yahboom
HomeLearn
Solution
Blog
Online Store
About Us
EN/中文
Raspbot V2📖
1.About Raspbot V2
  • 1. First trial
  • 2. Expansion board interface distribution
  • 3. About battery and charging
  • 4. Install servo PTZ
  • 5.Servo Gimbal Installation
  • 6. Important Notes (Read Before Starting)
2.Assembly
  • Installation Video
3.Car basic course
  • 0. Preparation before development
  • 1.Buzzer control
  • 2.Key reading
  • 3.RGB control
  • 4.Servo control (PTZ control)
  • 5.Motor control
  • 6.Printing ultrasonic data
  • 7.Tracking module status printing
  • 8.IR control key value printing
4.Car motion control course
  • 0.Mecanum wheel motion principle
  • 1.Car vertical movement
  • 2.Car diagonal movement
  • 3.Car sideways movement
  • 4.Car rotation movement
  • 5.Car patrol
5.Car advanced courses
  • 1.Infrared patrol car
  • 2.Ultrasonic avoid
  • 3.Ultrasonic follow
  • 4.Infrared tracking+ ultrasonic avoid
  • 5.Restricting
  • 6.Infrared remote control car
6.OpenCV courses
A. Getting Started with OpenCV
  • 1. Introduction to OpenCV
  • 2.OpenCV image reading and display
  • 3. OpenCV image writing
  • 4. OpenCV Image Quality
  • 5.OpenCV pixel operations
B. OpenCV geometric transformation
  • 1. Image scaling
  • 2. Image cutting
  • 3. Image translation
  • 4. Image mirroring
  • 5. Affine Transformation
  • 6. Image Rotation
  • 7. Perspective Transformation
C. OpenCV image processing and text line drawing
  • 1. Grayscale processing
  • 2. Image Binarization
  • 3. Edge Detection
  • 4. Line drawing
  • 5. Rectangle and circle drawing
  • 6. Text and image drawing
D. Image beautification
  • 1. Color Image Histogram
  • 2. Histogram Equalization
  • 3. Image repair
  • 4. Brightness enhancement
  • 5. Smooth and whiten
  • 6. Gaussian mean filtering
  • 7. Median Filter
E. Machine Learning
  • 1. KNN
  • 2. SVM
7.AI vision recognition course
  • 1.Camera driver tutorial
  • 2.Color recognition
  • 3.HSV value test
  • 4.Object Recognition
  • 5.QR code recognition
  • 6.Face recognition
  • 7.License Plate Recognition
  • 8. Human Pose Estimation
  • 9. Target detection
  • 10.yolov5-lite
  • 11. Gesture Recognition
  • 12. Face Detection
  • 13. Face effects
  • 14. 3D Object Recognition
  • 15. Paintbrush
  • 16. Finger control
8.AI vision interaction module
  • 1. Color tracking
  • 2. Color follows
  • 3. QR code recognition to control movement
  • 4. Face tracking
  • 5. Face following
  • 6. Visual tracking
  • 7. Garbage identification
  • 8. Track map Autopilot(purchased separately)
  • 9. Gesture following
9.Voice control basic course(For Superior Kit)new
  • 1.Voice control car movement
  • 2.Voice control RGB light
  • 3.Voice control color recognition
  • 4.Voice control autonomous driving
  • 5.Voice control car tracking
10.AI large model knowledge(For Superior Kit)new
  • 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
  • 5.Configure AI large model(Required)
11.AI Large Model Development(For Superior Kit & Reference)new
  • 1.Register a model service provider account
  • 2. Configuring API-KEY(Optional)
  • 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
  • 9. Custom wake-up response
  • 10. Core module testing tools
12.AI large basic course(For Superior Kit)new
  • 1.Free conversation
  • 2.Text-to-picture
  • 3.Scene description
  • 4.Video description
  • 5.Intelligent action choreography
  • 6.Dual-model agent application
13.ROS2-Robot chassis control course
  • 1. Robot information release
  • 2. Robot keyboard control
  • 3. URDF Model
14.ROS2-OpenCV course
  • 1.ROS+Opencv application
  • 2. AR Vision
  • 3. Mediapipe fun gameplay
  • 4. Gesture control car movement
  • 5. Gesture control car actions
  • 6. Palm control car movement
  • 7. Palm control servo PTZ
  • 8. Visual tracking
  • 9. AprilTag code recognition
  • 10. AprilTag tag code tracking
  • 11. AprilTag tag code follow
15.Development environment course
  • 1. Introduction to Raspberry Pi 5
  • 2. Image writing and backup
  • 3. Start the Raspberry Pi 5
  • 4. Remote Access
  • 5. Remote file transfer
  • 6. Common Linux commands and use of vim editor
  • 7. Install Raspbot V2 driver library
  • 8. Jupyter Lab environment construction
  • 9. Jupyter Lab plugin installation
  • 10. Basic use of Jupyter Widgets
16.Raspberry Pi basic settings
  • 1.Update and upgrade the operating system
  • 2. Introduction to raspi-config tool
  • 3. config.txt file description
  • 4. Network Configuration
  • 5. Set display resolution and rotation
  • 6. Set screen sleep
  • 7. Get real-time temperature of RPi
  • 8. Play audio and video
  • 9. Use USB Camera
  • 10.Use MIPI camera
17.Docker course
  • 1. Overview and installation
  • 2. Common commands
  • 3. Understand and publish images
  • 4. Docker data processing
  • 5. Enter docker container
18.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
Download
  • APP(iOS/Android)
  • Code
  • System_File
  • Instruction Manual
  • Hardware_Info
  • 3D_Model_File
  • ROS2_Basic_VM
Download ZIP

Welcome to Raspbot V2 repository

当製品の日本語版マニュアル資料は、こちらからご取得ください。

11. AprilTag tag code follow

11. AprilTag tag code follow

11. AprilTag tag code follow11.1 Introduction11.2 Startup11.2.1 Preparation before program startup11.2.2 Program description11.2.3, Program Startup11.2.4, Source code

11.1 Introduction

Apriltag tag code tracking is based on apriltag tag code recognition, adding the function of car body movement, controlling the car body so that the camera will keep the tag code moving left and right in the middle of the vision, and based on the principle that objects are larger when near and smaller when far in the camera image, the tag code following function is realized.

11.2 Startup

11.2.1 Preparation before program startup

This apriltag tag code uses TAG36H11 format, and the factory has equipped the relevant tag code and attached it to the building block. You need to take out the building block and place it on the camera screen for recognition.

11.2.2 Program description

After the program is started, the camera captures the image, puts the tag code into the camera screen, and the system will recognize and frame the four vertices of the tag code and display the ID number of the tag code. Then slowly move the position of the building block, and the car will move forward and backward, left and right with the building block.

Note: When the building blocks move, the label code should face the camera, and the movement speed should not be too fast to prevent the camera pan/tilt from failing to keep up.

image-20240702165110669

11.2.3, Program Startup

Open a terminal and enter the following command to enter docker,

./docker_ros2.sh

The following interface appears, indicating that you have successfully entered docker

image-20240814152903441

Enter the following command in the docker terminal to start the program

xxxxxxxxxx
ros2 launch yahboomcar_apriltag apriltag_follow.launch.py

11.2.4, Source code

After entering the docker container, the source code of this function is located at,

Code path: /root/yahboomcar_ws/src/yahboomcar_apriltag/yahboomcar_apriltag/apriltag_follow.py

​x
import rclpy
from rclpy.node import Node
from sensor_msgs.msg import Image
from cv_bridge import CvBridge
import cv2
import yahboomcar_apriltag.fps as fps
import numpy as np
from yahboomcar_apriltag.vutils import draw_tags
from dt_apriltags import Detector
from yahboomcar_apriltag.PID import PositionalPID
from yahboomcar_apriltag.common import *
from Raspbot_Lib import Raspbot
import math

class TagFollowNode(Node):
    def __init__(self):
        super().__init__('tag_follow_node')
        self.twist = Twist()
        self.pub_cmdVel = self.create_publisher(Twist,"/cmd_vel",1)
        self.h=0
        self.scale = 1000
        self.FollowLinePID = (60, 0, 20)
        self.linear = 0.05
        self.PID_init() 
        # 初始化 Raspbot 实例
        self.bot = Raspbot()
        self.fps = fps.FPS()  # 帧率统计器
        self.bridge = CvBridge()
        self.yservo_pid = PositionalPID(0.8, 0.6, 0.01)  # PID控制器用于Y轴
        self.numx=self.numy=1
        target_servox = 90
        target_servoy = 25
        self.bot.Ctrl_Servo(1,target_servox)
        self.bot.Ctrl_Servo(2,target_servoy)
        self.at_detector = Detector(searchpath=['apriltags'],
                                    families='tag36h11',
                                    nthreads=8,
                                    quad_decimate=2.0,
                                    quad_sigma=0.0,
                                    refine_edges=1,
                                    decode_sharpening=0.25,
                                    debug=0)
        self.subscription = self.create_subscription(
            Image,
            '/image_raw',
            self.image_callback,
            100)
        self.subscription  

    def image_callback(self, ros_image):
        # cv_bridge 
        try:
            cv_image = self.bridge.imgmsg_to_cv2(ros_image, desired_encoding='bgr8')
        except Exception as e:
            self.get_logger().error(f"Failed to convert image: {e}")
            return

        # 使用 AprilTags 检测器
        tags = self.at_detector.detect(cv2.cvtColor(cv_image, cv2.COLOR_BGR2GRAY), False, None, 0.025)
        tags = sorted(tags, key=lambda tag: tag.tag_id)

        # 绘制标签
        result_image = draw_tags(cv_image, tags, corners_color=(0, 0, 255), center_color=(0, 255, 0))

        
        # 处理 AprilTags
        if len(tags) == 1:
            _, y, _, self.h = tags[0].bbox
            center_x =tags[0].center[0]
            if math.fabs(180 - (y + self.h/2)) > 20: #调试方块半径    Debug Block Radius
                self.yservo_pid.SystemOutput = y + self.h/2
                self.yservo_pid.SetStepSignal(220)
                self.yservo_pid.SetInertiaTime(0.01, 0.1)
                target_valuey = int(650+self.yservo_pid.SystemOutput)
                target_servoy = int((target_valuey)/10)              
                if target_servoy > 110:
                    target_servoy = 110
                if target_servoy < 0:
                    target_servoy = 0
                #self.bot.Ctrl_Servo(2, target_servoy)
            self.twist.linear.x = self.linear
            [z_Pid, _] = self.PID_controller.update([((center_x) - 320)*1.0/16, 0])
            self.twist.angular.z = +z_Pid
            #self.get_logger().info('center = {} angular ={}'.format([(center_x)],[self.twist.angular.z])) 
            #self.get_logger().info('h = {} '.format([h])) 
            if(80<self.h<110):#调试目标半径70~90 Debug target radius 70~90
                self.twist.angular.z =0.0
                self.twist.linear.x= 0.0
            elif(self.h>110):#调试目标半径90 Debug target radius 90
                self.twist.linear.x= -self.linear
            elif(20<self.h<80):
                self.twist.linear.x= self.linear
            else:
                self.twist.angular.z =0.0
                self.twist.linear.x= 0.0
        else:
            self.twist.angular.z =0.0
            self.twist.linear.x= 0.0
        self.get_logger().info('h = {}linear = {} angular ={}'.format([self.h],[self.twist.linear.x],[self.twist.angular.z])) 
        #print(self.twist.linear.x,self.twist.angular.z)
        self.pub_cmdVel.publish(self.twist)

        # 更新并显示 FPS
        self.fps.update()
        self.fps.show_fps(result_image)
        cv2.imshow("result_image", result_image)
        key = cv2.waitKey(1)
        if key != -1:
            cv2.destroyAllWindows()

    def PID_init(self):
        self.PID_controller = simplePID(
            [0, 0],
            [self.FollowLinePID[0] / 1.0 / (self.scale), 0],
            [self.FollowLinePID[1] / 1.0 / (self.scale), 0],
            [self.FollowLinePID[2] / 1.0 / (self.scale), 0])
          
def main(args=None):
    rclpy.init(args=args)

    tag_follow_node = TagFollowNode()

    try:
        rclpy.spin(tag_follow_node)
    except KeyboardInterrupt:
        tag_follow_node.bot.Ctrl_Servo(1, 90)
        tag_follow_node.bot.Ctrl_Servo(2, 25)
        pass

if __name__ == '__main__':
    main()

 

AprilTag tag code tracking
Introduction to Raspberry Pi 5

COMPANY

  • About us
  • Team Style
  • Blog

LEARNING & EXPLORATION

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

WHERE TO BUY

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

TECHNOLOGY SUPPORT

  • Email: support@yahboom.com

BE A DISTRIBUTOR

  • Email: sales@yahboom.com
QR Code

Copyright © 2017-2026 Yahboom. All Rights Reserved.

EN/中文