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Muto RS Robot📖
01.Introduction to Muto Robot
  • 1.Accessories introduction
  • 2.Precautions for battery
  • 3.FAQ
  • 4.MutoRS Study Guide
  • 5.Quick Start Guide
02.Assembly and write system
  • 1.Raspberry Pi write system
  • 2.Assembly video
03.Remote control course
  • 1.Open and Close APP Control Program
  • 2.Mobile APP Remote Control
  • 3.USB Wireless Controller Remote Control
  • 4.Web Host Computer Remote Control
04.Remote login operation
  • 1.SSH and VNC remote login
  • 2.Jupyter Lab login and running example code
05.AI Large Model Basicsnew
  • 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
06.AI Large Model Developmentnew
  • 1.Register a model service provider account
  • 2.Configure API-KEY
  • 3.Introduction to Dify
  • 4.Dify Basic function
  • 5.AI large model development - Chatbot
  • 6.Deploy the RAG knowledge base
  • 7.RAG knowledge base + Chatbot
  • 8.AI agent workflow
  • 9.Custom wake-up response
  • 10.Core module testing tools
07.AI Large Model (Text Version)new
  • 0.MutoRS Model Setup & Details
  • 1.Semantic Understanding, Instruction Following
  • 2.Multimodal Visual Understanding
  • 3.Multimodal Visual Understanding + Depth Camera Distance Q&A
  • 4.Multimodal Visual Understanding + Visual Line Following
  • 5.Multimodal Visual Understanding + Visual Following
  • 6.Multimodal Visual Understanding + SLAM Navigation
  • 7.Multimodal Visual Understanding + SLAM Navigation + Visual Line Following
  • 8.Intention Estimation + Multimodal Visual Understanding + SLAM Navigation + Visual Functions
08.AI Large Model (Voice Version)new
  • 0.MutoRS Model Setup & Details
  • 1.Semantic Understanding, Instruction Following
  • 2.Multimodal Visual Understanding
  • 3.Multimodal Visual Understanding + Depth Camera Distance Q&A
  • 4.Multimodal Visual Understanding + Visual Line Following
  • 5.Multimodal Visual Understanding + Visual Following
  • 6.Multimodal Visual Understanding + SLAM Navigation
  • 7.Multimodal Visual Understanding + SLAM Navigation + Visual Line Following
  • 8.Intention Estimation + Multimodal Visual Understanding + SLAM Navigation + Visual Functions
09.AI Large Model (Fun Applications)new
  • 1.Versatile performer
  • 2.Action Imitation
  • 3.Problem Solving Expert
  • 4.Exploring Unfamiliar Environments
10.Robot basic course
  • 1)Control buzzer
  • 2)Control PWM servo
  • 3)Control bus servo
  • 4)Robot forward and backward
  • 5)Robot move left and right
  • 6)Robot rotate left and right
  • 7)Control height
  • 8)Control head
  • 9)Action group performance
  • 10)Read data
  • 11)Host computer control
  • 12)Camera driver
11.Voice control coursenew
  • 1.Control Muto motion
  • 2.Control Muto tracking
  • 3.Control Muto color recognition
  • 4.Control Muto color tracking
  • 5.Control multi-point navigation
12.AI vision course
  • 0)Color HSV Calibration
  • 1)Color recognition
  • 2)Color following
  • 3)Color recognition action group
  • 4)Face detection
  • 5)QR code recognition
  • 6)QR code instructions
  • 7)Visual line following
  • 8)Motion learning
  • 9)Synchronous action teaching
13.Mediapipe course
  • 1)Hand detection
  • 2)Attitude detection
  • 3)Overall inspection
  • 4)Face detection
  • 5)Face recognition
  • 6)Face effects
  • 7)Face detection
  • 8)3D object recognition
  • 9)Brush
  • 10)Finger control
  • 11)Gesture recognition
14.Depth camera course
  • 1)Depth camera use
  • 2)Internal reference calibration
  • 3)Web page real-time monitoring
  • 4)ROS+opencv application
  • 5)QR code creation and recognition
  • 6)AR vision
  • 7)Tracking
  • 8)Obstacle recognition
  • 9)Color tracking
  • 10)KCF object tracking
  • 11)RTAB-Map 3D mapping navigation
15.Lidar course
  • 1)About lidar
  • 2)Lidar obstacle avoidance
  • 3)Lidar tracking
  • 4)Lidar guard
  • 5)Patrol
  • 6)SLAM overview
  • 7)ROS2_Gmapping mapping algorithm
  • 8)ROS2_Cartographer mapping algorithm
  • 9)About ROS2 Navigation
  • 10)ROS2 navigation and avoid
  • 11) APP mapping
  • 12)APP navigation
16.Road network navigationnew
  • 1.Road network planning and navigation environment deployment
  • 2.Constructing pose maps
  • 3.Road network annotation
  • 4.Inter-road network planning and navigation
  • 5.Road network navigation under arbitrary poses
  • 6.Path replanning
17.Multi-machine control course
  • 1)Multi-vehicle control
  • 2)Multi-vehicle navigation
18.Robot chassis control
  • 1)Hexapod gait and kinematics
  • 2)Robot information release
  • 3)Robot control
  • 4)URDF model
19.Raspberry Pi course
  • 1)Python usage
  • 2)Use APT to install Python packages
  • 3)Python virtual environment
  • 4)GPIO Zero
  • 5)Pin output high and low levels
  • 6)Pin reads high and low levels
  • 7)Pin output PWM
  • 8)Serial communication
  • 9)I2C communication
20.OpenCV course
  • 1)Introduction to Open Source CV
  • 2)Image reading and display
  • 3)Picture writing
  • 4)Image Quality
  • 5)Pixel operations
  • 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
21.Docker course
  • 1)Overview and installation
  • 2)Common commands
  • 3)Deeply understand and publish images
  • 4)Hardware interaction and data processing
  • 5)Enter docker container
  • 6)Robot development environment construction
  • 7)Update docker images
22.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
23.Linux basic course
  • 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)Driver library and communication
  • 10)Static IP and hotspot mode
  • 11)Bind device ID
  • 12)Capacity expansion and resource allocation
  • 13)Back up system image
  • 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
24.Mainboard Large Model Course
Offline large model (Text Interaction)
  • 0. AI large model image description
  • 1. Deployment of large AI model environments
  • 2. Installation of large model dialogue platform
  • 3. Meta AI Llama 3.2 model
  • 4. Alibaba Cloud Qwen3 Model
  • 5. Microsoft Phi-4-mini model
  • 6. DeepSeek DeepSeek-R1 model
  • 7. Alibaba Cloud Qwen2.5vl Visual Multimodal Large Model
  • 8. Google Gemma3 Visual Multimodal Large Model
  • 9. Llava Visual Multimodal Large Model
  • 10. MiniCPM-V Visual Multimodal Large Model
  • 11. Multimodal Visual Understanding Applications
  • 12. Multimodal Text-Based Image Application
  • 13. Multimodal Video Analysis Application
  • 14. Multimodal visual positioning applications
  • 15. Multimodal table scanning application
  • 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
Download
  • RemoteCtrl_APP(iOS/Android)
  • Mapping_APP(iOS_Android)
  • SystemFile_For_RPi5
  • SystemFile_Jetson
  • SystemFile_RPi
  • Code
  • VM File
  • Hardware Info
  • Instruction Manual
  • Jetson_Boot_USB.tar.gz
  • ROS2_Basic_VM
Download ZIP

Welcome to Muto RS Robot repository

6)Robot development environment construction

6.Robot development environment construction

 

6.Robot development environment construction6.1. Use jupyter lab to access docker6.2. Use vscode to access docker6.2.1. Remote configuration6.2.2. Configure password-free login6.2.3. vscode configuration6.2.3.1. Download and install vscode6.2.3.2. vscode configures ssh remote login to the host6.2.3.3. Enter the robot container6.2.3.4. Configure the docker environment on the vscode remote host

6.1. Use jupyter lab to access docker

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

Note: When using jupyter lab in a docker container, you must run the docker container in host networking mode: 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. To view on other devices, open it in a windows or ubuntu browser (must be on the same LAN, 192.168.2.102 is the IP address in the docker container)
​x
http://192.168.2.102:8888/lab  # Enter the jupyter lab of the host machine
http://192.168.2.102:8889/lab  # Enter jupyter lab inside the container
​
Enter the password: yahboom to enter jupyter lab

image-20230424173324001

 

The following directory is the project path of the robot:

image-20230425101243048

 

 

6.2. Use vscode to access docker

Here we take configuring vscode to access the docker container in windows as an example to explain. The steps to access docker in ubuntu are basically the same.

 

6.2.1. Remote configuration

Make sure that windows can remotely log in to the docker host [car]:

xxxxxxxxxx
Open cmd in windows and input ssh command to test:ssh jetson@192.168.2.102  (Change the username and IP to your own)
​
Or use remote tools: putty, xshell, securecrt, winscp, mobaxterm, finalshell, etc. Mobaxterm is recommended.

 

6.2.2. Configure password-free login

Some of the above steps may require you to enter the host's password. Let's optimize it again and configure password-free login.

  1. First, test opening the cmd command on Windows and using ssh to log in to the host [car]. The instructions are as follows:
xxxxxxxxxx
ssh jetson@192.168.2.102   (Change the username and IP to your 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

Taking Windows 10 as an example, open: This PC---Properties---Advanced system settings---Environment variables, and open the environment variable properties page. Click New in the user variables section. The variable is HOME and the value is C:\Users\name, where name is the user name. You can check the user name of your computer by yourself. The key pair generated will be saved in this directory by default.在这里插入图片描述

 

(2) Generating a Key Pair

Open the command prompt (cmd) and run the command ssh-keygen -t rsa in the directory where the SSH program is located, or anywhere after adding the system environment. Press Enter repeatedly until you see a rectangular graph generated; this indicates successful key generation. Two new files will then appear in the .ssh folder in your user directory: id_rsa (private key) and id_rsa.pub (public key).

img

 

(3) Add the public key to the host machine

First, check if the .ssh directory already exists in the user's root directory on the host machine. If it does not exist, execute the steps in "(2) Generate key pair" above to add the .ssh directory.

Second, check if the authorized_keys file exists in the .ssh directory. If not, execute the following two commands to add it:

xxxxxxxxxx
touch ~/.ssh/authorized_keys
chmod 600 ~/.ssh/authorized_keys

Then, open the Windows command prompt (cmd) and type:

xxxxxxxxxx
ssh username@host "cat >> ~/.ssh/authorized_keys" < C:\Users\name\.ssh\id_rsa.pub
Note: Modify the path <C:\Users\name\.ssh\id_rsa.pub> according to your Windows computer's path.
​
#For example: Modify according to your situation
ssh jetson@192.168.2.102 "cat >> ~/.ssh/authorized_keys" < C:\Users\Admin\.ssh\id_rsa.pub

This command first logs into the host machine, then adds the local Windows public key to the host machine account's personal directory, thus enabling passwordless login. Note that this step requires entering the host machine account password.

(4) Verification

Test again by opening the cmd command on Windows and using SSH to log into the host machine [car]. The command is as follows:

 

xxxxxxxxxx
ssh jetson@192.168.2.102  (Change the username and IP to your own)

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

 

6.2.3. vscode configuration

 

6.2.3.1. Download and install vscode

vscode official website: https://code.visualstudio.com/, just download and install the windows version

 

6.2.3.2. vscode configures ssh remote login to the host

  1. Open vscode, click the icon with the arrow below on the left, then enter remote in the search box, select the Remote Development plug-in, and click Install to install the plug-in.

image-20230424183009372

After vscode is installed by default, it is the English version. You can install the Chinese plug-in to localize it:

image-20230424182205246

 

  1. Press the shortcut key [ctrl + shift + p] in vscode to open the command input window, enter: remote, and then follow the instructions in the figure below to log in to the remote host [car].

image-20230425091020733

image-20230425091230849

image-20230425091248247

image-20230425091304318

image-20230425091323608

image-20230425091358042

image-20230425091415820

If you see the screen above, it means you have successfully logged in to the host computer remotely.

 

6.2.3.3. Enter the robot container

See the tutorial in Chapter [5.Enter docker container] to enter.

 

6.2.3.4. Configure the docker environment on the vscode remote host

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

 

  1. After the installation is complete, a docker icon will appear in the left navigation bar.

image-20230425091532747

 

  1. Click the docker icon

image-20230425093400792

 

  1. Right-click on the running container and follow the steps below:

image-20230425093448428

image-20230425093609630

 

  1. A new window will open. When you see the following, you have entered the container.

image-20230425093643476

 

  1. Open the folder
xxxxxxxxxx
/root/yahboomcar_ros2_ws  # This is the project path of the robot

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 plug-ins to install here are:

img

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

Enter docker container
Update docker images

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