ROS SLAM Navigation Stack and Custom Robot

🚫 Course Updated to ROS NOETIC! 🚩

Rating is for OLD version of this course (for New Comers) 🏗️, New update to projects and way of explanation is what you are going to ❤️!

Course Workflow:

1. Creating the Custom Robot "Explorer Bot" 🚀:

   • We'll dive into creating a custom two-wheel differential drive robot from scratch using URDF (Unified Robotic Description Format). This includes understanding joints and links that define our robot's structure.

2. Robot Configuration with Gazebo Plugins:

   • Integrate essential Gazebo plugins like Differential Drive and Laser Scanner into the URDF of our "Explorer Bot". Get it ready for real-world simulation where we can actually drive the robot and read laser scan values within the simulation environment.

3. SLAM with Gmapping:

   • Master the SLAM (Simultaneous Localization and Mapping) process using the Gmapping algorithm, which will help our robot to map an environment with a lidar sensor as it navigates through it.

4. Autonomous Navigation Setup:

   • Create a room within Gazebo using the Model Builder tool and enable our "Explorer Bot" to perform autonomous driving tasks using the Navigation Stack algorithms. This includes path planning and managing costs in the environment.

Outcomes After this Course:

You will be able to:

• Create a Custom Workspace with all necessary directories, scripts, and resources.
• Develop Custom Python Packages tailored for your robotic projects.
• Design and simulate Mobile Robots using URDF in real-world scenarios.
• Calculate the Inertia Matrix for URDF Links to optimize your robot's dynamics.
• Integrate Gazebo plugins such as Differential Drive and Laser Scanner into your custom robot.
• Write and understand Launch files to manage simulation environments.
• Master RVIZ and Gazebo Simulation Fundamentals for visualizing and interacting with your robots in a virtual space.
• Work with 3D CAD Model meshes within the Gazebo simulation for a more realistic environment.
• Perform SLAM using the Gmapping node, which is crucial for robotics applications.
• Integrate the Navigation Stack into your custom robot, including configuring path planners and cost maps.

Projects:

• Custom Differential Drive Robot Creation: Implement a fully functional differential drive robot from scratch.
• Pipeline Exploration and Mapping Robot: Set up a system to explore environments and build accurate 3D maps.
• Custom Room creation and Autonomous Navigation in Gazebo: Design a custom room and program your robot to autonomously navigate through it using the Navigation Stack.

Process of Explanation:

1. Interactive writing and comments to understand the theory behind each concept.
2. Coding nodes for the discussed concepts, followed by analyzing the output.
3. Documenting resources used and understanding their impact on the project's outcome.

Software Requirements:

• Ubuntu 20.04 (Your robotics lab in a box!)
• ROS Noetic (The latest Robot Operating System for cutting-edge robotics)
• A motivated mind ready to embark on a programming adventure!

🔍 Before buying, take a look into this course's GitHub repository or message the instructor to get an insight into what you will be learning! And even if you don't buy the course, the code is available for you to learn from 🙂.

Global Course Rating: 4.35

Overall Summary: The course has received a mix of positive and constructive feedback from recent reviewers. Many students found it to be an excellent resource for learning Simultaneous Localization and Mapping (SLAM) using Robot Operating System (ROS), particularly for those new to ROS or SLAM concepts. The practical, straightforward approach to teaching has been praised, with some highlighting the hands-on learning experience and the clarity in the later sections of the course.

Pros:

• Practical Approach: Students appreciate the course's focus on practical implementation, especially for navigating robots.
• Comprehensive Content: The course covers a wide range of topics within ROS, including SLAM and path planning.
• Real-world Application: Reviewers point out that the bonus project is particularly valuable, providing students with an opportunity to apply what they've learned in a real-world context.
• Supplementary Materials: The availability of additional packages and code from the instructor is noted as helpful for completing the course projects.
• Clear Explanation: Some reviewers note that the later sections of the course are well-explained, especially when it comes to URDF creation.

Cons:

• Pacing and Complexity: A few reviewers found the pacing to be fast, requiring them to rewatch videos to fully understand the material. Additionally, some found the content challenging if they were not already familiar with ROS concepts.
• Theoretical Foundations: There is a request for more theoretical background, particularly in the earlier sections of the course, which could help beginners grasp the concepts better.
• Content Omission: Some reviewers felt that certain aspects, such as Python programming or deeper dives into TF (Transformation Frame), were not covered thoroughly and recommended consulting the ROS Wiki for further understanding.
• Technical Issues: A few reviews mention audio inconsistencies and some lessons ending with unresolved bugs, suggesting the instructor could improve by addressing these points in future iterations of the course.
• Language Consideration: One reviewer noted that having instructions or projects described in Hindi would be beneficial for a portion of the audience.
• Section Clarity: One section, specifically number 5, received criticism for being poorly explained, with the instructor appearing to struggle with the content.

Course Highlights:

• Completion Satisfaction: Most students who completed the course report feeling satisfied with what they learned and believe it offers good value for the cost.
• Community Feedback: The feedback provided by students across various review platforms indicates a generally positive experience, with many stating it is a "complete course" that teaches how to simulate an autonomous control system on a differential robot.

Recommendations for Improvement:

• Improve Pacing and Clarity: To cater to both beginners and those with more advanced ROS knowledge, consider slowing down the pacing and providing clearer explanations in the early sections of the course.
• Increase Theoretical Explanation: Enhance the theoretical aspects of ROS to give students a stronger foundation before diving into practical applications.
• Resolve Technical Issues: Address any technical issues, such as audio quality and bugs, to ensure a smooth learning experience.
• Expand on Missing Topics: Ensure that all relevant topics, including Python programming and the use of TF, are thoroughly covered within the course material.

Final Thoughts: The course is a valuable resource for students looking to learn about SLAM and robot navigation using ROS. With some improvements in pacing, clarity, and technical execution, this course could become an even more essential tool for students at all levels of ROS proficiency.