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DOGZILLA-Lite📖
1. Get started quickly
  • 0. Notes (Must Read)new
  • 1. Main interface function experience (cm5)new
  • 1. Main interface function experience(cm4)
  • 2. APP control experience
  • 3. Web page image transmission control
  • 4. LLM-Embodied Intelligence Capabilities
  • 5. LLM-Smart Dog Capabilities
2. Basic operations of Raspberry Pi system
  • 1.SSH remote login
  • 2.VNC remote login desktop
  • 3.Remote file transfer
  • 4.JupyterLab environment construction and use
  • 5.Burn the system image
  • 6.Connect to WiFi
  • 7.Close large programs
  • 8.Raspberry Pi system expansion
3. Basic control of the dogzilla
  • 0. Introduction to DOGZILLA-Lite
  • 1. Screen display control
  • 2. Record Audio
  • 3. Play Audio
  • 4. Text to audio
  • 5. Voice to text
  • 6. Record Video
  • 7. Play Video
  • 8. Video recording function
  • 9. Read the posture angle
  • 10. Battery level reading
  • 11. Read the servo angle
  • 12. Screen display of expressions
  • 13. Calibration of the robot dog
  • 14. Burn the firmware
  • 15. Analysis of common problems
4. Motion control of the dogzilla
  • 1. The dog's forward, backward, left, and right movements and gait
  • 2. Dog Spin
  • 3. The dog adjusts its height and posture
  • 4. Dog periodic translation and rotation
  • 5. Self-balancing mode
  • 6. The dog performs preset actions
  • 7. Single Leg Control
  • 8. Robotic arm control
  • 9. Teaching mode
  • 10. Group Control Performance
  • 11. Teaching demonstration onlinenew
5. Open Source CV
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 drawing text segments
  • 1. Grayscale processing
  • 2. Image Binarization
  • 3. Edge Detection
  • 4. Line drawing
  • 5. Rectangle and circle drawing
  • 6. Text and picture drawing
D. Image beautification
  • 1. Color Image Histogram
  • 2. Histogram Equalization
  • 3. Image repair
  • 4. Brightness enhancement
  • 5. Skin whitening
  • 6. Gaussian mean filtering
  • 7. Median Filter
6. AI recognition of the dogzilla
  • 1. Camera driver tutorial
  • 2. Color recognition
  • 3. HSV value test
  • 4. Object Recognition
  • 5. QR code recognition
  • 6. License Plate Recognition
  • 7. Human Pose Estimation
  • 8. Yolo detection
  • 9. Face Detection
  • 10. Human face mask
  • 11. Face effects
  • 12. 3D Object Recognition
  • 13. Paintbrush
  • 14. Finger recognition
  • 15. Gesture Recognition
  • 16. Age and gender identification
  • 17. Background Segmentation
  • 18. Facial Recognitionnew
  • 19. Emotion Recognitionnew
  • 20. Motion Detectionnew
7. Dogzilla AI Interaction
  • 1. Color patrol
  • 2. Line patrol and obstacle removal
  • 3. Color tracking
  • 4. Ball tracking
  • 5. The ball follows
  • 6. QR code recognition exercise
  • 7. Face tracking
  • 8. Remote control
  • 9. Gesture tracking
  • 10. Gesture-controlled movement
  • 11. You Drop, I Pick
  • 12. Independent football exercise
  • 13. Human Body Followingnew
8. AI large model interaction
  • 0.AI large model principle foundation
  • 1.Large model premise use configuration
  • 2.Q&A
  • 3.Embodied intelligence
  • 4.Smart dog
  • 5.Picture to text
  • 6.Intelligent interaction
  • 7.Voice intelligent vision
  • 8.Playing football
  • 9.Block gripping
  • 10.Line tracking and obstacle removal
  • 11.Control object tracking
Download
  • System_File
  • Code
  • Firmware
  • Communication interface
  • Instruction Manual
  • Appendix
Download ZIP

Welcome to DOGZILLA-Lite repository

1. Color patrol

Color patrol

1. Purpose of the experiment

This tutorial will introduce in detail how to program and control the robot dog to realize the automatic line patrol function, but it is limited to identifying and following the lines of four specific colors: red, green, blue, and yellow. First, you need to equip the robot dog with a color sensor module, which can accurately distinguish the four preset colors of red (RGB value is about 255,0,0), green (0,255,0), blue (0,0,255) and yellow (255,255,0). When programming, we need to establish a color recognition threshold system. When the sensor detects the set target color (for example, red is selected as the line patrol color), the robot dog's control system will adjust the left and right wheel speed difference through the PID algorithm to keep it on the color line. It is particularly important to note that the method in this tutorial is not applicable to other colors because the system only presets the feature value library of these four colors. During the implementation process, it is also necessary to consider the impact of ambient light conditions on color recognition. It is recommended to test and adjust in an indoor environment with stable light.

2. Experimental path source code

First, enter the robot dog system, end the robot dog program, and then enter the path of cd ~/DOGZILLA_Lite_class/6.AI Visual Interaction Course/01.color_line in the terminal and run follow_line.py .

3. Experimental Phenomenon

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python3 follow_line.py

After executing the above command, the robot dog will start the color line patrol operation. By default, it patrols the blue line. You can switch the patrol color by pressing the button in the upper right corner of the robot dog screen or the lowercase 'k' key on the keyboard. As shown in the figure:

image-2025031200001

The upper left corner of the picture shows a yellow line, but there are only blue lines, so the robot dog will not move. You need to press the button to turn yellow into blue , and the robot dog will patrol the line.

image-2025031200002

Press the button in the lower left corner of the screen to exit this program.

4. Main source code analysis

line_speed = 10 #巡线的速度 Speed of patrol line
line_color = 'blue' #yellow  blue  green  red
line_color_step = 2 #蓝色线 blue line
​
#初始化pid Init pid
Px_line = 0.08 # move:0.25  trun 0.15
Ix_line = 0
Dx_line = 0 #.0001
X_line_Middle_error = 160 #图像X轴中心  Image X-axis center
X_line_track_PID = PID.PositionalPID(Px_line, Ix_line, Dx_line) 

The above line patrol PID parameters, if you want to change the line patrol speed and PID value, just change these parameters in the source code.

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#要识别的颜色阈值 Color threshold to be recognized
color_hsv  = {"red"   : ((0, 70, 72), (7, 255, 255)),
              "green" : ((54, 109, 78), (77, 255, 255)),
              "blue"  : ((92, 100, 62), (121, 251, 255)),
              "yellow": ((26, 100, 91), (32, 255, 255))}

Here are the hsv parameters of the color. If you want to add a color for the line patrol, you can add one here. Then, combined with the ability to understand the code, add the function of adding color switching by yourself

Motion Detection
Line patrol and obstacle removal

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