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DOFBOT-Ultra📖
1. Get Started Quickly
  • 0.Unboxing Video
  • 1. Get Started Quickly
  • 2. Access the Terminal
  • 3. Calibration offset(Depth Camera)
  • 4. Calibration offset(USB Camera)
2. Assembly Course
  • 1. Installation Steps
  • 2. Calibrate the Robotic Arm
  • 3. Install the Map
3. Remote Control Course
  • 1. Mobile Phone Control
  • 2. USB Handle Control
  • 3. PC Web Page Programming
4. Preparation
  • 1. Manage Web control services
  • 2. Manage JupyterLab services
  • 3. Manage OLED services
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 Grasping Course
  • 1. Identify grab color block
  • 2. Catch game
  • 3. Color sorting
  • 4. Color stacking
  • 5. Label code sorting
  • 6. Label code stacking
  • 7. Gesture grasping color blocks
  • 8. Gesture stacking color blocks
  • 9. Single garbage sorting
  • 10. Garbage sorting
  • 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. 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
8. AI Large Model Voice Module (Must Read)
  • 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 Sorting
  • 11. Garbage Recognition with Voice Broadcast
  • 12. Garbage Sorting with Voice Broadcast
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. Yolov26 garbage sorting
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 Yolov26 Garbage Recognition Sorting
  • 8. Voice Control Object Tracking and Grasping
15. AI Model Basics
  • 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 Development
  • 0. DofbotUltra Large Model Development Quick Start Guide
  • 1. Register a Model Service Provider Account
  • 2. Configure API-KEY
  • 3. Introduction to Dify
  • 4. Basic Dify Features
  • 5. AI Large Model Development - chatbot
  • 6. Deploy the RAG knowledge base
  • 7. RAG knowledge base + chatbot
  • 8. AI agent workflow
17. AI Model – Text Version
  • 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 + YOLOv26 + Intelligent Garbage Classification
18. AI Model – Voice Version
  • 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 + YOLOv26 + Intelligent Garbage Classification
19. AI Model – Fun Applications
  • 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
PI 5
  • 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 and Conversion
  • 11. Model Prediction
RDK X5
  • 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 Prediction
  • 9. Model Conversion
22. YOLOv26 Development Course
PI 5
  • 6. Image Classification
  • 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 and Conversion
  • 11. Model Prediction
RDK X5
  • 1. YOLO Environment Setup
  • 2. CLI Usage
  • 3. Object Detection
  • 4. Instance Segmentation
  • 5. Pose Estimation
  • 7. Oriented Bounding Boxes Object Detection
  • 8. Model Conversion
  • 9. Dataset Annotation
  • 10. Model Training and Conversion
  • 11. 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. 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.RDKx5 Board Basic Course
  • 1.Introduction to RDK X5 board
  • 2.Write system file
  • 3.Hardware interface description
  • 4.Install case and other accessories
  • 5.Use 7-inch display
  • 6.SD card expansion
  • 7.Network configuration
  • 8.Remote login
  • 9.Update system
  • 10.srpi-config user guide
  • 11.config. txt configuration file
  • 12.Self-startup setting
  • 13.RDK exclusive command usage
  • 14.Linux command usage
  • 15.GPIO readwrite operation
  • 16.Image classification algorithm
  • 17.Use USB camera
  • 18.Use MIPI camera
  • 19.Binocular camera depth algorithm
26.PI5 Board Basic Course
  • 1.Introduction to Raspberry PI 5
  • 2.Raspberry PI system installation and backup
  • 3.Powering the Raspberry PI 5
  • 4.Startup of Raspberry PI 5
  • 5.Installation and introduction of shell and active radiator
  • 6.GPIO and 40-pin pins
  • 7.Tips for using Raspberry PI 5
  • 8.Raspberry PI PCIe connector
  • 9.MIPI CSI DSI connector
  • 10.UART connector
  • 11.Real-time clock
  • 12.Power over Ethernet (PoE) connector
  • 13.Update and upgrade operating system
  • 14.Introduction to raspi-config tool
  • 15.config.txt file description
  • 16.Network Configuration
  • 17.remote access
  • 18.Transfer files remotely
  • 19.Set display resolution and rotation
  • 20.Set screen to sleep
  • 21.Get real-time temperature of Raspberry Pi
  • 22.Play audio and video
  • 23.Using USB camera
  • 24.Using MIPI camera
27. Visual Basics 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 Detection
  • 16. Line Segment Drawing
  • 17. Rectangular Circle Drawing
  • 18. Text and Picture Drawing
28. 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
29. 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
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  • System_File
  • VM_File
  • Instruction Manual
  • Code
  • Software Tools
  • Communication_Protocols
  • Hardware_Info
Download ZIP

Welcome to DOFBOT-Ultra repository

9. Multimodal Large Model + Color Blocks Back In Place

Multimodal Large Model + Color Blocks Back In Place

[Note: The content of this section is an experimental feature, greatly affected by the environment. In complex scenarios, returning to the original position may fail. This tutorial is only intended to provide a reference approach.]

1. Feature Description

After the program runs, first enter the instruction to remember the current positions of the color blocks in the terminal. After the program completes recording, scramble the positions of the color blocks. Then enter the instruction to put the color blocks on the table back to their original positions. After the large model thinks, it will plan a list of actions. The program will execute the action functions in the list to control the robotic arm to complete the task of returning the color blocks to their original positions.

2. Startup

To calibrate colors:

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Raspberry Pi:python3 /home/yahboom/dofbot_ultra/2d/dofbot_color_identify/scripts/hsv_calibration_gui.py
RDK:python3 /home/sunrise/dofbot_ultra_ws/src/2d/dofbot_color_identify/scripts/hsv_calibration_gui.py

Enter the Docker container and run the following command:

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

Then open a second terminal and enter the following command:

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ros2 run text_chat text_chat

Now enter the instruction to remember the current positions of the color blocks in the terminal. Refer to the following example:

x
Remember the current positions of the color blocks

image-20251129151258160

Then you can scramble the positions of the color blocks. After scrambling, enter the instruction to return the color blocks to their original positions in the terminal. For example:

x
Return the blocks on the desktop to their original positions

image-20251129154127570

After the buzzer beeps, the program will start grasping the color blocks and putting them back to their original positions according to the order recorded in the program. If there are other blocks on top of the block that needs to be returned, the program will first remove the block on top, then grasp and return it to its 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. Return Color Blocks to Original Positions

  1. First call scan_blocks_hsv() to scan the blocks on the table and cache the bbox. For subsequent grasping/removal, uniformly use color parameters instead of manually passing coordinates.

  2. Call color_back_to_orin() to start the return process. The program will publish "Next, return the X color block to its original position" one by one.

  3. For each current target color block X, execute the following steps (only one action function at a time): (1) Call check_remove('X') to check the removal list; (2) If feedback is "The block exists in the removal list": call grasp_from_rm_list('X'), then call return_to_orin('X'); (3) If feedback is "The block does not exist in the removal list": first call scan_blocks_hsv() to update the cache, then call seewhat() to determine if there is an obstruction on top of X; (4) If there is no obstruction on top of X: call grasp_color('X') to grasp, then call return_to_orin('X') to return to position; (5) If there is an obstruction on top of X (obstructing color is top_color): call remove_color('top_color') to remove the obstruction, then call scan_blocks_hsv() to update the cache, then call seewhat() to re-check, until no obstruction remains, then execute (4).

  4. When all returnable color blocks have been processed, the system sends feedback return_to_orin_done and ends the current round of tasks.

Note: In the new workflow, seewhat() is only used for the large model to examine images and determine obstruction relationships, no longer responsible for providing grasping coordinates. During the return phase, using grasp_obj(...) and remove_obj(...) to replace the color interface is prohibited.

4. Core Code Analysis

4.1. compute_pose_order - Record Stacked Color Block Positions

Source path:

Raspberry Pi:/home/yahboom/dofbot_ultra_ws/src/llm/largemodel/largemodel/action_service.py

RDK:/home/sunrise/dofbot_ultra_ws/src/llm/largemodel/largemodel/action_service.py

xxxxxxxxxx
#Parameter description
#x1,y1,x2,y2: Top outer bounding box coordinates of the top block, x1,y1 represent
#name: Color of the top block
#order: Indicates the order list of stacked blocks from top to bottom. For example, if the colors stacked from top to bottom are red and yellow, the passed order would be ry
def compute_pose_order(self,x1,y1,x2,y2,name,order):
    #Get current block parameters
    cur_name = name.strip("'\"")  
    color_order = order.strip("'\"")
    #Create a list to store the stacking order from top to bottom
    order_list = list(color_order[1:])
    self.get_logger().info(f"order_list:{order_list}")
    self.cur_name = cur_name
    self.cur_pose[self.cur_name] = []
    #Start calculating position information
    cmd1 = "ros2 run largemodel_arm Record_pose"
    proc = subprocess.Popen(
        [
            "gnome-terminal",
            "--title=compute_pose",
            "--",
            "bash",
            "-c",
            f"{cmd1}; exec bash",
        ]
    )
    self.pid_num = proc.pid   
    self.get_logger().info(f"self.pid_num:{self.pid_num}")
    time.sleep(5.0)
    x1 = int(x1)
    y1 = int(y1)
    x2 = int(x2)
    y2 = int(y2)
    self.object_position_pub.publish(Int16MultiArray(data=[x1, y1, x2, y2]))

    while not self.compute_pose_future.done():
        if self.interrupt_flag:
            self.check_close_compute_pose()
            Arm.Arm_serial_servo_write6(90,150,12,20,90,30,1000)
            self.stop()
            return
        time.sleep(0.1)

    result = self.compute_pose_future.result()
    self.check_close_compute_pose()
    if not self.interrupt_flag:
        if result.data == "compute_pose_done":
            for i in range(0,len(order_list)):
                #Here we assume that the xy direction is consistent with the top block, only the height changes. So we only need to change the z value, decreasing by 2.8 cm each time, approximately the height of one block
                x = self.cur_pose[cur_name][0]
                y = self.cur_pose[cur_name][1]
                z = self.cur_pose[cur_name][2] - (i+1)*0.028
                if order_list[i] == 'r' or  order_list[i] == 'R':
                    self.cur_pose['red'] = []
                    self.cur_pose['red'].append(x) 
                    self.cur_pose['red'].append(y) 
                    self.cur_pose['red'].append(z) 
                elif order_list[i] == 'b' or  order_list[i] == 'B':
                    self.cur_pose['blue'] = []
                    self.cur_pose['blue'].append(x) 
                    self.cur_pose['blue'].append(y) 
                    self.cur_pose['blue'].append(z)                
                elif order_list[i] == 'y' or  order_list[i] == 'Y':
                    self.cur_pose['yellow'] = []
                    self.cur_pose['yellow'].append(x) 
                    self.cur_pose['yellow'].append(y) 
                    self.cur_pose['yellow'].append(z)  
                elif order_list[i] == 'g' or  order_list[i] == 'G':
                    self.cur_pose['green'] = []
                    self.cur_pose['green'].append(x) 
                    self.cur_pose['green'].append(y) 
                    self.cur_pose['green'].append(z)  
                    
            self.get_logger().info(f"self.cur_pose:{self.cur_pose}")
            #Provide feedback to the large model that recording stacked block positions is complete
            self.action_status_pub("compute_pose_order_done", x1=x1, y1=y1, x2=x2, y2=y2,name=name,order=order)
        else:
            self.action_status_pub("compute_pose_failed", x1=x1, y1=y1, x2=x2, y2=y2)

4.2. scan_blocks_hsv - Scan and Cache Block Bbox

Source path:

Raspberry Pi:/home/yahboom/dofbot_ultra_ws/src/llm/largemodel/largemodel/action_service.py

RDK:/home/sunrise/dofbot_ultra_ws/src/llm/largemodel/largemodel/action_service.py

xxxxxxxxxx
def scan_blocks_hsv(self):
    self.auto_detect_blocks(publish_status=False)
    found = {}
    missing = []
    for color in ("red", "green", "blue", "yellow"):
        box = self._load_latest_detections_box(color)
        if box:
            found[color] = box
        else:
            missing.append(color)
    self._HSV_BOXES_CACHE = found
    if not found:
        self.action_status_pub("scan_blocks_hsv_no_block")
        self.finishtask()
        return
    color_cn = {"red": "红色", "green": "绿色", "blue": "蓝色", "yellow": "黄色"}
    parts = [f"{color_cn.get(c, c)}(bbox={x1},{y1},{x2},{y2})"
             for c, (x1, y1, x2, y2) in found.items()]
    if missing:
        parts.append(f"未检测到:{','.join(color_cn.get(c, c) for c in missing)}")
    summary = ";".join(parts)
    self.action_status_pub("scan_blocks_hsv_done", summary=summary)

4.3. color_back_to_orin - Publish Color Block Return Order

Source path:

Raspberry Pi:/home/yahboom/dofbot_ultra_ws/src/llm/largemodel/largemodel/action_service.py

RDK:/home/sunrise/dofbot_ultra_ws/src/llm/largemodel/largemodel/action_service.py

xxxxxxxxxx
def color_back_to_orin(self):
    self.back_list = True
    self.return_order = self._get_return_color_order()
    self.return_order_index = 0
    self.return_done = False
    if len(self.return_order) == 0:
        self.action_status_pub("return_to_orin_done")
        self.back_list = False
        return
    self._publish_next_return_target()

"send_red_back_to_orin": "机器人反馈:接下来把红色方块进行归位",
"send_green_back_to_orin": "机器人反馈:接下来把绿色方块进行归位",
"send_blue_back_to_orin": "机器人反馈:接下来把蓝色方块进行归位",
"send_yellow_back_to_orin": "机器人反馈:接下来把黄色方块进行归位"

4.4. check_remove(color) - Check If the Current Block to Return Exists in the Removal List

Source path:

Raspberry Pi:/home/yahboom/dofbot_ultra_ws/src/llm/largemodel/largemodel/action_service.py

RDK:/home/sunrise/dofbot_ultra_ws/src/llm/largemodel/largemodel/action_service.py

​x
def check_remove(self,color):
    check_color = color.strip("'\"")
    if check_color == "all":
        if len(self.cur_rm_pose) > 0:
            self.action_status_pub("color is in the rm_list")
        else:
            self.action_status_pub("color is not in the rm_list")
        return
    if check_color in self.cur_rm_pose:
        self.action_status_pub("color is in the rm_list")
    else:
        self.action_status_pub("color is not in the rm_list")

"color is in the rm_list" : "移除列表中存在该色块,在移除列表中夹取该色块",
"color is not in the rm_list" : "移除列表中不存在该色块,判断该色块的顶部是否存在其它物体",
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