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ROS 2 Artificial Intelligent Robot using Raspberry PI

ROS 2 Artificial Intelligent Robot using Raspberry PI

Loại khoá học: Hardware

Utilize Tensorflow and OpenCV for Computer Vision on Robot Operating System enabled Robots using Micro Controllers

image
50.000 VND
1.399.000 VND
Đầy Đủ Bài Giảng
Học Online Tiện Lợi
Kích Hoạt Nhanh 2-5 Phút
Thanh toán tự động
Được phép tải xuống

Tổng quan

Nội dung khoá học

Đánh giá

Mô tả

Course Updated ROS Kinetic to ROS 2 Foxy :

Rating is for OLD version of this course , New update to projects and way of explanation is what you are going to love :)

Course Workflow:

This Course is for mobile robot which is a 2 wheel differential drive with a caster . We will First build the robot using 3D printed parts. All electronics is going to be explained for proper connections .

Raspberry Pi 4 is going to be main brain for this robot . ROS2 foxy and humble both are going to be utilized using this course . WiFi Communication between laptop and Raspberry Pi will be done .

We will look into image data transmission  and bandwidth optimization for our computer vision based projects . 



Sections  :

  1. ROS2 Workspace Raspberry pi Setup

  2. Robot Building and Driving with Joystick

  3. QR Maze Solving using OpenCV

  4. Line Following Real and Simulation Robot

  5. AI Surveillance Robot using Tensorflow Lite

Outcomes After this Course : You can create

  • Custom Workspace

  • Custom Python Packages

  • Launch files

  • Custom Mobile Robots

  • ROS 2 Robot and Simulation integration

  • RVIZ and Gazebo Simulation Fundamentals

  • Computer Vision with ROS 2 using OPENCV

  • Deep Neural Networks on ROS 2 based Nodes


Software Requirements

  • Ubuntu 22.04

  • ROS 2 Foxy

  • Motivated mind for a huge programming Project

    ----------------------------------------------------------------------------------

    Before buying take a look into this course GitHub repository

Bạn sẽ học được gì

Build your own ROS 2 Enabled AI Robot 🚘

Raspberry Pi 4 based Robot for Computer Vision 🧠

Joystick Real Time Driving Robot 🕹️

QR Maze Solving Robot 🚧

Line Following Robot

Yêu cầu

  • Software : Ubuntu 22.04 LTS
  • Software : ROS 2 Basics understanding
  • Software : Python Programming
  • Hardware : Raspberry pi 4
  • Hardware : L298D Motor Driver
  • Hardware : DC geared motors with Encoders
  • Hardware : Lipo Battery 12V
  • Hardware : Jumper Wires
  • Hardware : Power Bank
  • Basic Electronics Understanding
  • Hardware : Two Geared Dc motors 12V

Nội dung khoá học

5 sections

ROS 2 Bringup and Github Configure

12 lectures
Guide Lines for this Course
00:17
Section Introduction
00:30
Installing ROS Foxy
06:48
ROS2 Workspace and Package Setup
05:08
ROS2 Publisher Subscriber Node Refresher
12:01
Package Github Push
06:33
Raspberry pi 4 Introduction
01:07
Raspberry pi Setup tools
01:40
Raspberry pi Headless boot and SSH
05:25
Raspberry pi ROS Setup
04:09
Raspberry pi Package Setup
07:11
ROS2 Communication and Version Control
05:54

Building Robot

15 lectures
Section Introduction
01:03
Robot building Compnents
02:03
Lets Compile the Robot
03:04
Electronics Connection
03:29
Performing all Robot Connections
05:09
Understanding Motor Drive
02:04
Raspberry pi GPIO Library Functioning
05:42
GPIO Library Basic Testing
07:38
Robot Drive Testing
00:49
ROS2 Turtlesim Velocity Communication
05:53
Differential Drive Robot Kinematics Implementation
06:02
ROS2 Twist Message
06:27
Robot Drive Node
01:48
Joystick Integration
05:33
Github Push
02:32

Qr Maze Solving Robot

22 lectures
Section Introduction
01:38
Meshes Positions
07:44
Robot Joint's Positioning
05:27
Robot State Publisher
05:04
Robot to URDF
07:10
RVIZ and Meshes
04:49
Solving Meshes Issues for RVIZ
07:33
Joints Test in Rviz
06:50
Gazebo Launch Files
03:26
Differential Drive Plugin
05:01
Camera Plugin Integration
04:32
Lidar Plugin Integration
08:30
QR Codes Generation
05:07
Maze Creation in Blender
03:44
Finishing Maze Creation
04:40
Adding QR Codes to Maze
07:50
Bringing Maze to Gazebo
06:34
QR Maze World Creation in Gazebo
08:19
Setting up Nodes
07:48
Lidar Detection and Reaction
08:13
Manual Maze Solving
06:33
Github Push
01:56

Line Following Robot Simulation and Real Robot

19 lectures
Section Introduction
00:50
Line Model Creation in Blender
05:50
Model Exporting into Gazebo
08:08
Node Creation
06:30
Camera Repositioning
04:20
World File Updating
02:27
Image Processing Node
06:09
Line Segmentation
07:55
Mid Point Extraction
04:50
Line Following in Simulation
07:19
Raspberry pi Video Feed
08:37
Data Bandwidth Optimization
08:16
Real Time image Processing Node
05:40
Line Segmentation for Real Robot
04:05
Parameters Tuning for Algorithms
02:57
Testing line Following on ground
05:17
Finalizing Code
03:36
Robot Output Comparisons
00:38
Github Push
04:24

AI Search Robot

14 lectures
Section Introduction
01:08
Tensorflow Lite Model
04:19
Model Discussion and Obtaining
04:35
Model Running
04:10
Prediction Script
07:18
Inferencing
04:41
Boundary Boxes and Label Output
07:21
AI Node Creation
08:54
Real Time Model Predictions
09:31
Finalizing the Node
06:54
Joy drive and Launch File
07:11
Syncing things up
05:57
Project Output
04:08
Github Push
01:39

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