ROS 2 Artificial Intelligent Robot using Raspberry PI

ROS 2 Artificial Intelligent Robot using Raspberry PI, available at $54.99, has an average rating of 4.8, with 103 lectures, based on 86 reviews, and has 999 subscribers.

You will learn about 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 This course is ideal for individuals who are ROS and Robotic enthusiast or Developers or Electrical Engineers It is particularly useful for ROS and Robotic enthusiast or Developers or Electrical Engineers.

Enroll now: ROS 2 Artificial Intelligent Robot using Raspberry PI

Summary

Title: ROS 2 Artificial Intelligent Robot using Raspberry PI

Price: $54.99

Average Rating: 4.8

Number of Lectures: 103

Number of Published Lectures: 85

Number of Curriculum Items: 103

Number of Published Curriculum Objects: 85

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

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

Who Should Attend

ROS and Robotic enthusiast

Developers

Electrical Engineers

Target Audiences

ROS and Robotic enthusiast

Developers

Electrical Engineers

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 馃檪

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 4is 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

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Before buying take a look into this course GitHub repository

Course Curriculum

Chapter 1: ROS 2 Bringup and Github Configure

Lecture 1: Guide Lines for this Course

Lecture 2: Section Introduction

Lecture 3: Installing ROS Foxy

Lecture 4: Start Developing in ROS2

Lecture 5: ROS2 Workspace and Package Setup

Lecture 6: ROS2 Publisher Subscriber Node Refresher

Lecture 7: Package Github Push

Lecture 8: Raspberry pi 4 Introduction

Lecture 9: Raspberry pi Setup tools

Lecture 10: Raspberry pi Headless boot and SSH

Lecture 11: Raspberry pi ROS Setup

Lecture 12: Raspberry pi Package Setup

Lecture 13: ROS2 Communication and Version Control

Chapter 2: Building Robot

Lecture 1: Section Introduction

Lecture 2: Robot building Compnents

Lecture 3: Lets Compile the Robot

Lecture 4: Electronics Connection

Lecture 5: Performing all Robot Connections

Lecture 6: Understanding Motor Drive

Lecture 7: Raspberry pi GPIO Library Functioning

Lecture 8: GPIO Library Basic Testing

Lecture 9: Robot Drive Testing

Lecture 10: ROS2 Turtlesim Velocity Communication

Lecture 11: Differential Drive Robot Kinematics Implementation

Lecture 12: ROS2 Twist Message

Lecture 13: Read About ROS2 Nodes workflow

Lecture 14: Robot Drive Node

Lecture 15: Joystick Integration

Lecture 16: Github Push

Chapter 3: Qr Maze Solving Robot

Lecture 1: Section Introduction

Lecture 2: Meshes Positions

Lecture 3: Transform and Robot Structures

Lecture 4: Robot Joints Positioning

Lecture 5: Robot State Publisher

Lecture 6: Robot to URDF

Lecture 7: RVIZ and Meshes

Lecture 8: Solving Meshes Issues for RVIZ

Lecture 9: Joints Test in Rviz

Lecture 10: Gazebo Launch Files

Lecture 11: Differential Drive Plugin

Lecture 12: Camera Plugin Integration

Lecture 13: Lidar Plugin Integration

Lecture 14: QR Codes Generation

Lecture 15: Maze Creation in Blender

Lecture 16: Finishing Maze Creation

Lecture 17: Adding QR Codes to Maze

Lecture 18: Bringing Maze to Gazebo

Lecture 19: QR Maze World Creation in Gazebo

Lecture 20: Setting up Nodes

Lecture 21: Lidar Detection and Reaction

Lecture 22: Manual Maze Solving

Lecture 23: Github Push

Chapter 4: Line Following Robot Simulation and Real Robot

Lecture 1: Section Introduction

Lecture 2: Line Model Creation in Blender

Lecture 3: Model Exporting into Gazebo

Lecture 4: Node Creation

Lecture 5: Camera Repositioning

Lecture 6: World File Updating

Lecture 7: Image Processing Node

Lecture 8: Line Segmentation

Lecture 9: Mid Point Extraction

Lecture 10: Line Following in Simulation

Lecture 11: Raspberry pi Video Feed

Lecture 12: Data Bandwidth Optimization

Lecture 13: Real Time image Processing Node

Lecture 14: Line Segmentation for Real Robot

Lecture 15: Parameters Tuning for Algorithms

Lecture 16: Testing line Following on ground

Lecture 17: Finalizing Code

Lecture 18: Robot Output Comparisons

Lecture 19: Github Push

Chapter 5: AI Search Robot

Lecture 1: Section Introduction

Lecture 2: Tensorflow Lite Model

Lecture 3: Model Discussion and Obtaining

Lecture 4: Model Running

Lecture 5: Prediction Script

Lecture 6: Inferencing

Lecture 7: Boundary Boxes and Label Output

Lecture 8: AI Node Creation

Lecture 9: Real Time Model Predictions

Lecture 10: Finalizing the Node

Lecture 11: Joy drive and Launch File

Lecture 12: Syncing things up

Lecture 13: Project Output

Lecture 14: Github Push

Instructors

Muhammad Luqman
Heavy Roboticist

Rating Distribution

1 stars: 4 votes

2 stars: 5 votes

3 stars: 14 votes

4 stars: 25 votes

5 stars: 38 votes

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