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DOFBOT-JetsonNANO📖
00.Must Read Before Use!!
  • 00.Must Read Before Use!!
01.First Trial
  • 1.First Trial
  • 2.Analysis of common problems
  • 3.Calibration offset
  • 4.Precautions for DIY Robotic Arm
  • 5.Jetson Nano Version – Must Read
  • 6.Robotic arm gripping anomaly solutions
02.Assemble Course
  • 1.Installation video
  • 2.Calibrate the Robotic Arm
  • 3.Install the map
03.Remote control course
  • 1.Mobile APP control
  • 2.USB handle control
  • 3.PC web page programming
04.Preparation
  • 1.Manage APP control service
  • 2.Manage JupyterLab service
  • 3.Manage OLED service
  • 4.Modifying ROS_DOMAIN_ID for Multi-Machine
  • 5.Jetpack old system upgraded to SUPER system
05.Basic control course
  • 1.Control RGB light
  • 2.Control buzzer
  • 3.Control single servo
  • 4.Read servo angle
  • 5.Control all servo
  • 6.DOFBOT up down left right
  • 7.DOFBOT dancing
  • 8.DOFBOT memory action
  • 9.DOFBOT grab block
  • 10.Porter
  • 11.Stacked Arhat
  • 12.Synchronous action
  • 13.USB camera display
06.AI vision basic course
  • 1.Gesture Recognition
  • 2.Color threshold adjustment&Color calibration
  • 3.Color recognition
  • 4.Face recognition
  • 5.YOLOv11 garbage recognition
  • 6.YOLOv11 object recognition
07.2D Visual Tracking Course
  • 1.PID algorithm basics
  • 2.Color block positioning
  • 3.Color block tracking
  • 4.Face positioning
  • 5.Face tracking
  • 6.Gesture recognition fixed action
08.2D Visual Grasping Course
  • 1.Gesture recognition stack blocks
  • 2.Color recognition grab blocks
  • 3.Catch game
  • 4.Color sorting and stacking
  • 5.Garbage sorting
  • 6.Color interaction
09.Mediapipe course
  • 1.Hand detection
  • 02.Posture detection
  • 3.Overall detection
  • 4.Facial Detection
  • 5.Face Detection
  • 6.Face effects
  • 7.3D object recognition
  • 8.Paintbrush
  • 9.Finger control
  • 10.Gesture Recognition
  • 11.Gesture control robotic arm action group
  • 12.Arm posture control robotic arm
  • 13.Palm Targeting
  • 14.Robotic arm tracks the palm
  • 15.Fingertip trajectory recognition
  • 16.Fingertip trajectory control robotic arm
  • 17.Gesture Grasp and Release Objects
  • 18.Finger Control Robot Arm
10.AI Large Model Voice Module (Must Read)new
  • 1. Introduction to AI Large Model Voice Module
  • 2. Flash the firmware (Must read)
  • 3. Modify the wake-up word and command word
11.2D Voice Control Coursenew
  • 01.Simple Voice Control
  • 02.Smart Voice Control
  • 03.Voice Action Control
  • 04.Memory Learning Mode
  • 05.Color Recognition with Voice Broadcast
  • 06.Color Recognition and Grasping with Voice Broadcast
  • 07.Color Recognition and Sorting with Voice Broadcast
  • 08.Voice Recognition Target Tracking
  • 09.Voice Control Stacking
  • 10.Voice Control Color Sorting and Stacking
  • 11.Garbage Recognition with Voice Broadcast
  • 12.Garbage Sorting with Voice Broadcast
12.Robotic Arm Control and Inverse Kinematics
  • 1. Robotic arm ROS control
  • 2. Inverse solution
13.3D Sorting and Grasping Coursenew
  • 1.AprilTag ID sorting
  • 2.MediaPipe Gesture-AprilTag ID sorting
  • 3.MediaPipe Gesture-AprilTag distance sorting
  • 4.Block color sorting
  • 5.Block different shape sorting
  • 6.Yolov11 garbage sorting
14.3D Tracking Coursenew
  • 1.AprilTag Tracking
  • 2.Color Block Tracking
  • 3.KCF Object Tracking
15.3D Voice Control for Sorting & Tracking Coursenew
  • 1.Voice Control AprilTag ID Sorting
  • 2.Voice Control Color Block Sorting
  • 3.Voice Control Yolov11 Garbage Recognition Sorting
  • 4.Voice Control AprilTag Tracking
  • 5.Voice Control Color Block Tracking
  • 6.Voice Control Object Tracking
16.AI 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
17.AI Model Developmentnew
  • 1.Register a model service provider account
  • 2. Configure API-KEY
  • 3. Introduction to Dify
  • 4. Basic Dify Features
  • 5. Deploy the RAG knowledge base
  • 6. AI Large Model Development - chatbot
  • 7. RAG knowledge base + chatbot
  • 8. AI agent workflow
18.AI Model – Text Versionnew
  • 1. Semantic understand instruction following
  • 2. Multimodal visual understand
  • 3. Text Large Model + robotic arm control
  • 4. Multimodal Large Model+video understanding
  • 5. Multimodal Large Model+robotic arm gripping
  • 6. Multimodal Large Model+robotic arm tracking
  • 7. Multimodal Large Model+robotic arm sorting
  • 8. Multimodal Large Model+robotic arm handling
  • 9. Multimodal Large Model+Color Blocks Back In Place
  • 10. Multimodal Large Model + Intent Prediction + Robotic Arm Gripping
  • 11. Text Large Model + YOLOv11 + Intelligent Garbage Classification
19.AI Model – Voice Versionnew
  • 1. Semantic understand instruction following
  • 2. Multimodal visual understand
  • 3. Text Large Model + robotic arm control
  • 4. Multimodal Large Model+video understanding
  • 5. Multimodal Large Model+robotic arm gripping
  • 6. Multimodal Large Model+robotic arm tracking
  • 7. Multimodal Large Model+robotic arm sorting
  • 8. Multimodal Large Model+robotic arm handling
  • 9. Multimodal Large Model+Color Blocks Back In Place
  • 10. Multimodal Large Model + Intent Prediction + Robotic Arm Gripping
  • 11. Text Large Model + YOLOv11 + Intelligent Garbage Classification
20.AI Model – Fun Applicationsnew
  • 1.AI Butler-Intent Inference
  • 2.AI Encyclopedia-Arithmetic Master
  • 3.AI Encyclopedia-You asked I Pointed
  • 4.AI Encyclopedia-Nutritional Analysis
  • 5.AI Encyclopedia-Problem Solving Expert
  • 6.AI Encyclopedia-English Spelling
  • 7.AI Butler-Garbage Sorting
  • 8.AI Top Chef
21.MoveIt2 Course
  • 1.MoveIt configuration
  • 2. MoveIt Usage
  • 3. MoveIt Drive Real Robot
  • 4. MoveIt Random Movement
  • 5. MoveIt Forward Kinematics Design
  • 6. MoveIt Inverse Kinematics Design
  • 7. MoveIt Cartesian Path
  • 8. MoveIt Scene Design
  • 9. MoveIt Trajectory Planning
  • 10. MoveIt Collision Detection
22.YOLOv11 Development Course
  • 1. YOLO Environment Setup
  • 2. CLI Usage
  • 3. Object Detection
  • 4. Instance Segmentation
  • 5. Pose Estimation
  • 6. Image Classification
  • 7. Oriented Bounding Boxes Object Detection
  • 8. Model Conversion
  • 9. Model Prediction
23.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.Capacity expansion and resource allocation
  • 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
24.GPIO Hardware Control Course
  • 1.GPIO description
  • 2.GPIO library installation
  • 3.GPIO reading
  • 4.GPIO output
  • 5.Serial communication
  • 6.I2C communication
25.OpenCV course
  • 0. Must Read!
  • 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
26.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
27. Advanced Visuals Course
  • 0. Must Read! Porting and Compilation
  • 1. QR Code
  • 2. Human Pose Estimation
  • 3. Object Detection
  • 4. AR Vision
  • 5. ROS + OpenCV Basics
28.Jupyter Lab Usage Course
  • 0.Read the document before operation
  • 1.Jupyter Lab environment construction
  • 2.Basic use of Jupyter Lab
  • 3.Jupyter Lab plug-in installation
  • 4.Basic use of Jupyter Widgets
29.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.Start the Dofbot container
30.Python Programming Basics
  • 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
Download
  • APP(iOS/Android)
  • Code
  • SystemFile
  • VM File
  • PC software
  • Instruction manual
  • Jetson_Boot_USB.tar.gz
  • More Annex
  • Tools
Download ZIP
FAQ
  • 1、Precautions for DIY Robotic Arm
  • 2、Why i can't robotic arm start normally
  • 3、When you control the No.1 servo, all servos will turn.
  • 4、How open VNC service on DOFBOT system
  • 5、Where can I get the URDF file?
  • 6、Gesture recognition cannot work normally.
  • 7、Write system image into U disk
  • 8、Write EMMC boot file

Welcome to DOFBOT-JetsonNANO repository

3.Docker images deeply understand and publish images

3、Docker images deeply understand and publish images

 

3、Docker images deeply understand and publish images3.1、image understanding3.2、UnionFS(Federated file systems)3.3、image layering3.3.1、hierarchical understanding3.3.2、Docker images should use layering benefits3.4、Make and publish images3.4.1、Make an image3.4.2、Publish the image

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

3.1、image understanding

  1. An image is a lightweight, executable stand-alone software package that contains everything needed to run a piece of software. We package applications and configurations into a ready, deliverable, deployable runtime environment, including code, libraries, environment variables and configuration files required for runtime, and this large package runtime environment is the image image file.

  2. Docker container instances can only be generated through image files.

3.2、UnionFS(Federated file systems)

1.Union file system (UnionFS) is a hierarchical, lightweight, high-performance file system, it is the basis of docker images, and supports the modification of the file system as a commit to overlay layer by layer, while different directories can be mounted under the same virtual file system.

2.The image can be inherited through layering, and based on the basic image, various specific application images can be made.

Features of the Union file system: load multiple file systems at the same time, but from the outside, only one file system can be seen; Federated loading overlays the layers of file systems so that the final file system contains files and directories for all layers.

3.3、image layering

When downloading an image, pay attention to the downloaded log output, you can see that it is downloading layer by layer:

image-20230424154810939

# The way to view image layering can be done with the command: docker image inspect image name
jetson@ubuntu:~$ docker image inspect mysql:latest
[
    {
        "Id": "sha256:5371f8c3b63eec64a33b35530be5212d6148e0940111b57b689b5ba1ffe808c8",
        .........
        "RootFS": {
            "Type": "layers",
            "Layers": [
                "sha256:d6d4fc6aef875958d6186f85f03d88e6bb6484ab2dd56b30a79163baceff2f6d",
                "sha256:05c3b0b311a02bc56ca23105a76d16bc9b8c1d3e6eac808f4efb1a2e8350224b",
                "sha256:7b80f7f05642477ebc7d93de9539af27caab7c41a768db250fe3fe2b5506ca2c",
                "sha256:50e037faefab22cb1c75e60abb388b823e96a845650f3abd6d0a27e07a5a1d5e",
                "sha256:66040abb3f7201d2cc64531349a8225412db1029447a9431d59d999c941d56f6",
                "sha256:857162425652837a362aa5f1c3d4974cc83702728793de52ba48176d5367a89b",
                "sha256:7eebed3016f6b6ab68aa8e6be35f0689a3c18d331b7b542984a0050b859eaf26",
                "sha256:2fc4c142633d57d795edc0f3fd457f99a35fa611eab8b8c5d75c66e6eb729bc2",
                "sha256:7fde2d12d484f0c14dabd9ca845da0bcdaf60bd773a58ca2d73687473950e7fe",
                "sha256:9319848a00d38e15b754fa9dcd3b6e77ac8506850d32d8af493283131b9745a3",
                "sha256:5ff94d41f068ea5b52244393771471edb6a9a10f7a4ebafda9ef6629874a899b"
            ]
        },
        "Metadata": {
            "LastTagTime": "0001-01-01T00:00:00Z"
        }
    }
]

3.3.1、hierarchical understanding

  • All docker images start from a base image layer, and when modifications or additions are made, a new image layer will be created on top of the current image layer.

  • For a simple example, if a new image is created based on Ubuntu 20.04, this is the first layer of the new image; If you add a Python package to the image, a second image layer is created on top of the base image layer; If you continue to add a security patch, a third mirror layer is created.

  • Docker images are all read-only, and when the container starts, a new writable layer is loaded on top of the image! This layer is what we usually call the container layer, and what is under the container is called the image layer!

3.3.2、Docker images should use layering benefits

Resource sharing, for example, if there are multiple images built from the same base image, then the host only needs to keep a base image on disk, and only one base image needs to be loaded in memory, so that all containers can be served, and each layer of the image can be shared.

3.4、Make and publish images

3.4.1、Make an image

Method 1: Submit an image from the container:

​x
# command
docker commit -m="description of commit" -a="author" container id Target image name to be created: [tag name] [You can also omit the -m -a parameter]

# Test
jetson@ubuntu:~$ docker ps -a
CONTAINER ID   IMAGE           COMMAND       CREATED       STATUS                   PORTS     NAMES
c54bf9efae47   ubuntu:latest   "/bin/bash"   3 hours ago   Up 24 minutes                      funny_hugle
3b9c01839579   hello-world     "/hello"      3 hours ago   Exited (0) 3 hours ago             jovial_brown
jetson@ubuntu:~$ docker commit c54bf9efae47 ubuntu:1.0
sha256:78ca7be949b6412f74ba12e8d16bd548aaa7c3fa25134326db3a67784f848f8f
jetson@ubuntu:~$ docker images   # Generated the ubuntu:1.0 image
REPOSITORY                   TAG       IMAGE ID       CREATED         SIZE
ubuntu                       1.0       78ca7be949b6   5 seconds ago   69.2MB
yahboomtechnology/ros-foxy   3.4.0     49581aa78b6b   5 hours ago     24.3GB
yahboomtechnology/ros-foxy   3.3.9     cefb5ac2ca02   4 days ago      20.5GB
yahboomtechnology/ros-foxy   3.3.8     49996806c64a   4 days ago      20.5GB
yahboomtechnology/ros-foxy   3.3.7     8989b8860d17   5 days ago      17.1GB
yahboomtechnology/ros-foxy   3.3.6     326531363d6e   5 days ago      16.1GB
ubuntu                       latest    bab8ce5c00ca   6 weeks ago     69.2MB
hello-world                  latest    46331d942d63   13 months ago   9.14kB

Method 2: Make an image from a dockerfile:

xxxxxxxxxx
# command
docker build -f dockerfile file path -t new image name: TAG .  # The docker build command has a .     Represents the current directory
# Test
docker build -f dockerfile-ros2 -t yahboomtechnology/ros-foxy:1.2 .

For more information on writing dockerfiles, please refer to: https://docs.docker.com/develop/develop-images/dockerfile_best-practices/

3.4.2、Publish the image

The docker repository is a centralized storage place for image files. The largest public repository is Docker Hub (https://hub.docker.com/), which houses a huge number of images for users to download. Domestic public warehouses include Alibaba Cloud, NetEase Cloud, etc.

Steps to publish the image to docker hub:

  1. Address: https://hub.docker.com/, register an account first

  2. Ensure that the account can log in normally

image-20230424151714735

  1. Use the tag command to modify the image name

The specification for publishing an image to docker hub is:

xxxxxxxxxx
docker push registration username/image name

For example, my registered username here is: pengan88, then you must first change the image name

xxxxxxxxxx
# Command:
The name of the image after the docker tag image ID is modified

# Test
jetson@ubuntu:~$ docker images
REPOSITORY                   TAG       IMAGE ID       CREATED         SIZE
ubuntu                       1.0       78ca7be949b6   5 seconds ago   69.2MB
ubuntu                       latest    bab8ce5c00ca   6 weeks ago     69.2MB
hello-world                  latest    46331d942d63   13 months ago   9.14kB
jetson@ubuntu:~$ docker tag 78ca7be949b6 pengan88/ubuntu:1.0
jetson@ubuntu:~$ docker images
REPOSITORY                   TAG       IMAGE ID       CREATED          SIZE
pengan88/ubuntu              1.0       78ca7be949b6   23 minutes ago   69.2MB
ubuntu                       1.0       78ca7be949b6   23 minutes ago   69.2MB
ubuntu                       latest    bab8ce5c00ca   6 weeks ago      69.2MB
hello-world                  latest    46331d942d63   13 months ago    9.14kB
  1. Log in to Docker Hub to publish the image:

xxxxxxxxxx
jetson@ubuntu:~$ docker login -u pengan88
Password:     # Enter the password of the account registered by Docker Hub here
WARNING! Your password will be stored unencrypted in /home/jetson/.docker/config.json.
Configure a credential helper to remove this warning. See
https://docs.docker.com/engine/reference/commandline/login/#credentials-store

Login Succeeded
jetson@ubuntu:~$ docker push pengan88/ubuntu:1.0
The push refers to repository [docker.io/pengan88/ubuntu]
ca774712d11b: Pushed
874b048c963a: Mounted from library/ubuntu
1.0: digest: sha256:6767d7949e1c2c2adffbc5d3c232499435b95080a25884657fae366ccb71394d size: 736

5、Visit Docker Hub to see that it has been successfully released

image-20230424152530278

Common commands for docker image containers
Docker hardware interaction and data processing

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