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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

1. Dabai_DCW2 camera

Dabai_DCW2 Camera

Before starting this function, you need to close the large program and APP processes. If you need to restart the large program and APP later, start them from the terminal:

bash ~/dofbot_pro/APP_DOFBOT_PRO/start_app.sh

1. Function Description

After starting the camera, you can obtain RGB color images, Depth images, and IR infrared images.

2. Startup and Operation

2.1. Startup

After connecting the camera to the main board via data cable, open the terminal and enter:

xxxxxxxxxx
ros2 launch orbbec_camera dabai_dcw2.launch.py

As shown in the figure below, if the terminal prints the above content, it means the camera is running normally and started successfully. You can use the following command to view the topics published by the camera node. Enter in the terminal:

image-20250415094737199

xxxxxxxxxx
ros2 node info /camera/camera

As shown in the figure below, the published color image topic is /camera/color/image_raw, the published depth image topic is /camera/depth/image_raw, the published infrared image topic is /camera/ir/image_raw, and the published point cloud data topic is /camera/depth/points.

image-20250415094930453

2.2. Operation

Use rviz to view the three images and point cloud provided by the camera. Restart a terminal and enter:

xxxxxxxxxx
rviz2

After rviz starts, configure rviz according to the following diagram:

image-20240923151317605

Change [Fixed Frame] here to camera_link

Add color image display

image-20240923151723919

Click [Add], then click [By topic], select [/color/image_raw/Image], and finally click [ok] to confirm. The resulting color image is as follows:

image-20240923151949732

Add depth image display

image-20240923152144334

Click [Add], then click [By topic], select [/depth/image_raw/Image], and finally click [ok] to confirm. The resulting depth color image is as follows:

image-20240923152242374

Add infrared image display

image-20240923152423870

Click [Add], then click [By topic], select [/ir/image_raw/Image], and finally click [ok] to confirm. The resulting infrared image is as follows:

image-20240923152507428

View depth point cloud data

image-20240923152817924

Click [Add], then click [By topic], select [/depth/points/PointCloud2], and finally click [ok] to confirm. The resulting depth point cloud image is as follows:

image-20240923152956800

View color point cloud data

image-20250415102030489

Click [Add], then click [By topic], select [/depth_registered/points/PointCloud2], and finally click [ok] to confirm. The resulting color point cloud image is as follows:

image-20240923153713210

3. Camera Parameters

3.1. Hardware Parameter Table

image-20240923154415732

3.2. Camera Parameter Acquisition and Settings

The camera's initialization parameters have been set in the launched launch file. The launch file path is:

xxxxxxxxxx
/home/jetson/dofbot_pro_ws/src/OrbbecSDK_ROS2-main/orbbec_camera/launch/dabai_dcw2.launch.py

The content inside is as follows:

​x
from launch import LaunchDescription
from launch.actions import DeclareLaunchArgument
from launch.substitutions import LaunchConfiguration
from launch_ros.actions import PushRosNamespace
from launch.actions import GroupAction
from launch_ros.actions import ComposableNodeContainer
from launch_ros.descriptions import ComposableNode
from launch_ros.actions import Node
import os
​
​
def generate_launch_description():
    # Declare arguments
    args = [
        DeclareLaunchArgument('camera_name', default_value='camera'),
        DeclareLaunchArgument('depth_registration', default_value='true'),
        DeclareLaunchArgument('serial_number', default_value=''),
        DeclareLaunchArgument('usb_port', default_value=''),
        DeclareLaunchArgument('device_num', default_value='1'),
        DeclareLaunchArgument('vendor_id', default_value='0x2bc5'),
        DeclareLaunchArgument('product_id', default_value=''),
        DeclareLaunchArgument('enable_point_cloud', default_value='true'),
        DeclareLaunchArgument('enable_colored_point_cloud', default_value='true'),
        DeclareLaunchArgument('cloud_frame_id', default_value=''),
        DeclareLaunchArgument('point_cloud_qos', default_value='default'),
        DeclareLaunchArgument('connection_delay', default_value='100'),
        DeclareLaunchArgument('color_width', default_value='640'),
        DeclareLaunchArgument('color_height', default_value='480'),
        DeclareLaunchArgument('color_fps', default_value='30'),
        DeclareLaunchArgument('color_format', default_value='MJPG'),
        DeclareLaunchArgument('enable_color', default_value='true'),
        DeclareLaunchArgument('flip_color', default_value='false'),
        DeclareLaunchArgument('color_qos', default_value='default'),
        DeclareLaunchArgument('color_camera_info_qos', default_value='default'),
        DeclareLaunchArgument('enable_color_auto_exposure', default_value='true'),
        DeclareLaunchArgument('color_exposure', default_value='-1'),
        DeclareLaunchArgument('color_gain', default_value='-1'),
        DeclareLaunchArgument('enable_color_auto_white_balance', default_value='true'),
        DeclareLaunchArgument('color_white_balance', default_value='-1'),
        DeclareLaunchArgument('color_ae_max_exposure', default_value='-1'),
        DeclareLaunchArgument('color_brightness', default_value='-1'),
        DeclareLaunchArgument('color_sharpness', default_value='-1'),
        DeclareLaunchArgument('color_saturation', default_value='-1'),
        DeclareLaunchArgument('color_contrast', default_value='-1'),
        DeclareLaunchArgument('color_gamma', default_value='-1'),
        DeclareLaunchArgument('color_hue', default_value='-1'),
        DeclareLaunchArgument('depth_width', default_value='640'),
        DeclareLaunchArgument('depth_height', default_value='400'),
        DeclareLaunchArgument('depth_fps', default_value='15'),
        DeclareLaunchArgument('depth_format', default_value='Y11'),
        DeclareLaunchArgument('enable_depth', default_value='true'),
        DeclareLaunchArgument('flip_depth', default_value='false'),
        DeclareLaunchArgument('depth_qos', default_value='default'),
        DeclareLaunchArgument('depth_camera_info_qos', default_value='default'),
        # /config/depthfilter/Openni_device.json,need config path.
        DeclareLaunchArgument('depth_filter_config', default_value=''),
        DeclareLaunchArgument('ir_width', default_value='640'),
        DeclareLaunchArgument('ir_height', default_value='400'),
        DeclareLaunchArgument('ir_fps', default_value='15'),
        DeclareLaunchArgument('ir_format', default_value='Y10'),
        DeclareLaunchArgument('enable_ir', default_value='true'),
        DeclareLaunchArgument('flip_ir', default_value='false'),
        DeclareLaunchArgument('ir_qos', default_value='default'),
        DeclareLaunchArgument('ir_camera_info_qos', default_value='default'),
        DeclareLaunchArgument('enable_ir_auto_exposure', default_value='true'),
        DeclareLaunchArgument('ir_exposure', default_value='-1'),
        DeclareLaunchArgument('ir_gain', default_value='-1'),
        DeclareLaunchArgument('publish_tf', default_value='true'),
        DeclareLaunchArgument('tf_publish_rate', default_value='0.0'),
        DeclareLaunchArgument('ir_info_url', default_value=''),
        DeclareLaunchArgument('color_info_url', default_value=''),
        DeclareLaunchArgument('log_level', default_value='none'),
        DeclareLaunchArgument('enable_publish_extrinsic', default_value='false'),
        DeclareLaunchArgument('enable_soft_filter', default_value='true'),
        DeclareLaunchArgument('enable_ldp', default_value='true'),
        DeclareLaunchArgument('soft_filter_max_diff', default_value='-1'),
        DeclareLaunchArgument('soft_filter_speckle_size', default_value='-1'),
        DeclareLaunchArgument('ordered_pc', default_value='false'),
        DeclareLaunchArgument('use_hardware_time', default_value='false'),
        DeclareLaunchArgument('enable_depth_scale', default_value='true'),
        DeclareLaunchArgument('align_mode', default_value='HW'),
        DeclareLaunchArgument('laser_energy_level', default_value='-1'),
        DeclareLaunchArgument('enable_heartbeat', default_value='false'),
    ]
​
    # Node configuration
    parameters = [{arg.name: LaunchConfiguration(arg.name)} for arg in args]
    # get  ROS_DISTRO
    ros_distro = os.environ["ROS_DISTRO"]
    if ros_distro == "foxy":
        return LaunchDescription(
            args
            + [
                Node(
                    package="orbbec_camera",
                    executable="orbbec_camera_node",
                    name="ob_camera_node",
                    namespace=LaunchConfiguration("camera_name"),
                    parameters=parameters,
                    output="screen",
                )
            ]
        )
    # Define the ComposableNode
    else:
        # Define the ComposableNode
        compose_node = ComposableNode(
            package="orbbec_camera",
            plugin="orbbec_camera::OBCameraNodeDriver",
            name=LaunchConfiguration("camera_name"),
            namespace="",
            parameters=parameters,
        )
        # Define the ComposableNodeContainer
        container = ComposableNodeContainer(
            name="camera_container",
            namespace="",
            package="rclcpp_components",
            executable="component_container",
            composable_node_descriptions=[
                compose_node,
            ],
            output="screen",
        )
        # Launch description
        ld = LaunchDescription(
            args
            + [
                GroupAction(
                    [PushRosNamespace(LaunchConfiguration("camera_name")), container]
                )
            ]
        )
        return ld
​

After the camera runs, we can use some services to get or set some parameters, such as getting the exposure value and setting the exposure value. First, let's check what services the camera node can call. Enter in the terminal:

xxxxxxxxxx
ros2 node info /camera/camera

As shown in the figure below, these are some services provided after the camera node starts:

image-20250415100304367

3.2.1. Camera Parameter Acquisition

Taking getting the exposure value as an example, call the service. Enter in the terminal:

xxxxxxxxxx
ros2 service call /camera/get_color_exposure orbbec_camera_msgs/srv/GetInt32 '{}'

As shown in the figure below, the obtained exposure value is 100

image-20250415101115535

3.2.2. Camera Parameter Settings

Taking the auto exposure value switch as an example, call the service. Enter in the terminal:

xxxxxxxxxx
ros2 service call /camera/set_color_auto_exposure std_srvs/srv/SetBool '{data: true}'

image-20250415101613528

Manual setting

xxxxxxxxxx
ros2 service call /camera/set_color_exposure orbbec_camera_msgs/srv/SetInt32 '{data: 50}'

image-20250415182707647

After setting the auto exposure value, the color image will change the exposure value according to the set environment changes.

image-20240923153713210

 

Garbage Sorting with Voice Broadcast(Orin)
Deep Pseudo-Color Image

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