses
  • 14.1 KNN recognizes handwritten digits
  • 14.2 Basic use of TensorFlow
  • 14.3 Basic use of PyTorch(jetson)
  • 14.4 yolov5 model training(jetson)
  • 14.5 yolov5+tensorrt acceleration(jetson)
  • 14.6 yolov4-tiny
15.ROS1-Voice control courses
  • 15.0 How to use old voice module in new image
  • 15.1 Introduction of voice control module-NEW
  • 15.2 Voice control module port binding-NEW
  • 15.1 Introduction of voice control module-Old
  • 15.2 Voice control module port binding-Old
  • 15.3 Voice control car movement
  • 15.4 Voice control Autopilot
  • 14.5 Voice control color recognition
  • 15.6 Voice control color tracking
  • 15.7 Voice control multi-point navigation
16.ROS1-Model training Autopilot
  • 16.0 ReadMe
  • 16.1 yolov5 to train traffic signs
  • 16.2 TensorRt acceleration identification mark
  • 16.3 Road sign indication function description
  • 16.4 Collect data
  • 16.5 Training model
  • 16.6 Model transformation and application
  • 16.7 AI Autopilot
17. ROS2-Remote control course
  • 1. APP remote control
  • 2. Model aircraft remote control
  • 3. Handle remote control
18.ROS2-Hardware course
  • 1. Expansion board introduction_V3.0
  • 1. Expansion board introduction_V1.0
  • 2. STM32 development environment build
  • 3. Key control buzzer sounding
  • 4. FreeRTOS applications
  • 5. Serial communication
  • 6. SBUS model airplane remote control
  • 7. CAN bus communication
  • 8. RGB colorful light bar
  • 9. Timer interrupt control PWM servos
  • 10. Control serial servos
  • 11. Nine-axis attitude sensor acquires data
  • 12. Control motor forward and reverse
  • 13. Timer Capture Encoder Data
  • 14. Theory of Robot Kinematics Analysis
  • 14.1.Mecanum wheel
  • 14.2.Ackerman
  • 14.3.4WD
  • 15. PID control of robot motion
19. ROS2-Basic course
  • 1. Update the firmware of the expansion board
  • 2.Close the large self-starting program at boot
  • 3. Install Rosmaster driver library
  • 4. Buzzer sounding
  • 5. Control PWM servo
  • 6. RGB colorful light bar special effects show
  • 7. Control motor forward and reverse
  • 8. Controlling robot motion
  • 9. Control serial servos
20. ROS2-Linux operating systemnew
  • 01. Virtual Machines
  • 02. Linux Basics
  • 03. Remote control
  • 04. Multi-computer communication configuration
  • 05. Static ip and hotspot mode
  • 06. Bind device ID
  • 07.Webpage real-time monitoring
  • 08. Expansion Tutorial
  • 09-1.Jetson Nano write system file
  • 09-2. Raspberry Pi write system file
  • 09-3.Orin write system
  • 10.Jetpack old system upgraded to SUPER systemnew
21.ROS2 -Docker(Nano Pi)
  • 1. docker overview and docker installation
  • 2. docker image container common command
  • 3. docker image in-depth understanding and publishing images
  • 4. docker hardware interaction and data processing
  • 5. Enter the docker container of the robot
  • 6. docker in the robot development environment build
  • 7. Customer's method of updating docker images in the future
22.ROS2- ROS2 Basic Tutorial
  • 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
23. ROS2-Robot control course
  • 1. PID algorithm theory
  • 2. Robot information release
  • 3. Robot control
  • 4. Robot state estimation
  • 5. Robot calibration
  • 6. URDF model
24. ROS2-Lidar course
  • 1-1. Introduction and use of SLAM lidar
  • 1-2. Introduction and use of 4ROS lidar
  • 2. Lidar obstacle avoidance fun gameplay
  • 3. Lidar tracking fun gameplay
  • 4. Patrol function play
  • 5. gmapping map building algorithm
  • 6. cartographer building algorithm
  • 7. Navigation and obstacle avoidance
  • 8. Rosmaster_R2 using the app to build maps
  • 9. Rosmaster_R2 navigating with the app
25. ROS2-OpenCV courses
  • 1. opencv basic course
  • 2. ROS+opencv application
  • 3. QR code creation and recognition
  • 4. AR Vision
  • 5. mediapipe fun gameplay
  • 6. Patrol line autopilot
26. ROS2-Depth camera courses
  • 1. Depth camera use
  • 2. Camera calibration
  • 3. Color tracking
  • 4. KCF object tracking
  • 5. ORB_SLAM2 Basics
  • 6. ORB_SLAM2 PCL mapping
  • 7. ORB_SLAM2 Octomap mapping
  • 8. RTAB-Map 3D mapping navigation
27.ROS2- Voice control courses
  • 0. How to use old voice module in new image
  • 1.1 Introduction to the module-New
  • 1.2 Voice Control Module Port Binding-New
  • 1.3 Introduction to the module-Old
  • 2. Voice control car movement
  • 3. Voice control autopilot
  • 4. Voice control color recognition
  • 5. Voice control color tracking
  • 6. Voice control multi-point navigation
28.Deep learning courses(SUPER)
  • 1.KNN recognizes handwritten digitsnew
  • 2.Basic use of TensorFlownew
  • 3.Basic use of PyTorch(jetson)new
  • 4.YOLOv11Model conversion(SUPER)new
  • 5.YOLOv11Dataset annotation(SUPER)new
  • 6.YOLOv11Model conversion + tensorrt acceleration(SUPER)new
  • 7.YOLOv11Object recognition(SUPER)new
29. ROS2-Multi-robot control
  • 1. Multi-robot control
  • 2. Multi-robot navigation
LLM tutorial
1.Development environment
  • 0. Model usage instructions
  • 1.Environments deployment
  • 2.Dialogue platform installation
2.Jetson Nano B01
  • 1.Qwen2 model
  • 2.TinyLlama model
  • 3.Phi-3 model
  • 4.Gemma model
  • 5.Orca Mini model
  • 6.StarCoder2 model
  • 7.DeepSeek-R1 model
  • 8.Expand LLaVA-Phi3
3.Jetson Orin NXNANO
  • 1.Llama3 model
  • 2.Qwen2 model
  • 3.Phi-3 model
  • 4.Gemma model
  • 5.WizardLM2 model
  • 6.Codellama model
  • 7.DeepSeek-R1 model
  • 8.Orca Mini model
  • 9.StarCoder2 model
  • 10.TinyLlama model
  • 11.Expand LLaVA
  • 12.Expand LLaVA-Phi3
4.Raspberry Pi 5B
  • 1.Meta AI Llama3 model
  • 2.Qwen2 model
  • 3.Phi-3 model
  • 4.Gemma model
  • 5.WizardLM2 model
  • 6.Meta AI Codellama model
  • 7.DeepSeek-R1 model
  • 8.Expand LLaVA
FAQ
  • 1.Using your own Jetson board, car cannot start normally
Download
  • RemoteCtrl_APP(iOS/Android)
  • Mapping_APP(iOS_Android)
  • Instruction Manual
  • SystemFile_ROS1
  • SystemFile_ROS2
  • SystemFile_ROS2_OrinNANOSUPER
  • SystemFile_ROS2_OrinNXSUPER
  • VM software
  • Code
  • Jetson_Boot_USB.tar.gz
  • Autopilot Annex
  • Other_Annex_File
  • LLM_SystemFile
  • ROS2_Basic_VM
Download ZIP

Welcome to ROSMASTER R2 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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