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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

8. Multimodal Large Model+robotic arm handling

Multimodal Large Model + Robotic Arm Color Block Handling(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 color block handling commands through the terminal. The large model will plan the actions to complete the color block handling, then the program will control the robotic arm to handle the color blocks in sequence.

The wooden blocks used in this lesson: 30x30x30mm blocks.

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

Then input color block handling commands in the terminal. You can refer to the following example:

xxxxxxxxxx
Place the green block on the desktop above the red block

The placement directions can be front, back, left, right, or top - five directions in total.

image-20251129110951802

The program will first take a photo to get the positions of the green block and red block; then it will control the robotic arm to grip the green block and place it above the red block.

3. Task Planning

  1. Call seewhat() to observe the environment and get the positions of the green block and red block on the desktop.
  2. Call the change_pose(x1, y1, x2, y2, x3, y3, x4, y4, 'green', 'red', 5) function to place the green block above the red block. Where (x1, y1, x2, y2) are the top outer border coordinates of the green block, (x3, y3, x4, y4) are the top outer border coordinates of the red block, and 5 means above.

4. Core Code Analysis

4.1. change_pose Function

Source code path: LargeModel_ws/src/largemodel/largemodel/action_service.py

xxxxxxxxxx
#Parameter description
#x1,y1,x2,y2: Top-left and bottom-right coordinates of the outer border of the color block to be gripped, in the above example it's the outer border coordinates of the green block
#x3,y3,x4,y4: Top-left and bottom-right coordinates of the outer border of the color block to be placed, in the above example it's the outer border coordinates of the red block
#src: Color of the block to be gripped, in the above example it's the green block
#tar: Color of the block to be placed, in the above example it's the red block
#side: Placement direction, front, back, left, right, top correspond to values 1, 2, 3, 4, 5 respectively, in the above example it's 5, meaning top
def change_pose(self, x1, y1, x2, y2,x3,y3,x4,y4,src,tar,side):
    #Extract the color of the block to be gripped
    src_color = src.strip("'\"")  
    self.cur_down_name = src_color
    #Create a key in the dictionary
    self.cur_down_pose[self.cur_down_name] = []
    #Start the handling program
    cmd1 = "ros2 run largemodel_arm Get_Target_Pose_KCF"
    subprocess.Popen(
        [
            "gnome-terminal",
            "--title=grasp_desktop",
            "--",
            "bash",
            "-c",
            f"{cmd1}; exec bash",
        ]
    )
    time.sleep(3.0)
    #Publish outer border coordinate information and placement direction topic data, Get_Target_Pose_KCF node will subscribe to this topic
    x1 = int(x1)
    y1 = int(y1)
    x2 = int(x2)
    y2 = int(y2)
    x3 = int(x3)
    y3 = int(y3)
    x4 = int(x4)
    y4 = int(y4)
    side = int(side)
    time.sleep(5.0)       
    self.object_position_pub.publish(Int16MultiArray(data=[x1, y1, x2, y2,x3,y3,x4,y4,side])

4.2. Get_Target_Pose_KCF Handling Program

Source code path: LargeModel_ws/src/largemodel_arm/largemodel_arm/Get_Target_Pose_KCF.py

xxxxxxxxxx
#Create subscriber, subscribe to corner_xy topic content
self.xy_subscription = self.create_subscription(Int16MultiArray,'corner_xy',self.GetXYCallback,qos_profile=1)
#Create publisher, publish topic of robotic arm placement posture angle values
self.down_joints_pub = self.create_publisher(CurJoints,'/down_joints',1)
#GetXYCallback callback function, handles received topic message data
def GetXYCallback(self,msg):
    print("msg: ",msg.data)
    #Get outer border coordinates of the block to be gripped
    self.Roi_init_src = (msg.data[0],msg.data[1],msg.data[2],msg.data[3])
    #Get outer border coordinates of the block to be placed
    self.Roi_init_tar = (msg.data[4],msg.data[5],msg.data[6],msg.data[7])
    #Get placement direction data
    self.side = msg.data[8]
    self.Track_state = 'identify'
    self.get_xy = True
    time.sleep(2.5)
    
#Image callback function get_Src_Tar
def get_Src_Tar(self,color_frame,depth_frame):
    #If outer border coordinates of both gripped and placed blocks are not empty
    if len(self.Roi_init_src)!=0 and len(self.Roi_init_tar)!=0:
        cv2.rectangle(result_image, (self.Roi_init_src[0], self.Roi_init_src[1]), (self.Roi_init_src[2], self.Roi_init_src[3]), (255,0,0), thickness=2)
        cv2.rectangle(result_image, (self.Roi_init_tar[0], self.Roi_init_tar[1]), (self.Roi_init_tar[2], self.Roi_init_tar[3]), (0,255,0), thickness=2)
        
        #Get center point coordinates of the gripped block and calculate its depth information
        grasp_cx = (self.Roi_init_src[0] + self.Roi_init_src[2])/2
        grasp_cy = (self.Roi_init_src[1] + self.Roi_init_src[3])/2
        grasp_depth = depth_image_info[int(grasp_cy),int(grasp_cx)]/1000
        
        #Get center point coordinates of the placed block and calculate its depth information
        tar_cx = (self.Roi_init_tar[0] + self.Roi_init_tar[2])/2
        tar_cy = (self.Roi_init_tar[1] + self.Roi_init_tar[3])/2
        tar_depth = depth_image_info[int(tar_cy),int(tar_cx)]/1000    
        #If depth information of both is not 0, it means the distance is valid
        if tar_depth!=0 and grasp_depth!=0:
            #Calculate position information of the gripped block
            pose_grasp = self.compute_heigh(grasp_cx,grasp_cy,grasp_depth)
            #Calculate position information of the placed block
            pose_tar = self.compute_heigh(tar_cx,tar_cy,tar_depth)
            self.tar_x = pose_tar[0]
            self.tar_y = pose_tar[1]
            self.tar_z = pose_tar[2]
            #print("pose: ",pose)
            if self.done ==True:
                #Get current end effector position of the robotic arm
                self.get_current_end_pos()
                self.done = False
                #Call grip function to grip the block, passing in position information of the gripped block
                self.grasp(pose_grasp[0],pose_grasp[1],pose_grasp[2])
#Grip function grasp, passing in position information of gripped block, calling inverse kinematics service
def grasp(self,x,y,z):
    while not self.client.wait_for_service(timeout_sec=1.0):
        self.get_logger().info('Service not available, waiting again...')       
    self.get_current_end_pos()        
    request = Kinemarics.Request()        
    request.tar_y = y  + self.y_offset + 0.01
    request.tar_z = z  + self.z_offset + 0.01
    if x>0:
        request.tar_x = x +0.01 + self.x_offset
    else:
        request.tar_x = x - 0.01 + self.x_offset
    request.kin_name = "ik"
    request.roll = -0.785
    future = self.client.call_async(request)
    future.add_done_callback(self.grasp_get_ik_respone_callback)   
#Inverse kinematics service callback function grasp_get_ik_respone_callback, gets servo values, controls servos to move to target posture and grip
def grasp_get_ik_respone_callback(self, future):    
    try: 
        response = future.result()
        print("calcutelate_response: ",response)
        joints = [0.0, 0.0, 0.0, 0.0, 0.0,0.0]
        joints[0] = response.joint1 #response.joint1
        joints[1] = response.joint2 
        joints[2] = response.joint3 
        if response.joint4>90:
            joints[3] = 90 
        else:
            joints[3] = response.joint4 
        joints[4] = 90
        joints[5] = 30
        self.Arm.Arm_serial_servo_write6(joints[0],joints[1],joints[2],joints[3],joints[4],joints[5],2000)
        time.sleep(3.5)
        self.Arm.Arm_serial_servo_write(6, 135, 2000)
        time.sleep(2.5)
        self.init_joints[5] = self.grasp_joint
        #After gripping, return to initial posture, prepare to place above the placed block
        self.Arm.Arm_serial_servo_write6_array(self.init_joints,2000)    
        time.sleep(2.5)
        #Execute function to place above the placed block
        self.move()
    except Exception:
       pass 
#Place block function move
def move(self):
    request = Kinemarics.Request()
    px = self.tar_x + self.x_offset
    py = self.tar_y + self.y_offset 
    pz = self.tar_z + self.z_offset 
    #According to placement direction value, modify the position of the placed block, px represents left-right, py represents front-back, pz represents up
    #Front
    if self.side == 1:
        py = py - 0.05
    #Back
    elif self.side == 2:
        py = py + 0.05
    #Left
    elif self.side == 3:
        px = px - 0.07
    #Right
    elif self.side == 4:
        px = px + 0.07
    #Top
    elif self.side == 5:
        pz = pz + 0.04
    #After determining direction data, call inverse kinematics service to get angles when robotic arm reaches placement block position    
    request.tar_x =  px 
    request.tar_y = py 
    request.tar_z = pz 
    request.kin_name = "ik"
    request.roll = -1.0
    future = self.client.call_async(request)
    future.add_done_callback(self.move_get_ik_respone_callback)
#Inverse kinematics service callback function move_get_ik_respone_callback, gets servo values, controls servos to move to target posture and place
def move_get_ik_respone_callback(self, future):    
    try: 
        response = future.result()
        print("calcutelate_response: ",response)
        joints = [0.0, 0.0, 0.0, 0.0, 0.0,0.0]
        joints[0] = response.joint1 #response.joint1
        joints[1] = response.joint2 
        joints[2] = response.joint3 
        if response.joint4>90:
            joints[3] = 90 
        else:
            joints[3] = response.joint4 
        joints[4] = 90
        joints[5] = self.grasp_joint
        self.Arm.Arm_serial_servo_write6(joints[0],joints[1],joints[2],joints[3],joints[4],joints[5],2000)
        time.sleep(2.5)
        self.Arm.Arm_serial_servo_write(6, 0, 2000)
        time.sleep(2.5)
        cur_joints = CurJoints()
        cur_joints.joints = [int(x) for x in joints]
        #Publish robotic arm placement posture angle values topic, action_service node will subscribe to this topic
        self.down_joints_pub.publish(cur_joints)
        self.init_joints[5] = 30
        self.Arm.Arm_serial_servo_write6_array(self.init_joints,2000)
        time.sleep(2.5)
        #Give feedback to the large model, the placement is complete
        self.largemodel_arm_done_pub.publish(String(data='change_pose_done'))
    except Exception:
       pass    

5. Advanced Instructions

We can make the robotic arm complete a more complex task: remember the current position of the color block, then place the color block on another color block, and finally return the block to its original position. You can input the following content in the text_chat terminal:

xxxxxxxxxx
Remember the current position of the blue block, then place the blue block on top of the green block, and finally return the blue block to its original position

image-20251129122230210

The program will first take a photo, find the blue block and calculate its current position, then take another photo to find the positions of the blue block and green block, then control the robotic arm to place the blue block on top of the green block. Then grip the blue block and finally return the blue block to its original position.

5.1. Task Planning

  1. Call seewhat() to observe the environment;
  2. Call compute_pose(x1, y1, x2, y2) function to record the current position of the blue block;
  3. Call seewhat() to observe the environment, check the positions of the blue block and green block;
  4. Call change_pose(x1, y1, x2, y2, x3, y3, x4, y4, 'blue', 'green', side) function to place the blue block on top of the green block;
  5. Call grasp_from_down_list('blue') to find the current position of the blue block in the placement list;
  6. Call return_to_orin('blue') function to return the blue block to its original position.

5.2. Core Code Analysis

change_pose has been analyzed above, mainly analyzing the three functions: compute_pose, grasp_from_down_list, and return_to_orin.

5.2.1. compute_pose Position Recording Function

Source code located at: LargeModel_ws/src/largemodel/largemodel/action_service.py

xxxxxxxxxx
def compute_pose(self,x1,y1,x2,y2,name):
    
    cur_name = name.strip("'\"")  # Remove single quotes and double quotes
    self.cur_name = cur_name
    self.cur_pose[self.cur_name] = []
    #self.check_close_compute_pose()
    #Start position recording program
    cmd1 = "ros2 run largemodel_arm Record_pose"
    # cmd3 = "ros2 run --prefix 'gdb -ex run --args' M3Pro_KCF ALM_KCF_Tracker_Node"
    subprocess.Popen(
        [
            "gnome-terminal",  
            "--title=compute_pose",
            "--",
            "bash",
            "-c",
            f"{cmd1}; exec bash",
        ]
    )                 
    time.sleep(5.0)
    #Publish outer border coordinate information topic data, Record_pose 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]))

Record_pose position recording program, source code located at: LargeModel_ws/src/largemodel_arm/largemodel_arm/Record_pose.py

​x
#Create subscriber, subscribe to object outer border information
self.xy_subscription = self.create_subscription(Int16MultiArray,'corner_xy',self.GetXYCallback,qos_profile=1)   #Create publisher, publish current position topic message   
self.current_pose_position_pub = self.create_publisher(Float32MultiArray, "current_pose", 1)
​
#Callback function GetXYCallback, handles received messages, calculates object center point coordinates
def GetXYCallback(self,msg):
    print("msg: ",msg.data)
    print(msg.data[0])
    print(msg.data[1])        
    print(msg.data[2])
    print(msg.data[3])
    self.cx = (msg.data[0] + msg.data[2])/2
    self.cy = (msg.data[1] + msg.data[3])/2
    
#In callback function TagDetect,
if self.cx!=0 and self.cy!=0:
    cx = self.cx
    cy = self.cy
    #Get depth value of object center point
    cz = depth_image_info[int(cy),int(cx)]/1000
    if cz!=0 and self.pub_pose == True:
        #Calculate current center point position
        pose = self.compute_heigh(cx,cy,cz)
        print("pose: ",pose) 
        #Publish object current position topic message
        self.current_pose_position_pub.publish(Float32MultiArray(data=[pose[0], pose[1], pose[2]]))
        self.pub_pose = False
        #Feedback to large model that current position recording is complete
        self.largemodel_arm_done_pub.publish(String(data="compute_pose_done"))
5.2.2. grasp_from_down_list Function to Grip Object from Placement List

Source code located at: LargeModel_ws/src/largemodel/largemodel/action_service.py

xxxxxxxxxx
def grasp_from_down_list(self,color):
    tar_color = color.strip("'\"")
    #Find robotic arm target posture value in placement based on key value
    tar_joints = self.cur_down_pose.get(tar_color)
    #Control robotic arm to move to target posture
    Arm.Arm_serial_servo_write6(tar_joints[0], tar_joints[1], tar_joints[2], tar_joints[3], tar_joints[4], 0,2000)
    time.sleep(2.0)
    Arm.Arm_serial_servo_write(6, 140, 1000)
    time.sleep(2.0)
    Arm.Arm_serial_servo_write6(90,120,10,10,90,140,2000)
    time.sleep(2.0)
    #Feedback to large model that gripping from placement list is complete
    self.action_status_pub("grasp_from_down_list_done")   
5.2.3. return_to_orin Color Block Return Function

Source code located at: LargeModel_ws/src/largemodel/largemodel/action_service.py

xxxxxxxxxx
def return_to_orin(self,name):
    #Get current color parameter
    tar_name = name.strip("'\"")
    self.get_logger().info(f'"tar_name": {tar_name}')
    self.return_flag = True
    self.get_logger().info('Start to put it to the orin position.')
    self.get_current_end_pos()
    while not self.client.wait_for_service(timeout_sec=1.0):
        self.get_logger().info('Service not available, waiting again...')     
    #If current color block color can be found in the dictionary storing current positions, then call inverse kinematics service, pass the position corresponding to the color block as parameter to the service, get robotic arm servo angle values, then control robotic arm movement.
    if tar_name in self.cur_pose:
        self.get_logger().info('Get the orin position.') 
        request = Kinemarics.Request()
        request.tar_x = self.cur_pose[tar_name][0]
        request.tar_y = self.cur_pose[tar_name][1] + 0.01
        request.tar_z = self.cur_pose[tar_name][2] + 0.01
        request.kin_name = "ik"
        request.roll = -1.0
        #request.pitch = self.CurEndPos[4]
        #request.yaw = self.CurEndPos[5]
        self.get_logger().info(f"request: {request}")
        future = self.client.call_async(request)
        future.add_done_callback(self.get_ik_respone_callback)

 

Multimodal Large Model+robotic arm sorting
Multimodal Large Model+Color Blocks Back In Place

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