yahboom
HomeLearn
Solution
Blog
Online Store
About Us
EN/中文
DOFBOT-Pi📖
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
  • System Image
  • VM File
  • PC software
  • Instruction manual
  • More Annex
  • Tools
Download ZIP
FAQ
  • 1、Precautions for DIY Robotic Arm
  • 2、Where i can to check Raspberry Pi 4 Version tutorials?
  • 3、When you control the No.1 servo, all servos will turn.
  • 4、Dofbot start up process video
  • 5、Gesture recognition cannot work normally
  • 6、How to write image file into SD card
  • 7、How open VNC service on DOFBOT system
  • 8、Where can I get the URDF file?

Welcome to DOFBOT-Pi 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

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

xxxxxxxxxx
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.
  • 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:

x
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

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 red block in my hand

image-20251128162857520

First, the robotic arm will adjust to the tracking posture, then acquire the current frame image to find the red block in hand, then control the robotic arm to track the red block. Slowly move the position of the red block, 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 seewhat() to observe the environment and locate the red block in your hand.
  2. Call track_object() to start tracking the red block.
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

COMPANY

  • About us
  • Team Style
  • Blog

LEARNING & EXPLORATION

  • Tutorials
  • Edge AI hardware
  • Educational robots
  • Embedded intelligent hardware

WHERE TO BUY

  • Yahboom Online Store
  • Amazon
  • Alibaba
  • AliExpress
  • Mercado

TECHNOLOGY SUPPORT

  • Email: support@yahboom.com

BE A DISTRIBUTOR

  • Email: sales@yahboom.com
QR Code

Copyright © 2017-2026 Yahboom. All Rights Reserved.

EN/中文