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DOFBOT Pro📖
1. Get started quickly
  • 0.Unboxing introduction video
  • 1. First Trial
  • 2. Analysis of common problems
  • 3. Calibration offset
  • 4. Jetpack old system upgraded to SUPER system
  • 5. Precautions for DIY Robotic Arm
  • 6. Jetson Nano Version – Must Read
  • 7.Robotic arm gripping anomaly solutions
2. Assembly course
  • 1. Installation video
  • 2. Calibrate the Robotic Arm
  • 3. Install the map
3. Remote control course
  • 1. Mobile APP control
  • 2. USB handle control
  • 3. PC web page programming
4. Preparation
  • 1. Manage APP control services
  • 2. Manage JupyterLab services
  • 3. Manage OLED services
  • 4. Modifying ROS_DOMAIN_ID for Multi-Machine
5. 2D visual tracking course
  • 1. Color block positioning
  • 2. Color block tracking
  • 3. Face positioning
  • 4. Face tracking
  • 5. Label code positioning
  • 6. Label code tracking
6. 2D visual grabbing course
  • 1. Identify grab color block
  • 2. Catch game
  • 3. Color sorting
  • 4. Color stacking
  • 5. Label code sorting
  • 6. Label code stacking
  • 7. Identify gesture grab color block
  • 8. Gesture stacking
  • 9.1 Single garbage sorting(Jetson-Nano)
  • 9.2 Single garbage sorting(Orin)
  • 10.1 Garbage sorting(Jetson-Nano)
  • 10.2 Garbage sorting(Orin)
  • 11. Tracking game
7. 2D Mediapipe course
  • 1. Hand detection
  • 2. 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
  • 14.1 Robotic arm tracks the palm(Jetson-Nano)
  • 14.2 Robotic arm tracks the palm(Orin)
  • 15. Fingertip trajectory recognition
  • 16. Fingertip trajectory control robotic arm
  • 17. Gesture Grasp and Release Objects
  • 18. Finger Control Robot Arm
8. 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
9. 2D Voice Control Course
  • 1.Simple Voice Control
  • 2.Smart Voice Control
  • 3.Voice Action Control
  • 4.Memory Learning Mode
  • 5.Color Recognition with Voice Broadcast
  • 6.Color Recognition and Grasping with Voice Broadcast
  • 7.Color Recognition and Sorting with Voice Broadcast
  • 8.Voice Recognition Target Tracking
  • 9.Voice Control Stacking
  • 10.Voice Control Color Sorting
  • 11.1 Garbage Recognition with Voice Broadcast(Jetson Nano)
  • 11.2 Garbage Recognition with Voice Broadcast(Orin)
  • 12.1 Garbage Sorting with Voice Broadcast(Jetson Nano)
  • 12.2 Garbage Sorting with Voice Broadcast(Orin)
10. 3D Depth Camera Course
  • 1. Dabai_DCW2 camera
  • 2. Deep Pseudo-Color Image
  • 3. Depth camera ranging
  • 4. Color block volume measurement
11. 3D Robotic Arm Control and Inverse Kinematics
  • 1. Robotic arm ROS control
  • 2. Inverse solution
12. 3D Sorting and Grasping Course
  • 1. AprilTag ID sorting
  • 2. AprilTag abnormal height sorting
  • 3. MediaPipe Gesture-AprilTag ID sorting
  • 4. MediaPipe Gesture-AprilTag height sorting
  • 5. MediaPipe Gesture-AprilTag distance sorting
  • 6. Block color sorting
  • 7. Block abnormal height sorting
  • 8. Block different shape sorting
  • 9. Block shape abnormal height sorting
  • 10. Yolov11 garbage sorting(Orin)
13. 3D Tracking and Grasping Course
  • 1. AprilTag Tracking and Grasping
  • 2. Color Block Tracking and Grasping
  • 3. KCF Object Tracking and Grasping
14. 3D Voice Control for Grasping Course (Sorting & Tracking)
  • 1. Voice Control AprilTag ID Sorting
  • 2. Voice Control Remove Height Abnormal AprilTag
  • 3. Voice Control Tracking and Grasping AprilTag
  • 4. Voice Control Color Block Sorting
  • 5. Voice Control Remove Height Abnormal Color Block
  • 6. Voice Control Tracking and Grasping Color Block
  • 7. Voice Control Yolov11 Garbage Recognition Sorting
  • 8. Voice Control Object Tracking and Grasping
15. 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
16. 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
17. 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
18. 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
19. 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
20. MoveIt 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
21. YOLOv11 Development Course (ORIN)
  • 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. Dataset Annotation
  • 10. Model Training_Conversion
  • 11. Model Prediction
22. YOLOv11 Development Course (Jetson Nano)
  • 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.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
24. Basic Control Course
  • 1. Control RGB Light
  • 2. Control Buzzer
  • 3. Control Single Servo
  • 4. Read Servo Angle
  • 5. Control All Servos
  • 6. DOFBOT Up Down Left Right Movement
  • 7.DOFBOT dancing
  • 8. DOFBOT Memory Actions
  • 9. DOFBOT Grab Blocks
  • 10. Porter
  • 11. Stack Blocks
  • 12. Two DOFBOT Synchronized Actions
25. Jetson Board Basic Course
  • 1. Jetson board Introduction
  • 2. Write system image
  • 3. Re-write system image
  • 4. System startup
  • 5. System and desktop introduction
  • 6. System expansion
  • 7. Network Configuration
  • 8. SSH remote login and file transfer
  • 9. VNC Remote Login
  • 10. System backup
  • 11. Increased swap space
  • 12. Use of Jtop
26. GPIO Hardware Control Course
Jetson Nano
  • 1.GPIO library API usage
  • 2. Hardware library configuration
  • 3. Pin reading function
  • 4. Pin level output control
  • 5. Control LED
  • 6. External device serial communication
  • 7.I2C communication
Jetson Orin Nano_NX
  • 1.GPIO description
  • 2.GPIO library installation
  • 3.GPIO reading
  • 4.GPIO output
  • 5.Serial communication
  • 6.I2C communication
27. OpenCV 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. 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
28. Visual Basics Course
  • 1. Camera display
  • 2. Color Calibration
  • 3. Color recognition
  • 4. Face Detection
  • 5.1 Garbage Identification(Jetson-Nano)
  • 5.2 Garbage Identification(Orin)
  • 6.1 Object Recognition(Jetson-Nano)
  • 6.2 Object Recognition(Orin)
  • 7. Label code recognition
  • 8.PID algorithm basics
29. 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
30. Visual Advanced Course
  • 1.QR code
  • 2.Human pose estimation
  • 3.Target Detection
  • 4.AR vision
  • 5.ROS+Opencv basics
  • 6.AR QR code
  • 7.Face recognition
  • 8.harris corner detection
  • 9.Target tracking algorithm
  • 10.Contour moment
  • 11.Polygon outline
  • 12.Discrete fourier transform algorithm
  • 13.Edge detection algorithm
  • 14.Face detection algorithm
  • 15.Optical flow detection algorithm
  • 16.Contour detection
  • 17.General contour detection
  • 18.Feature point tracking
  • 19.HLS color filtering
  • 20.Hough circle detection
  • 21.Hough linear detection
  • 22.HSV color filtering
  • 23.LK optical flow algorithm
  • 24.detection algorithm
  • 25.Phase dependent displacement detection
  • 26.Image pyramid sampling algorithm
  • 27.RGB color filtering
  • 28.Clear background detection algorithm
  • 29.Simplified optical flow algorithm
  • 30.Simple filter
  • 31.Threshold image processing
  • 32.Watershed segmentation algorithm
31. Jupyter Lab Usage Course
  • 1. Read the document before operation
  • 2. Jupyter lab environment construction tutorial
  • 3. Basic tutorial of Jupyter lab
  • 4. Jupyter widgets configuration tutorial
  • 5. Getting Started Tutorial for JupyterWidgets Controls
Download
  • APP(Android/iOS)
  • SystemFile_OrinNANOSUPER
  • SystemFile_OrinNXSUPER
  • SystemFile_NANO4GB_SUB
  • VM_File
  • Code
  • Instruction_Manual
  • Hardware_Info
  • 3D_Model_File
  • ROS2_Basic_VM
  • Tools
Download ZIP

Welcome to DOFBOT Pro 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.Wood blocks used: 30x30x30mm wood blocks.

2. Startup

Users with Jetson-Nano mainboard version need to enter the docker container first and then input the following command. Users with Orin mainboard can directly open the terminal and input the following command:

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:

xxxxxxxxxx
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:

xxxxxxxxxx
Put the blocks on the desktop back 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 color block;

  3. If there are two or more piles of color blocks in the environment, based on whether they are individual color blocks, choose to execute the following content, calculating each pile of color blocks. Here only one action function can be output at a time:

    • If it is an individual color block, pass the outer bounding box coordinates of the color block to the function compute_pose(x1, y1, x2, y2, 'name'), where name represents the color of the color block, with values red, blue, green or yellow;
    • If it is not an individual color block, pass the outer bounding box coordinates of the top color block to 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, order represents the arrangement order of color blocks from top to bottom, with the value being a string composed of the first letters of each layer's color from top to bottom. For example, if the arrangement order of color blocks from top to bottom 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 bounding box coordinates of the top color block to 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, order represents the arrangement order of color blocks from top to bottom, with the value being a string composed of the first letters of each layer's color from top to bottom. For example, if the arrangement order of color blocks from top to bottom is: red, blue, green, yellow, then the value of order is rbgy, and so on.

3.2. Color Block Return

  1. First, call color_back_to_orin() function to control color blocks to return to original positions;
  2. Call check_remove(color) function to check if the target color block exists in the removal list (where color is the color of the target color block, with values red, blue, green or yellow);
  3. If the target color block exists in the removal list, perform the following operations:
    (1) Call grasp_from_rm_list(color) function to grip the target color block from the removal list (meaning of color parameter is the same as above);
    (2) Pass the color of the target color block as a parameter to the return_to_orin(color) function, for example when the target color block is red, call return_to_orin(red) (range of color parameter is the same as above);
  4. If the target color block does not exist in the removal list, the color block needs to be returned to its original position according to the following steps (only output one action function at a time):
    (1) Call seewhat() function to find the current target color block that needs to be returned;
    (2) Call seewhat() function to determine if there are other objects on top of the current target color block that needs to be returned;
    (3) If there are no other color blocks on top of the current target color block that needs to be returned, perform the following steps:
    ① Call grasp_obj(x1, y1, x2, y2) function to grip the current target color block that needs to be returned (where (x1, y1, x2, y2) are the top surface bounding box coordinates of the current target color block that needs to be returned);
    ② Pass the color of the target color block as a parameter to the return_to_orin(color) function (range of color parameter is the same as above);
    (4) If there are other color blocks on top of the current target color block that needs to be returned, perform the following steps:
    ① Call remove_obj(x1, y1, x2, y2, color) function to remove this color block (where (x1, y1, x2, y2) are the top surface bounding box coordinates of this color block, color is the color of this color block, with values red, blue, green or yellow);
    ② Call seewhat() function to observe the environment;
    ③ If there are no other color blocks on top of the current target color block that needs to be returned, call grasp_obj(x1, y1, x2, y2) function to grip the target color block (where (x1, y1, x2, y2) are the top surface bounding box coordinates of the target color block);
    ④ Pass the color of the target color block as a parameter to the return_to_orin(color) function (range of color parameter is the same as above)

4. Core Code Analysis

You can refer to the content in 4. Core Code Analysis from tutorial [17. 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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