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ROSMASTER M3📖
00.Configuration and Operation Guide
  • 1.Quick start handle to control the car
  • 2.Log in to the car and view the code
  • 3.Enter the Docker (For RPI5)
  • 4.Camera and Radar Configuration (Must Read)
  • 5.Assembly video
  • 6.Modify Control Board Firmware Parameters
01.Chassis Control Course
  • 01.ROS control
  • 02.Handle control
  • 03.Keyboard control
  • 04.Robot state estimation
  • 05.Data remote visualization
  • 06.Line velocity calibration
  • 07.Angular velocity calibration
  • 08.Robot URDF model
  • 09.Line patrol automatic driving
  • 10.Patrol
02.Lidar Course
  • 01.Lidar introduction and use
  • 02.Lidar point cloud fusion and filtering
  • 03.Lidar obstacle avoidance
  • 04.Lidar tracking
  • 05.Lidar guard
  • 06.Gmapping-SLAM mapping
  • 07.Cartographer-SLAM mapping
  • 08.slam_toolbox mapping
  • 09.Lifelong mapping
  • 10.Navigation2 single-point navigation avoid
  • 11.Navigation2 multi-point navigation avoid
  • 12.Cartographer relocalization navigation
  • 13.slam_toolbox localization and navigation
  • 14.Synchronous mapping and navigation
  • 15.RTAB-Map mapping
  • 16.RTAB-Map navigation
  • 17.APP mapping and navigation
03.AI Model Basics
  • 1.AI large model types and principles
  • 2.RAG retrieval enhancement and model training samples
  • 3.Embodied intelligent robot system architecture
  • 4.Embodied intelligent functions core source code
04.AI Model Development
  • 01.Register a model service provider account
  • 02.Configuring API-KEY
  • 03.Introduction to Dify
  • 04.Basic Dify Features
  • 05.AI Large Model Development - chatbot
  • 06.Deploy the RAG knowledge base
  • 07.RAG knowledge base + chatbot
  • 08.AI agent workflow
  • 09.Custom wake-up response
  • 10.Core module testing tools
05.AI Model - Voice Version
  • 1.Semantic understand and command follow
  • 2.Multimodal visual understanding
  • 3.Multimodal visual understanding+SLAM navigation
  • 4.Intention estimation
06.AI Model - Text Version
  • 1.Semantic understand and command follow
  • 2.Multimodal visual understanding
  • 3.Multimodal visual understanding+SLAM navigation
  • 4.Intention estimation
07.Road Network Planning and Navigation
  • 1.Introduction and Quick Start
  • 2.Constructing a Pose Map
  • 3.Road Network Labelling
  • 4.Inter-Road Network Planning and Navigation
  • 5.Free-Pose Road Network Navigation
  • 6.Path Replanning
08.Autonomous Driving expand(Need purchase track map)
  • 01.Environment Setup & Precautions
  • 02.YOLO Environment Configuration
  • 03.Data Collection
  • 04.Data Annotation
  • 05.YOLOv11 Model Training
  • 06.Model Format Conversion
  • 07.Vehicle Centerline Calibration
  • 08.Lane Detection
  • 09.Lane-Keeping Autonomous Driving
  • 10.Traffic Sign Recognition
  • 11.Comprehensive Autonomous Driving Applications
  • 12.Autonomous Driving Debugging Module
  • 13.Road Network Planning with Track Map
  • 14.Road Network Planning with Traffic Sign Recognition
  • 15.AI Model with Road Network Planning
  • 16.AI Model with Track Map and SLAM Navigation
  • 17.AI Model with Road Network Planning and Autonomous Driving
09.Depth Camera Course
  • 01.Dabai_DCW2 Camera Introduction
  • 02.Nuwa-HP60C Camera Introduction
  • 03.Depth Pseudo-Color Image
  • 04.Depth Camera Distance Measurement
  • 05.Wood Block Volume Measurement
  • 06.Edge Detection
  • 07.YOLOv11 Object Detection
  • 08.YOLOv11 Object Tracking
  • 09.Color Following
  • 10.Face Following
  • 11.QR Code Following
  • 12.Machine Code Following
  • 13.KCF Object Tracking
  • 14.QR Code Motion Control
  • 15.AR Vision
10.Mediapipe Visual Course
  • 01.Hand Detection
  • 02.Posture Detection
  • 03.Overall Detection
  • 04.Facial Landmark Detection
  • 05.3D Object Recognition
  • 06.Face Detection
  • 07.Gesture Recognition
  • 08.Face Special Effects
  • 09.Brush
  • 10.Finger Control
  • 11.Palm Target Positioning
  • 12.Fingertip Trajectory Recognition
  • 13.Gesture Following
  • 14.Posture Following
  • 15.Gesture Motion Control
11.Multi-vehicle Course
  • 1.Multi-vehicle Control
  • 2.Multi-vehicle Navigation
  • 3.Multi-vehicle Platoon
12.AI Large Model Basic Course
Offline large model (Text Interaction)
  • 0. AI Large Model Mirroring Explanation
  • 1. Deployment of large-scale AI models
  • 2. Installation of the large model dialogue platform
  • 3. Meta AI Llama 3.2 model
  • 4. Alibaba Cloud Qwen3 Model
  • 5. DeepSeek DeepSeek-R1 model
  • 6. Microsoft Phi-4 mini model
  • 7. Alibaba Cloud Qwen2.5vl Visual Multimodal Large Model
  • 8. Google Gemma3 Visual Multimodal Large Model
  • 9. Llava Visual Multimodal Large Model
  • 10. Multimodal Visual Understanding Applications
  • 11.MiniCPM-V Visual Multimodal Large Model
  • 12. Multimodal Text-Based Image Application
  • 13. Multimodal Video Analysis Application
  • 14. Multimodal table scanning application
  • 15. Multimodal visual positioning applications
  • 16. Multimodal Autonomous Agent Application
Offline large model (Voice Interaction)
  • 0. Voice Interaction Hardware Connection (Must Read)
  • 1. Offline speech-to-text (ASR)
  • 2. Offline Text-to-Speech (TTS)
  • 3. AI large model voice interaction
  • 4. Multimodal visual understanding and voice interaction
  • 5. Multimodal Text-Based Image Application
  • 6. Multimodal Video Analysis Application
  • 7. Multimodal visual positioning applications
  • 8. Multimodal table scanning application
  • 9. Multimodal Autonomous Agent Applications
  • 10. AI large model offline voice assistant
Online large model (Text Interaction)
  • 1. OpenRouter Large Model API Aggregation Platform
  • 2. Multimodal visual understanding applications
  • 3. Multimodal visual positioning applications
  • 4. Multimodal table scanning application
  • 5. Multimodal Autonomous Agent Applications
Online large model (Voice Interaction)
  • 0. Voice Interaction Hardware Connection (Must Read)
  • 1. Online speech-to-text (ASR)
  • 2. Online Text-to-Speech (TTS)
  • 3. AI large model voice interaction
  • 4. Multimodal visual and voice interaction
  • 5. Multimodal visual positioning applications
  • 6. Multimodal table scanning application
  • 7. Multimodal Autonomous Agent Applications
  • 8. AI large model online voice assistant
13.Control Board Course
1.Development environment setup
  • 1.Introduction to the Control Board
  • 2.Set up the STM32CUBEIDE development environment
  • 3.Burning STM32 firmware using SWD
  • 4.Burning STM32 firmware using serial port
  • 5.Compile the microros driver library
  • 6.Install and start the microros agent
  • 7.Import Project
2.Control board STM32 basic routine
  • 1.Turn on the LED light
  • 2.Button functions
  • 3.Drive the buzzer
  • 4.Serial communication
  • 5.Battery voltage detection
  • 6.Driving PWM Servo
  • 7.Drive motor
  • 8.Read motor encoder data
  • 9.PID control of motor speed
  • 10.Robot kinematics analysis theory
  • 11.Read IMU data
  • 12.Read Lidar data
  • 13.Flash access data
  • 14.Driving OLED Displays
  • 15.Driving RGB light strip
  • 16.SBUS model aircraft remote control
  • 17.USB controller remote control
  • 18.CAN bus communication
3.Control board micro_ros basic routine
  • 1.Post a topic
  • 2.Subscribe to a topic
  • 3.Multi-topic subscription and publishing
  • 4.Subscribe to the buzzer topic
  • 5.Subscribe to the bus servo topic
  • 6.Subscribe to the rate control topic
  • 7.Release Speed Topic
  • 8.Publish IMU data topic
  • 9.Publish radar data topic
14.Main Control Course
Jetson Orin Nano_NX
  • 1.Jetson Orin board introduction
  • 2.Restore the factory image system
  • 3.Backup SSD system
  • 4.SSD expansion
  • 5.Network configuration
  • 6.SSH remote login
  • 7.VNC remote control
  • 8.Remote file transfer
  • 9.Jtop tool
  • 10.Exchange space expansion
Raspberry Pi
  • 1. Introduction to Raspberry PI 5
  • 2. Raspberry PI system installation and backup
  • 3. Powering the Raspberry PI 5
  • 4. Startup of Raspberry PI 5
  • 5. Update and upgrade operating system
  • 6. Introduction to raspi-config tool
  • 7. config.txt file description
  • 8. Network Configuration
  • 9. remote access
  • 10. Transfer files remotely
  • 11. Set display resolution and rotation
  • 12. Set screen to sleep
  • 13. Play audio and video
  • 14. Using USB camera
  • 15. Using MIPI camera
  • 16. Get real-time temperature of Raspberry Pi
RDKX5
  • 1.Introduction to RDK X5 board
  • 2.Write system file
  • 3.Hardware interface description
  • 4.Install case and other accessories
  • 5.Network configuration
  • 6.Remote login
  • 7.Update system
  • 8.srpi-config user guide
  • 9.config. txt configuration file
  • 10.Self-startup setting
  • 11.RDK exclusive command usage
  • 12.Linux command usage
  • 13.GPIO readwrite operation
  • 14.Image classification algorithm
  • 15.Use USB camera
  • 16.Use MIPI camera
  • 17.Binocular camera depth algorithm
15.YOLOv11 Development (ORIN)
  • 1.YOLO environment construction
  • 2.CLI use
  • 3.Object detection
  • 4.Instance segmentation
  • 5.Pose estimation
  • 6.Image classification
  • 7.Oriented object detection
  • 8.Model conversion
  • 9.Dataset annotation
  • 10.Model Training_Conversion
  • 11.Model prediction
16.YOLOv11 Development(RDKX5)
  • 1.YOLO environment construction
  • 2.CLI use
  • 3.Object detection
  • 4.Instance segmentation
  • 5.Pose estimation
  • 6.Image classification
  • 7.Oriented object detection
  • 8.Model prediction
  • 9.Model Quantization
17.YOLOv11 Development (RPI)
  • 1.YOLO environment construction
  • 2.CLI use
  • 3.Object detection
  • 4.Instance segmentation
  • 5.Pose estimation
  • 6.Image classification
  • 7.Oriented object detection
  • 8.Model conversion
  • 9.Model prediction
18.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
19.ROS2 Basic Course
  • 1.Introduction to ROS2
  • 2.ROS2 install Humble
  • 3.ROS2 development environment
  • 4.ROS2 workspace
  • 5.ROS2 function package
  • 6.ROS2 node
  • 7.ROS2 topic communication
  • 8.ROS2 service communication
  • 9.ROS2 action communication
  • 10.ROS2 custom interface message
  • 11.ROS2 parameter service case
  • 12.ROS2 meta-function package
  • 13.ROS2 distributed communication
  • 14.ROS2 DDS
  • 15.ROS2 time related API
  • 16.ROS2 common command tools
  • 17.ROS2 rviz2 use
  • 18.ROS2 rqt toolbox
  • 19.ROS2 Launch startup file configuration
  • 20.ROS2 recording and playback tool
  • 21.ROS2 URDF model
  • 22.ROS2 Gazebo simulation platform
  • 23.ROS2 TF2 coordinate transformation
20.Docker Course
  • 1、Docker overview and docker installation
  • 2、Common commands for docker image containers
  • 3、Docker images deeply understand and publish images
  • 4、Docker hardware interaction and data processing
  • 5、Enter the bot's docker container
  • 6、Robot development environment construction in Docker
21.Image Processing Basics Course
  • 1. Introduction to Open Source CV
  • 2. Image reading and display
  • 3. Image writing
  • 4. Image quality
  • 5. Pixel Operation
  • 6. Image zoom
  • 7. Image cutting
  • 8. Image translation
  • 9. Image Mirroring
  • 10. Affine Transformation
  • 11. Image Rotation
  • 12. Perspective Transformation
  • 13. Grayscale processing
  • 14. Image Binarization
  • 15. Edge Detection
  • 16. Line drawing
  • 17. Rectangle and circle drawing
  • 18. Drawing text and pictures
Download
  • Mapping_APP(iOS_Android)
  • MainBoard_AI_Pure_System
  • Robot_Image_System_File
  • Code-Firmware
  • Instruction_Manual
  • VM_File
  • Software_Tools
  • Firmware_DriverFile
  • 3D_Model_File
  • ROS2_Basic_VM
  • Other_Annex
Download ZIP

Welcome to ROSMASTER M3 repository

6、Robot development environment construction in Docker

6、Robot development environment construction in Docker

 

6、Robot development environment construction in Docker6.1、Using Jupyter Lab to access Docker6.2、Use vscode to access docker6.2.1、Remote configuration6.2.2、vscode configuration6.2.2.1、download and install VSCODE6.2.2.2、vscode configuration ssh remote login to the host6.2.2.3、enter the robot container6.2.2.4、Vscode remote host configuration docker environment6.2.2.5、Configure passwordless login

The operating environment and software and hardware reference configurations are as follows:

  • Reference model: ROSMASTER X3

  • Robot hardware configuration: Arm series main control, Silan A1 lidar, AstraPro Plus depth camera

  • Robot system: Ubuntu (version not required) + docker (version 20.10.21 and above)

  • PC Virtual Machine: Ubuntu (20.04) + ROS2 (Foxy)

  • Usage scenario: Use on a relatively clean 2D plane

6.1、Using Jupyter Lab to access Docker

  1. Enter the container, see [5. Enter the docker container of the robot], and execute the following command:

Note: When using Jupyter Lab in a docker container, you must run the docker container as host: add the "--net=host" parameter when running the container

root@ubuntu:/# jupyter lab --allow-root
[I 2023-04-24 09:27:45.265 ServerApp] Package jupyterlab took 0.0001s to import
[I 2023-04-24 09:27:45.277 ServerApp] Package jupyter_server_fileid took 0.0096s to import
[I 2023-04-24 09:27:45.297 ServerApp] Package jupyter_server_terminals took 0.0190s to import
[I 2023-04-24 09:27:45.429 ServerApp] Package jupyter_server_ydoc took 0.1301s to import
[I 2023-04-24 09:27:45.431 ServerApp] Package nbclassic took 0.0001s to import
..................
  1. Other devices view, open in Windows or Ubuntu browser (must be under the same LAN, 192.168.2.102 is the IP address in the docker container)

xxxxxxxxxx
http://192.168.2.102:8888/lab
Enter the password: Yahboom            enter Jupyter Lab

image-20230424173324001

The following directory is the project path of the bot:

image-20230425101243048

6.2、Use vscode to access docker

Let's take the example of configuring VSCODE to access the Docker container in Windows, and the steps to access Docker in Ubuntu are basically the same.

6.2.1、Remote configuration

See [VI. Linux Operating System---- 3. Remote Control] chapter

Make sure Windows can telnet to the Docker host [car]:

​x
Open cmd in windows enter ssh command test: ssh jetson@192.168.2.102 (username and ip modified to own)

Or use remote tools: putty, xshell, securecrt, winscp, mobaxterm, finalshell, etc

6.2.2、vscode configuration

6.2.2.1、download and install VSCODE

VSCODE official website: https://code.visualstudio.com/, download the installation of the Windows version

6.2.2.2、vscode configuration ssh remote login to the host

  1. Open vscode, click the icon of the arrow at the bottom left, then enter remote in the search box, select the Remote Development plugin, and click Install to install the plugin

image-20230424183009372

vscode is installed by default and then in English, and you can install Chinese plugins to localize:

image-20230424182205246

  1. Press the shortcut key [Ctrl + Shift + P] in VSCODE to open the command input window, type: remote, and then log in to the remote host [car] according to the instructions shown below.

image-20230425091020733

image-20230425091230849

image-20230425091248247

image-20230425091304318

image-20230425091323608

image-20230425091358042

image-20230425091415820

How to see the screen shown above, indicating that you have successfully logged in to the host computer.

6.2.2.3、enter the robot container

See the tutorial in the [5. Enter the docker container of the robot] chapter.

6.2.2.4、Vscode remote host configuration docker environment

  1. Install the docker plug-in on the remote host [car].image-20230425091515978

  2. After the installation is completed, a docker icon will appear in the left navigation bar

image-20230425091532747

  1. Click the docker icon

image-20230425093400792

  1. Right-click the running container and operate as shown below:

image-20230425093448428

image-20230425093609630

  1. A new window will open and see the following indicates that it has entered the container

image-20230425093643476

  1. Open the folder

xxxxxxxxxx
/root/yahboomcar_ros2_ws  # This is the project path for the bot

image-20230425093748086

image-20230425093730162

image-20230425093808074

image-20230425093900096

  1. Similarly, we can also install the plug-ins we need in the container to facilitate our development

image-20230425102200355

In addition to ROS, the recommended plugins to install here are:

img

After completing the above steps, you can manipulate the code files in the container to develop and learn.

6.2.2.5、Configure passwordless login

In the above steps, some steps may require entering the password of the host, here is another optimization, configure passwordless login.

  1. First test using SSH to log in to the host [car] in Windows, the instructions are as follows:

xxxxxxxxxx
ssh jetson@192.168.2.102 (username and IP modified to own)

At this time, you will find that you need to enter the host password

  1. Next, configure password-free login

(1) Add environment variables

Open the environment variable properties page, click New in the user variables section, the variable is HOME, the value is C:Usersname, where name is the user name, you can view the user name of your own computer, and then the generated key pair is saved in this directory by default. 在这里插入图片描述

(2) Generate a key pair

Open the cmd command line, run it in the directory where the ssh program is located, or run [ssh-keygen -t rsa] anywhere after adding the system environment to generate the key, and then enter all the way, when you see a histogram generated, then the key is generated successfully. At this time, there will be two more files under the .ssh folder of the user directory, namely id_rsa (private key) and id_rsa.pub (public key)

img

(3) Add the public key to the host

Similarly, open the cmd command line, enter

xxxxxxxxxx
ssh username@host "cat >> ~/.ssh/authorized_keys" < C:Usersname.sshid_rsa.pub 
#For example: Modify ssh jetson@192.168.2.102 "cat >> ~/.ssh/authorized_keys" < C:UsersAdmin.sshid_rsa.pub  

This command first logs in to the host, and then adds the public key under the local computer, i.e. win, to the personal directory of the host account to achieve password-free login. Note that this step requires the password of the host account.

(4) Verification

Test again using ssh in Windows to log in to the host [trolley], the command is as follows:

xxxxxxxxxx
ssh jetson@192.168.2.102 (username and IP modified to own)

At this time, you will find that you no longer need to enter the password.

Restart VSCODE, and where you need to enter a password, you don't need to enter a password.

Enter the bot's docker container
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