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DOFBOT SE📖
1.First Trial
  • 1.First Trial
  • 2.Analysis of common problems
  • 3.Calibration offset
  • 4.Precautions for DIY Robotic Arm
  • 5.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 software control
4.Preparation
  • 1.Manage APP control service
  • 2.Manage JupyterLab service
  • 3.Write STM32 firmware
  • 4.Introduction to expansion board
5.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.Stack block
  • 12.USB camera display
  • 13.Synchronous action
6.AI Vision Basic Course
  • 1.Gesture Recognition
  • 2.Color threshold adjustment Color calibration
  • 3.Color recognition
  • 4.Face recognition
  • 5.Garbage recognition
  • 6.Yolo object recognition
  • 7.Real-time web view
7.2D 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
8.2D Visual Grasping Course
  • 0.Visual positioning
  • 1.Gesture recognition stack blocks
  • 2.Color recognition grab blocks
  • 3.Catch game
  • 4.Color sorting and stacking
  • 5.Garbage sorting
  • 6.Color interaction
9.2D Mediapipe Course
  • 1.Hand detection
  • 2.Posture detection
  • 3.Overall inspection
  • 4.Face detection
  • 5.Face recognition
  • 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)
  • 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 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.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 Course
  • 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 Course
  • 1.AprilTag Tracking
  • 2.Color Block Tracking
  • 3.KCF Object Tracking
15.3D Voice Control for Sorting & Tracking Course
  • 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 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
17.AI Model Development
  • 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
18.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 + YOLOv11 + Intelligent Garbage Classification
19.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 + YOLOv11 + Intelligent Garbage Classification
20.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
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.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.OpenCV Course
  • 1.Introduction to Open Source CV
  • 2.Image reading and display
  • 3.Picture writing
  • 4.Image Quality
  • 5.Pixel operations
  • 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
25.Advanced Visuals Course
  • 1. QR Code
  • 2. Human Pose Estimation
  • 3. Object Detection
  • 4. AR Vision
  • 5. ROS + OpenCV Basics
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.Jupyter Lab Usage Course
  • 0.Read the document before operation
  • 1.Jupyter Lab environment construction
  • 2.Basic use of Jupyter Widgets
  • 3.Launch JupyterLab
  • 4.How to Use JupyterLab
Download
  • APP(iOS/Android)
  • VM File
  • Instruction manual
  • Hardware_3DModelFile
  • PC_Software
  • Code
  • Communication Protocol-Python Library
  • Tools
Download ZIP

Welcome to DOFBOT SE repository

1. Robotic arm ROS control

Robotic Arm ROS Control

1. Function Description

After the program starts, you can use ROS to control the robotic arm's pose and individual servo values. This uses the ROS2 topic command-line tool to change the robotic arm's pose and individual servo values.

2. Startup and Operation

2.1 Startup Command

After opening the terminal, enter the following command:

# Start the underlying driver
ros2 run dofbot_driver arm_driver 

2.2 Operation Steps

Start the terminal and enter the following command:

xxxxxxxxxx
ros2 topic list

image-20260320162906073

As shown in the image above, the displayed /TargetAngle is the topic for controlling the robotic arm. We publish messages to this topic using the rostopic tool. First, let's check the topic's related information. Enter in the terminal:

xxxxxxxxxx
ros2 topic info /TargetAngle

image-20260320162947253

As shown in the image above, the data type name of this topic is dofbot_interface/ArmJoint. This is our custom message data interface. You can view the specific content of this data type with the following command. Enter in the terminal:

xxxxxxxxxx
ros2 interface show dofbot_interface/msg/ArmJoint

image-20260320163059487

As shown in the image above, the specific content of the dofbot_pro_interface/ArmJoint type data is displayed. The attributes of each variable are as follows:

  • id: corresponds to the servo's id. When controlling a single servo, you need to specify this value. The value range is [1-6];
  • run_time: servo running time. Within this time, the robotic arm needs to reach the specified pose or a single servo needs to reach the specified angle. Unit is ms
  • angle: control angle. When controlling a single servo, you need to set the angle value after operation. The value range is [0,180]
  • joints: an array of length 6. When controlling the values of 6 servos, write the values of the 6 servos into this data. The array index corresponds to each servo's id-1. The control range of each servo angle is [0,180]

We can test this with the following command:

To control a single servo, taking the control of servo #1 as an example, enter in the terminal:

xxxxxxxxxx
ros2 topic pub /TargetAngle dofbot_interface/ArmJoint "id: 1
run_time: 1000
angle: 120.0
joints: []"

It is recommended to use Tab on the single quote keyboard to complete the topic name and the content of the following data, then modify the corresponding values. After input is complete, press Enter to send the topic data. Servo #1 of the robotic arm will rotate to the 120° state.

To control multiple servos, taking the robotic arm presenting an upward extended pose as an example, enter in the terminal:

xxxxxxxxxx
ros2 topic pub /TargetAngle dofbot_interface/ArmJoint "id: 1
run_time: 1000
angle: 120.0
joints: [90,90,90,90,90,90]" 

Similarly, we recommend using Tab on the single quote keyboard to complete the topic name and the content of the following data, then modify the corresponding values. After input is complete, press Enter to send the topic data. The robotic arm will present an upward extended pose.

To control the buzzer:

​x
# Turn the buzzer on
ros2 topic pub /Beep std_msgs/msg/Bool "data: True" --once
​
# Turn the buzzer off
ros2 topic pub /Beep std_msgs/msg/Bool "data: False" --once

3. Program Flowchart

image-20240921115939383

4. Core Code Analysis

Code path:

xxxxxxxxxx
/home/yahboom/dofbot_ws/src/dofbot_driver/dofbot_driver/arm_driver.py

Import library files:

xxxxxxxxxx
import rclpy
from rclpy.node import Node
# Robotic arm control library
from Arm_Lib import Arm_Device
from dofbot_interface.msg import ArmJoint
from std_msgs.msg import Bool
import time
from sensor_msgs.msg import JointState
import numpy as np
from math import pi
import os

Define a subscriber to subscribe to the TargetAngle topic:

xxxxxxxxxx
self.sub_Arm = self.create_subscription(ArmJoint,"TargetAngle", self.arm_callback, queue_size=1000)

Write a callback function to process the subscribed messages and send data to the underlying control:

xxxxxxxxxx
def arm_callback(self, msg):
    arm_joint = ArmJoint()
​
    if len(msg.joints) != 0:
        self.get_logger().info(f"Received joints: {msg.joints}")
        arm_joint.joints = self.cur_joints
        for _ in range(2):
            self.Arm.Arm_serial_servo_write6(
                msg.joints[0], msg.joints[1], msg.joints[2],
                msg.joints[3], msg.joints[4], msg.joints[5],
                time=msg.run_time
            )
            self.cur_joints = list(msg.joints)
            self.arm_pub_update.publish(arm_joint)
    else:
        self.get_logger().info(f"Moving joint {msg.id} to {msg.angle}")
        arm_joint.id = msg.id
        arm_joint.angle = msg.angle
        for _ in range(2):
            self.Arm.Arm_serial_servo_write(msg.id, msg.angle, msg.run_time)
            self.cur_joints[msg.id - 1] = msg.angle
            self.arm_pub_update.publish(arm_joint)
​
    self.joints_states_update()

The code checks the length of the received joints, which is len(msg.joints) in the code. It determines whether to control 6 servos or a single servo based on the length of joints. If it is not 0, it means controlling 6 servos. When publishing data to the underlying layer, we need to use the Arm_serial_servo_write6 function to send data; otherwise, if it is 0, it means controlling a single servo, and we need to use the Arm_serial_servo_write function to send data.

The Arm_serial_servo_write6 function is provided by the Arm_Lib library. Arm_Lib is our custom library for controlling the underlying driver. Arm_serial_servo_write6 has 7 parameters, which are the values of the 6 servos and the running time. After receiving the topic data, we just need to input the 6 servo values (joints) and the run_time value correspondingly.

The Arm_serial_servo_write function is also provided by the Arm_Lib library. It requires 3 input parameters: the id of the servo to control, the angle to control the servo to, and the running time. After receiving the topic data, we just need to input the id value, the control angle value (angle), and the run_time value correspondingly.

Garbage Sorting with Voice Broadcast
Inverse solution

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