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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

9. Multimodal Large Model+Color Blocks Back In Place

Multimodal Large Model+Color Blocks Back In Place(Voice Version)

Before running the function, you need to close the App and large programs. For the closing method, refer to [4. Preparation] - [1. Manage APP control services].

1. Function Description

After the program runs, wake up the voice module and input the command to remember the current positions of color blocks. After the program completes recording, scramble the positions of the color blocks. Then wake up the voice module again and input the command to end the current recording task. Then wake up the voice module again and input the color block return command. After thinking, the large model will plan an action list, and the program will control the robotic arm to complete the color block return task according to the action functions in the action list.

2. Startup

After entering the Docker container, enter the following commands in the Docker terminal.

xxxxxxxxxx
ros2 launch largemodel largemodel_control.launch.py

After waking up the voice module, input by voice:

xxxxxxxxxx
Remember the current positions of the color blocks

After recording the positions of the color blocks, wake up the voice module again and input by voice:

x
End the current recording task

Then you can scramble the positions of the color blocks. After scrambling, wake up the voice module and input by voice:

x
Return the blocks on the desktop to their original positions

After the buzzer beeps once, it will start gripping the color blocks and returning them to their original positions according to the order recorded in the program. If there are other color blocks on top of the current color block that needs to be returned, it will first remove the color blocks on top, then grip and return to the original position.

3. Task Planning

3.1. Record Current Positions of Color Blocks

  1. Call seewhat() to observe the environment;
  2. Call seewhat() again to observe the environment, then determine which color block is the top block;
  3. If there are two or more piles of color blocks in the environment, select and execute the following content based on whether a color block is a single one. Calculate for each pile of color blocks. Only one action function can be output at a time here:
    (1) If it is a single color block, pass the outer frame coordinates of the color block into the function compute_pose(x1, y1, x2, y2, 'name'), where name represents the color of the color block, and its value can be red, blue, green, or yellow;
    (2) If it is not a single color block, pass the outer frame coordinates of the top color block into the function compute_pose_order(x1, y1, x2, y2, 'name', 'order'), where name represents the color of the color block (with values red, blue, green, or yellow), and order represents the top-to-bottom arrangement order of the color blocks. Its value is a string composed of the initial letters of the colors of each layer of color blocks from top to bottom. For example, if the top-to-bottom arrangement order of the color blocks is red, blue, green, yellow, then the value of order is rbgy, and so on.
  4. If there is only one pile of color blocks, pass the outer frame coordinates of the top color block into the function compute_pose_order(x1, y1, x2, y2, 'name', 'order'), where name represents the color of the color block (with values red, blue, green, or yellow), and order represents the top-to-bottom arrangement order of the color blocks. Its value is a string composed of the initial letters of the colors of each layer of color blocks from top to bottom. For example, if the top-to-bottom arrangement order of the color blocks is red, blue, green, yellow, then the value of order is rbgy, and so on.

3.2. Color Block Return

  1. First, call the color_back_to_orin() function to control the color blocks to return to their original positions.
  2. Call the check_remove(color) function to check whether the target color block exists in the removal list (where color is the color of the target color block, and its value range is red, blue, green, or yellow).
  3. If the target color block exists in the removal list, perform the following operations:
    (1) Call the grasp_from_rm_list(color) function to grasp the target color block from the removal list (the meaning of the color parameter is the same as above);
    (2) Pass the color of the target color block as a parameter into the return_to_orin(color) function. For example, when the target color block is red, call return_to_orin(red) (the value range of the color parameter is the same as above).
  4. If the target color block does not exist in the removal list, follow the steps below to put the color block back to its original position (only one action function can be output at a time):
    (1) Call the seewhat() function to observe the environment;
    (2) Determine whether there are other color blocks on top of the target color block that needs to be returned to its original position;
    (3) If there are no other color blocks on top of the target color block that needs to be returned to its original position, perform the following steps: ①Call the grasp_obj(x1, y1, x2, y2) function to grasp the target color block that needs to be returned to its original position (where (x1, y1, x2, y2) are the border coordinates of the top surface of the target color block that needs to be returned to its original position);
    ②Pass the color of the target color block as a parameter into the return_to_orin(color) function (the value range of the color parameter is the same as above).
    (4) If there are other color blocks on top of the target color block that needs to be returned to its original position, perform the following steps:
    ①Call the remove_obj(x1, y1, x2, y2, color) function to remove the color block (where (x1, y1, x2, y2) are the border coordinates of the top surface of this color block, and color is the color of this color block, with a value range of red, blue, green, or yellow);
    ②Call the seewhat() function to observe the environment;
    ③If there are no other color blocks above the target color block that needs to be returned to its original position, call the grasp_obj(x1, y1, x2, y2) function to grasp the target color block (where (x1, y1, x2, y2) are the border coordinates of the top surface of the target color block); ④Pass the color of the target color block as a parameter into the return_to_orin(color) function (the value range of the color parameter is the same as above).

4. Core Code Analysis

You can refer to the content in 4. Core Code Analysis from tutorial [18. AI Model - Text Version] - [Multimodal Large Model+Color Blocks Back In Place]. The voice version and text version have the same action functions, only the task command input method is different.

Multimodal Large Model+robotic arm handling
Multimodal Large Model + Intent Prediction + Robotic Arm Gripping

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