ROS 2 Artificial Intelligent Robot using Raspberry PI from Udemy

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 :

ROS2 Workspace Raspberry pi Setup
Robot Building and Driving with Joystick
QR Maze Solving using OpenCV
Line Following Real and Simulation Robot
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

What's inside

Learning objectives

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

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

Syllabus

ROS 2 Bringup and Github Configure

Guide Lines for this Course

Installing ROS Foxy

Start Developing in ROS2

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Covers ROS2, a widely adopted robotics software framework, and integrates it with Raspberry Pi, a popular platform for robotics projects, making it highly relevant for practical applications

Integrates computer vision using OpenCV and deep neural networks using Tensorflow Lite within the ROS2 framework, providing a comprehensive introduction to AI-powered robotics

Teaches how to create custom Python packages, launch files, and mobile robots, which are essential skills for developing and deploying ROS2-based robotic systems

Requires Ubuntu 22.04 and ROS2 Foxy, which may necessitate learners to set up a specific development environment before starting the course

Requires a motivated mind for a huge programming project, which may be a barrier for learners who are new to programming or have limited time to dedicate to the course

Uses ROS2 Foxy, which was released in 2020, so learners should be aware that newer versions of ROS are available and may offer additional features and improvements

Reviews summary

Build a ros 2 ai robot

According to learners, this course is a highly practical and engaging experience focused on building a real robot. Students particularly appreciate the hands-on projects, stating that the ability to construct a physical robot controlled by ROS 2 and a Raspberry Pi is a significant highlight. Many found the course provides a good foundational understanding of integrating hardware with ROS 2 for computer vision and AI tasks. However, some learners note that the course can be challenging for beginners, requiring prior knowledge in areas like Python programming, Linux, and potentially basic electronics. While the update to ROS 2 is well-received, navigating setup and potential code issues requires significant troubleshooting and persistence.

Covers interesting computer vision and AI applications.

"The sections on QR maze solving and line following using OpenCV were very cool."

"Learning how to integrate TensorFlow Lite on the robot for object detection was a major plus."

"The course touches on advanced topics like image processing and neural networks within a robotics context."

"I enjoyed seeing how computer vision could be applied in a real-world robot scenario."

Provides a solid introduction to ROS 2 integration.

"The course gives a really good overview of how to connect ROS 2 nodes to physical hardware."

"I feel like I understand the basics of using ROS 2 with a mobile robot much better now."

"It covers essential topics like URDF, Gazebo simulation, and integrating sensors."

"The explanation of ROS 2 communication patterns in the context of the robot was helpful."

Focuses on building and programming a physical robot.

"The course is fantastic for getting your hands dirty and building a real robot from scratch."

"I loved the practical aspects, especially building the robot and seeing it perform tasks."

"The projects are the strongest part, applying concepts directly to hardware is very rewarding."

"Building the differential drive robot and implementing the kinematics was a great learning experience."

Learners need to be prepared to debug code and setup.

"Be ready to spend time debugging code and adapting it to your specific hardware setup."

"Some parts of the code needed minor adjustments to work correctly on my system."

"Troubleshooting network communication between the Pi and laptop was necessary."

"It's a course where problem-solving skills are essential to succeed."

Setting up hardware and software can be difficult.

"Getting ROS 2 and all the dependencies set up on the Raspberry Pi was the most challenging part."

"I spent a lot of time troubleshooting connection and installation issues before I could even start the projects."

"The hardware setup requires careful attention to detail, and any mistakes can be frustrating."

"There are many steps involving software versions and libraries that need to match perfectly."

Assumes prior knowledge in programming and Linux.

"This course is definitely not for absolute beginners, you need a solid grasp of Python and Linux."

"I struggled with the setup and some coding parts due to limited prior experience with ROS and Ubuntu."

"It helps if you have some background in robotics concepts before diving into this."

"Be prepared to learn a lot on your own if you don't have the necessary coding background."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in ROS 2 Artificial Intelligent Robot using Raspberry PI with these activities:

Review ROS 2 Fundamentals

Show steps

Reinforce your understanding of core ROS 2 concepts like nodes, topics, services, and parameters before diving into the course's practical applications.

Show steps

Review the official ROS 2 documentation.
Complete a basic ROS 2 tutorial.
Practice creating simple publisher/subscriber nodes.

Brush up on Python Programming

Show steps

Strengthen your Python skills, as ROS 2 heavily relies on Python for scripting and node development.

Browse courses on Python

Show steps

Review Python syntax and data structures.
Practice writing functions and classes.
Work through Python tutorials focused on robotics.

Review 'A Concise Introduction to Robot Programming with ROS 2'

Show steps

Supplement the course material with a comprehensive guide to ROS 2 programming.

View Putting Knowledge to Work on Amazon

Show steps

Read the book's introductory chapters.
Work through the example code provided in the book.
Experiment with modifying the example code.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Build a Simple Robot Simulator

Show steps

Apply your knowledge by creating a basic robot simulator using Gazebo and ROS 2, reinforcing your understanding of robot modeling and simulation.

Show steps

Design a simple robot model in a CAD tool.
Import the robot model into Gazebo.
Create ROS 2 nodes to control the robot's movement.
Implement basic sensor simulation.

Document Your Learning Journey

Show steps

Solidify your understanding by creating a blog or video series documenting your progress and challenges throughout the course.

Show steps

Choose a platform for your documentation.
Document each major milestone in the course.
Share your documentation with the course community.

Contribute to a ROS 2 Package

Show steps

Deepen your understanding by contributing to an existing open-source ROS 2 package, gaining experience with real-world robotics projects.

Show steps

Find an open-source ROS 2 package on GitHub.
Identify a bug or feature to work on.
Submit a pull request with your changes.

Optimize AI Surveillance Robot

Show steps

Enhance the AI surveillance robot project by optimizing its performance, improving its accuracy, and adding new features.

Show steps

Profile the robot's performance to identify bottlenecks.
Implement optimizations to improve speed and efficiency.
Add new features such as object tracking or facial recognition.

Career center

Learners who complete ROS 2 Artificial Intelligent Robot using Raspberry PI will develop knowledge and skills that may be useful to these careers:

ROS Developer

A ROS developer specializes in creating software and tools using the Robot Operating System (ROS) for robotics applications. This course is highly useful, focusing primarily on ROS 2 and its application in building AI powered robots. The course takes you through setting up a ROS 2 workspace on Raspberry Pi, building custom Python packages, and creating launch files. You'll also gain experience in integrating ROS 2 with RVIZ and Gazebo for simulation. By building a complete mobile robot and implementing functionalities like joystick control, QR maze solving, and line following, you'll gain practical experience directly applicable to a ROS developer role.

See salaries and explore the career path for ROS Developer

Robotics Engineer

A robotics engineer designs, builds, programs, and tests robots for various applications. This course helps in building a foundation for this role, especially with its focus on ROS 2, a widely used framework in robotics. The practical experience of building a mobile robot using 3D printed parts and integrating it with Raspberry Pi 4, as covered in this course, directly translates to real world robotics projects. The sections on robot building and driving with joystick, QR maze solving using OpenCV, line following robot, and AI surveillance robot using Tensorflow Lite are beneficial for anyone wanting to become a robotics engineer.

See salaries and explore the career path for Robotics Engineer

Computer Vision Engineer

Computer vision engineers design and implement algorithms enabling machines to 'see' and interpret images, which is a key component in many robotic systems. This course helps in mastering computer vision techniques within the ROS 2 framework. A key aspect of this course is the integration of OpenCV for QR maze solving and image processing for line following. The section on AI surveillance using TensorFlow Lite is directly relevant to object detection and recognition, a core area within computer vision. This course prepares you for roles needing practical skills in image data transmission and bandwidth optimization for computer vision projects.

See salaries and explore the career path for Computer Vision Engineer

Image Processing Specialist

Image processing specialists develop algorithms and techniques to process and analyze images, often for applications in computer vision and medical imaging. This course helps with image processing skills as it covers image data transmission, bandwidth optimization, and real time image processing using OpenCV. The section on line following involves segmenting lines in images and extracting midpoints, which are essential image processing techniques. This course prepares you for roles that involve developing image processing algorithms for embedded systems.

See salaries and explore the career path for Image Processing Specialist

Embedded Systems Engineer

Embedded systems engineers design and develop hardware and software for embedded systems, often used in robotics and IoT devices. This course helps with the embedded systems aspect by focusing on Raspberry Pi 4 as the main brain for the robot. Setting up and configuring the Raspberry Pi, understanding GPIO library functioning, and managing communication between the Raspberry Pi and other components are essential skills for an embedded systems engineer. The course's hands on approach to building and programming a robot using Raspberry Pi is valuable.

See salaries and explore the career path for Embedded Systems Engineer

AI Engineer

An artificial intelligence engineer develops and implements AI models and algorithms for different applications. This course helps in AI engineering through its coverage of TensorFlow Lite for AI surveillance. You'll learn how to integrate deep neural networks into ROS 2 based nodes and perform real time model predictions. This course helps with creating custom Python packages and deploying AI models on embedded systems like Raspberry Pi, which are all critical skills for an AI engineer working in robotics.

See salaries and explore the career path for AI Engineer

AI Model Developer

An AI model developer focuses on designing, training, and deploying artificial intelligence models, which could apply to various applications. This course helps in AI model development through its coverage of TensorFlow Lite for AI surveillance robots. You learn how to perform real time model predictions on embedded systems. This course prepares you for roles that involve deploying AI models on resource constrained devices for use in autonomous systems. The section on inferencing will prove useful.

See salaries and explore the career path for AI Model Developer

Software Engineer

Software engineers design, develop, and test software applications. This course can be advantageous with its emphasis on creating custom Python packages, writing launch files, and managing ROS 2 nodes. The course allows you to develop skills in areas such as communication protocols, version control, and writing efficient code for resource constrained environments like the Raspberry Pi. The project based approach, where you build and program a robot from scratch, is valuable for software engineers interested in robotics or embedded systems.

See salaries and explore the career path for Software Engineer

Simulation Engineer

Simulation engineers create and use computer models to simulate physical systems. This course can be useful as it covers the integration of ROS 2 with Gazebo for robot simulation. This course trains you how to create launch files, integrate robot models into Gazebo, and use plugins for camera and lidar simulation. The experience gained in simulating the robot's behavior in different environments, such as the QR maze, is applicable to the role of a simulation engineer.

See salaries and explore the career path for Simulation Engineer

Mechatronics Engineer

Mechatronics engineers integrate mechanical, electrical, and computer engineering principles to design and build automated systems. This course can be useful with its focus on building a physical robot and integrating it with software and AI components. The course covers the mechanical aspects of robot building, the electrical connections for the robot, and the programming needed to drive and control the robot with ROS 2. The integration of computer vision and AI for tasks like QR maze solving and AI surveillance prepares you for a job.

See salaries and explore the career path for Mechatronics Engineer

Autonomous Vehicle Engineer

Autonomous vehicle engineers develop self driving systems for cars, trucks, and other vehicles. This course may be useful as it focuses on ROS 2 and robotic systems, which are pivotal in developing autonomous capabilities. This course's curriculum, including robot building and driving, coupled with AI surveillance using TensorFlow Lite, provides a practical grounding in the technologies used in autonomous vehicles. Line following and QR maze solving showcase path planning and computer vision skills, which are essential for autonomous navigation. The course's hands on approach using Raspberry Pi also mirrors the embedded systems used in autonomous vehicles.

See salaries and explore the career path for Autonomous Vehicle Engineer

Automation Engineer

An automation engineer designs and implements automated systems for various industries. This course may be useful as it trains for creating automated robotic systems using ROS 2. The course includes learning how to build and program a robot to perform tasks such as QR maze solving and line following, all of which are relevant to automation. The integration of computer vision and AI using OpenCV and TensorFlow Lite adds to the appeal of this course for automation engineers.

See salaries and explore the career path for Automation Engineer

Research Scientist

A research scientist conducts research and development in various fields, often requiring advanced degrees such as a Master's or PhD. This course may be useful for those involved in robotics or AI research, as it provides practical experience with ROS 2 and Raspberry Pi. The projects covered in the course, such as QR maze solving and AI surveillance, can serve as a starting point for more advanced research projects. The ability to create custom ROS 2 packages and integrate deep neural metworks could be helpful for research.

See salaries and explore the career path for Research Scientist

Test Engineer

Test engineers design and implement tests to ensure the quality and functionality of products. This course may be useful as it provides hands on experience testing robot systems and their components. The process of building, programming, and testing the robot, along with verifying the functionality of computer vision and AI algorithms, is valuable for a test engineer. The course may also help you learn how to identify and troubleshoot issues in a robotic system.

See salaries and explore the career path for Test Engineer

Application Engineer

Application engineers work with customers to understand their needs and develop solutions using a company's products. This course may be useful as it provides a practical understanding of ROS 2 and its applications in robotics. The ability to build and program a robot, integrate computer vision and AI, and create custom ROS 2 packages are skills that can be used to understand customer requirements and develop solutions using ROS 2 based systems. The ability to integrate ROS 2 with RVIZ and Gazebo will prove useful.

See salaries and explore the career path for Application Engineer

ROS 2 Artificial Intelligent Robot using Raspberry PI

Here's a deal for you

What's inside

Learning objectives

Syllabus

Traffic lights

Save this course

Reviews summary

Build a ros 2 ai robot

Activities

Career center

Reading list

Share

Similar courses