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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

6. Multimodal Large Model+robotic arm tracking

Multimodal Large Model + Robotic Arm Tracking(Text 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, you can input robotic arm tracking object commands through the terminal. According to the type of object to be tracked, the large model will start external programs to control the robotic arm to track the target object.

2. Startup

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

ros2 launch largemodel largemodel_control.launch.py text_chat_mode:=True

Then open a second terminal and input the following command:

xxxxxxxxxx
ros2 run text_chat text_chat

Robotic arm tracking has two types of objects:

  • Track machine code: Track machine code x, where x is the machine code ID, values are 1-4, for example, track machine code 1.Wood blocks used: 40x40x40mm wood blocks.
  • Track other objects: Track any object, for example, track the small yellow duck on the green wooden block.

2.1. Track Machine Code

2.1.1. Startup

Input in the text_chat terminal:

xxxxxxxxxx
Track machine code 2

image-20251128153203859

First, the robotic arm will adjust to the tracking posture, then acquire the current image and start recognizing and tracking the target machine code. If the recognized machine code is not 2, the robotic arm will not track and will print "Not target apriltag" in the terminal; if the recognized machine code is 2, the robotic arm will start tracking, moving the machine code slowly, and the robotic arm will follow the movement. If you want to exit tracking, input in the text_chat terminal: Cancel tracking.

image-20251128153713286

2.1.2. Task Planning
  1. Call track_pose() function to adjust the robotic arm posture to tracking posture;
  2. Call apriltag_follow(2) function, where the passed parameter is 2, representing the ID of the machine code to be tracked.
2.1.3. Core Code Analysis

track_pose() function, source code located at: LargeModel_ws/src/largemodel/largemodel/action_service.py

xxxxxxxxxx
def track_pose(self):  # Robotic arm tracking posture
    #Directly communicate with the underlying control board, send values for 6 robotic arm servos
    Arm.Arm_serial_servo_write6(90,150,12,20,90,30,1000)
    time.sleep(3.0)
    if not self.combination_mode and not self.interrupt_flag:
        self.action_status_pub("track_pose_done")

apriltag_follow function, source code located at: LargeModel_ws/src/largemodel/largemodel/action_service.py

xxxxxxxxxx
def apriltag_follow(self,target_id):
    target_idf = float(target_id)
    #Open a terminal, run machine code tracking program, pass parameter target_id, representing the machine code ID to be tracked
    cmd1 = f"ros2 run largemodel_arm apriltag_follow_2D --ros-args -p target_id:={target_idf:.1f}"
    subprocess.Popen(
        [
            "gnome-terminal",
            "--title=apriltag_follow",
            "--",
            "bash",
            "-c",
            f"{cmd1}; exec bash",
        ]
    )    

Machine code tracking program apriltag_follow_2D, source code path at: LargeModel_ws/src/largemodel/largemodel/action_service.py

xxxxxxxxxx
#Get target tracking id through command line parameters
self.declare_parameter('target_id', 0.0)
self.TargetID = int(self.get_parameter('target_id').get_parameter_value().double_value)
#In the image callback function callback
cur_id = tags[0].tag_id
#If the currently recognized id is the target tracking id
if cur_id == self.TargetID:
    #Get the center point coordinates of the machine code
    center_x, center_y = tags[0].center
    #Determine if the center point coordinates are within 10 pixels error of the image center
    if (abs(center_x-320) >10 or abs(center_y-240)>10) and self.XY_Track_flag==True:
        #Call XY_track function for tracking, passing the current center point coordinates
        self.XY_track(center_x,center_y)  
        print("Tracking")
        print("-------------------------------------")
    else:
        print("Not target apriltag.")

2.2. Track Other Objects

2.2.1. Startup

Input in the text_chat terminal:

xxxxxxxxxx
Track the small yellow duck in hand

image-20251128162857520

First, the robotic arm will adjust to the tracking posture, then acquire the current frame image to find the small yellow duck in hand, then control the robotic arm to track the small yellow duck. Slowly move the position of the small yellow duck, and the robotic arm will follow the movement. If you want to exit tracking, input in the text_chat terminal: Cancel tracking.

2.2.2. Task Planning
  1. Call function track_pose() to adjust the robotic arm to tracking posture;
  2. Call seewhat() to observe the environment and find the small duck in hand;
  3. Call function track(x1, y1, x2, y2), where x1, y1, x2, y2 are the outer border coordinates of the small yellow duck.
2.2.3. Core Code Analysis

track_pose() and seehat() functions have been analyzed before and will not be repeated here. Let's mainly look at the track function, which source code is located at: LargeModel_ws/src/largemodel/largemodel/action_service.py

xxxxxxxxxx
#x1,y1,x2,y2: Object outer border coordinates
def track(self, x1, y1, x2, y2):
    #Start KCF tracking program
    cmd1 = "ros2 run largemodel_arm KCF_Track_Move"
    #Start KCF tracking positioning program
    cmd2 = "ros2 run largemodel_arm ALM_KCF_Tracker"
    subprocess.Popen( 
        [
            "gnome-terminal",
            "--title=KCF_track",
            "--",
            "bash",
            "-c",
            f"{cmd1}; exec bash",
        ]
    )
    subprocess.Popen(
        [
            "gnome-terminal",
            "--title=ALM_KCF_Tracker_Node",
            "--",
            "bash",
            "-c",
            f"{cmd2}; exec bash",
        ]
    )
    #Publish outer border coordinate information topic data, ALM_KCF_Tracker node will subscribe to this topic
    x1 = int(x1)
    y1 = int(y1)
    x2 = int(x2)
    y2 = int(y2)
    self.object_position_pub.publish(Int16MultiArray(data=[x1, y1, x2, y2]))

KCF_Track_Move program, this program source code location: LargeModel_ws/src/largemodel_arm/largemodel_arm/KCF_Track_Move.py

​x
#Import library, this library path is LargeModel_ws/src/largemodel_arm/largemodel_arm/Dofbot_Track.py
from largemodel_arm.Dofbot_Track import *
​
#Create robotic arm tracking gripping object
self.dofbot_tracker = DofbotTrack()
​
#Create subscriber, subscribe to object position information topic (xy)
self.sub_grasp_status = self.create_subscription(Position,"/pos_xy",self.TrackAndGrap,100)
#Topic callback function, handle received topic message data,
def TrackAndGrap(self,position):  
    center_x, center_y = position.x,position.y
    #If the current object's center point coordinates are not within 8 pixels error range of the image center
    if abs(center_x-320) >8 or abs(center_y-240)>8 :
        #Call XY_track function for tracking, passing the current center point xy coordinates
        self.dofbot_tracker.XY_track(center_x,center_y) 

 

xxxxxxxxxx
#View XY_track function in Dofbot_Track.py, calculate values for servos 1-4 based on center point coordinates and PID parameters
def XY_track(self,center_x,center_y):
    self.px = center_x
    self.py = center_y
    if not (self.target_servox>=180 and center_x<=320 and self.a == 1 or self.target_servox<=0 and center_x>=320 and self.a == 1):
        if(self.a == 0):
            self.xservo_pid.SystemOutput = center_x
            if self.x_out_range == True:
                if self.target_servox<0:
                    self.target_servox = 0
                    self.xservo_pid.SetStepSignal(630)
                if self.target_servox>0:
                    self.target_servox = 180
                    self.xservo_pid.SetStepSignal(10)
                self.x_out_range = False
            else:
                self.xservo_pid.SetStepSignal(320)
                self.x_out_range = False
           
            self.xservo_pid.SetInertiaTime(0.01, 0.1)
            
            target_valuex = int(1500 + self.xservo_pid.SystemOutput)
            
            self.target_servox = int((target_valuex - 500) / 10) -10
    
            if self.target_servox > 180:
                self.x_out_range = True
                
            if self.target_servox < 0:
                self.x_out_range = True
                        
    if not (self.target_servoy>=180 and center_y<=240 and self.b == 1 or self.target_servoy<=0 and center_y>=240 and self.b == 1):
        if(self.b == 0):
            self.yservo_pid.SystemOutput = center_y
​
            if self.y_out_range == True:
                self.yservo_pid.SetStepSignal(450)
                self.y_out_range = False
            else:
                self.yservo_pid.SetStepSignal(240)
​
            self.yservo_pid.SetInertiaTime(0.01, 0.1)
           
            target_valuey = int(1500 + self.yservo_pid.SystemOutput)
            
            if target_valuey<=1000:
                target_valuey = 1000
                self.y_out_range = True
            self.target_servoy = int((target_valuey - 500) / 10) - 55
            if self.target_servoy > 180: self.target_servoy = 180 
            if self.target_servoy < 0: self.target_servoy = 0 
​
            joint2 = 120 + self.target_servoy
            joint3 =  self.target_servoy / 4.5
            joint4 =  self.target_servoy / 3
    joints_0 = [float(self.target_servox/1), float(joint2), float(joint3), float(joint4), 90.0, 30.0]
    self.Arm.Arm_serial_servo_write6_array(joints_0,1000)
    self.cur_joints = joints_0

 

Multimodal Large Model+robotic arm gripping
Multimodal Large Model+robotic arm sorting

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