ROS 2 Artificial Intelligent Robot using Raspberry PI

🚀 Master ROS 2 with Artificial Intelligence on Raspberry Pi! 🤖✨

Course Updated: ROS Kinetic to the latest ROS 2 Foxy - we've updated everything, from projects to explanations, to ensure you get the most up-to-date learning experience! 📚✨

Course Workflow: This hands-on course is centered around building a 2-wheel differential drive robot with a caster, utilizing 3D printed parts for durability and flexibility. We'll cover all the electronics, ensuring you understand each connection for a robust setup. 🛠️🤖

Raspberry Pi 4: The powerhouse of our robot! We'll dive deep into ROS 2 Foxy and Humble on Raspberry Pi, setting up your workspace, and enabling WiFi communication between your laptop and Raspberry Pi. 🍓💻

Key Sections:

1. ROS2 Workspace Raspberry pi Setup - Laying the foundation for your ROS 2 environment on Raspberry Pi.
2. Robot Building and Driving with Joystick - Assembling your robot and learning to control it in real-time.
3. QR Maze Solving using OpenCV - Implementing computer vision algorithms to navigate through a maze guided by QR codes.
4. Line Following Real and Simulation Robot - Mastering line following techniques both in the real world and with simulation for robust learning.
5. AI Surveillance Robot using Tensorflow Lite - Building an AI-powered surveillance robot that can recognize objects or faces.

Outcomes After this Course:

• Create a custom workspace tailored to your projects.
• Develop your own Python packages, ensuring modularity and reusability of your code.
• Craft launch files to manage ROS nodes as needed.
• Build a custom mobile robot with integrated ROS 2 and simulation capabilities.
• Get hands-on experience with RVIZ and Gazebo simulations, essential for testing and refining your robots.
• Integrate computer vision using OPENCV within your ROS 2 based applications.
• Implement deep neural networks into your ROS 2 nodes to add intelligent behaviors to your robot.

Software Requirements:

• Ubuntu 22.04 for a smooth development experience.
• ROS 2 Foxy, the latest version to ensure compatibility and support.
• A motivated mind ready to tackle a large-scale programming project! 🚀

🔗 Explore this course on GitHub before you buy! Get a glimpse of what's in store for your learning journey with our updated projects and clear, step-by-step explanations. Dive into the world of ROS 2 AI robots with Raspberry Pi today! 🧵🎉

Enroll now and take your first step towards becoming an expert in integrating Artificial Intelligence with Robot Operating System (ROS) on Raspberry Pi micro-controllers! 🤓🚀

📚 Overall Course Review

The average global rating for this course is an impressive 4.55 out of 5 stars, based on recent reviews. The course has been praised for its comprehensive content and the expertise of the instructor, despite some challenges that may arise from the course's fast pace and dense material. Here's a breakdown of the feedback:

Pros:

• Expert Guidance: The instructor is knowledgeable and delivers content in an engaging manner, making complex topics understandable.
• Practical Projects: The course includes great example projects with clear instructions, which are highly beneficial for learners.
• Real-World Application: The use of real robots in the course allows learners to see how concepts apply in practical scenarios, which is a significant strength of the course.
• Content Quality: The course material is content-rich and well-prepared, providing a comprehensive learning experience.
• Motivational Teaching Style: The teaching approach involves students effectively, making topics more interesting and useful.
• Relevance to Real-World Scenarios: Learners are using the course to program real robots for applications like line following and potentially integrating AI.

Cons:

• Pace of Learning: The course moves at a fast pace, which can be challenging for beginners or those not already familiar with Python and ROS. It's suggested that students have prior knowledge in these areas to keep up.
• Detail in Instructions: Some crucial details in the instructions may go unnoticed due to the speed of the course, which could lead to confusion later on.
• Visual Aids: There is a recommendation for more detailed schematics of electronic connections rather than short videos, to aid in understanding the robot connections assembly.
• Assumption of Previous Knowledge: The course assumes a certain level of familiarity with ROS2, which might be overwhelming for absolute beginners.
• Complexity of Material: The volume of material covered is substantial, and some learners find it to be a bit too much at once.

Additional Notes:

• It's emphasized that while the course is challenging, it is still a wonderful experience for those who are interested in robotics and have at least a foundational understanding of Python and ROS.
• The course is recommended with the caveat that students should have prior knowledge of ROS before purchasing, to ensure a better learning experience.
• Overall, the course is considered highly worthwhile for individuals looking to enhance their skills in ROS2 with real robots.

In conclusion, this course is well-regarded among learners, with its strong points being the practical projects and the expert guidance provided. However, potential students should be prepared for a fast-paced learning environment and should ideally have some prior knowledge of Python and ROS to fully benefit from the course material.