ROS2 Autonomous Driving and SLAM using NAV2 with TurtleBot3

🚀 [ROS2 Autonomous Driving and SLAM using NAV2 with TurtleBot3] Course Updated to ROS2 HUMBLE 🚀

Course Headline: 🚧 Maze Solving and Autonomous Waiter with GUI using Robot Operating System 2 and Navigation Stack based on 2D SLAM

Rating is for OLD version of this course 🕰️ (for New Comers), New update to projects and way of explanation will make your learning experience much more engaging! Additionally, we've included ROS1 Noetic Navigation stack implementation after section 4 for those who are interested in understanding both versions.

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Course Workflow:

🤖 Main Robot: TurtleBot3 by Robotis

We will start by obtaining the package from the official GitHub repository and then delve into how to launch the robot into simulations like Rviz and Gazebo. After a comprehensive understanding of multiple launch files, we will create a custom launch file to bring the robot into our simulations. Our journey continues with the SLAM Toolbox, where we will navigate through a maze using Cartographer and SLAM Toolbox.

Next, we'll embark on creating an Autonomous Waiter that leverages the NAV2 stack for path planning and obstacle avoidance, complete with a GUI interface for user interaction.

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Outcomes After This Course:

🚀 You will be able to:

• Create a Custom Workspace
• Develop Custom Python Packages
• Reduce Launch Files for Efficiency
• Master RVIZ and Gazebo Simulation Fundamentals
• Record Simulation Videos with Node Communication Analysis
• Perform 2D SLAM using Cartographer and SLAM Toolbox
• Integrate the NAV2 stack, including Path Planners and Cost Maps

📝 Projects:

1. TurtleBot3 World Navigation using NAV2
2. Maze Solving using Commander API and NAV2
3. Autonomous Waiter with GUI

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Process of Explanation:

1. Theory for Concepts Building - Understanding the fundamental concepts behind each topic.
2. Writing Code for the nodes and concepts discussed - Practical implementation of the theories learned.
3. Analyzing the output and noting the resources utilized - Learning from real-time results and optimization.

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Software Requirements:

✅ Ensure you have:

• Ubuntu 22.04
• ROS2 Humble LTS
• A motivated mind for a huge programming project 🎩

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🔍 Before buying, take a look into this course GitHub repository or message us to get the code and learn from it, even if you decide not to buy! We believe in sharing knowledge and fostering a collaborative learning environment. 🤝

Join us on this journey to master ROS2, SLAM, and autonomous driving with TurtleBot3. Enroll now to unlock your robotics potential! 🧙‍♂️🤖🚀

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Overall Course Review

The Global course rating for this course is an impressive 4.05 out of 5, with all recent reviews aligning positively, indicating a solid educational experience. The majority of students found the course to be informative and interactive, with constructive feedback provided by the instructor. However, it's important to note that previous knowledge of Python, ROS, and Linux is beneficial before taking this course.

Pros:

• Interactive Learning Experience: Many learners appreciated the instructor’s availability for discussions on problems encountered throughout the course.
• Detailed Content: The course was described as very good and detailed, with great descriptions that cater to those eager to dive into ROS and create their first project.
• Educational Value: The course is a great introduction to the topics covered, and it was noted for its well-thought-out structure.
• Community Engagement: Some learners found additional videos discussing more features of rviz and gazebo valuable and enjoyed the extra information provided on system operations.
• Practical Application: The course provides a solid foundation for SLAM, allowing learners to explore and understand the code by applying it to their own projects.
• Supportive Community: Some learners indicated that they were able to resolve errors by searching for answers online, showing a supportive community that can assist with challenges encountered during the course.
• Real-World Application: The course was praised for its practical approach, as demonstrated by one learner who found it the best course on SLAM taken to date.

Cons:

• Setup and Instructions: A few learners found the course lacking in detailed setup instructions and clear programming instructions, making it less accessible for beginners.
• Pacing of Content: The course covers examples at a very fast pace, which may require additional effort from learners to keep up if they are not already familiar with some of the content and tools.
• Video Editing: It was noted that some video lessons end abruptly, and it would be beneficial for these videos to be re-done for a smoother learning experience.
• Problem Explanation: There were suggestions for the course to include more detailed explanations of problems that may occur during development, rather than leaving them in the middle of other subjects.
• Code Exploration: Some learners pointed out that the course could go into more depth on certain topics and that it would be beneficial to show more details about all functions and why certain arguments are used.
• Advanced Topics: While the course covers a lot, some advanced topics such as the fusion of odometry and multiples /scan for 360-degree perception with LiDARs were recommended to be included, especially for learners interested in working with AGV/AMR's.

Additional Notes:

• Language Support: Positive feedback was given in Spanish ("Fantastico..."), indicating the course is well-explanated and step-by-step for learners who may have language barriers.
• Future Improvements: The course could improve by implementing a session that demonstrates solving a maze using a user-created robot, and by showing how to port developed code to a physical robot.
• Resource Recommendations: It was suggested that using fusion2urdf for ros2 would be helpful in the development process.

Conclusion:

The course is highly recommended for those with some background knowledge in Python, ROS, and Linux, seeking to learn about ROS and navigation stacks. With a few adjustments, particularly in areas such as detailed setup instructions, problem resolution, and inclusion of advanced topics, this course could offer an even more comprehensive learning experience for students at all levels.