ROS2 Path Planning and Maze Solving with Computer Vision

🎉 Master Mobile Robotics with ROS2: Path Planning, Navigation & Motion Planning 🎉

Course Title: 🚀 "ROS2 Path Planning and Maze Solving with Computer Vision"

Course Headline: 🧭 "Mobile Robot Localization, Navigation, and Motion Planning with Robot Operating System 2"

Course Description:

Embark on a journey to master the intricacies of robotics with our comprehensive online course. Dive into the world of ROS2, where you'll learn how to create a simulated maze-solving robot using Python and computer vision. This course is meticulously designed for those eager to understand the Maze Solving behavior of robots in a simulation environment.

Key Focus:

• Integration of Computer Vision: We'll explore how to integrate important robotics algorithms for motion planning, with a special emphasis on computer vision techniques.
• Differential Drive Robot with Caster Wheel: You'll work with a Differential Drive Robot equipped with a caster wheel, learning to manipulate and control it from scratch.
• Custom Robot Creation: Start by creating your own robot design using Blender, the 3D modeling software, before integrating it into ROS2 simulations.

Course Structure:

1. Custom Robot Creation
2. Gazebo and Rviz Integration
3. Localization
4. Navigation
5. Path Planning

Every component of the robot, from its creation to the last computer vision Node, will be constructed and understood step by step. We'll adhere to Python's Object-Oriented Programming best practices for robust development.

Learning Outcomes: 🎓

Simulation Part:

• Create a Custom Robot Design in Blender (3D modeling)
• Integrate your Maze Bot into ROS2 simulations using Gazebo and RVIZ
• Drive your robot with Nodes and add sensors for better environmental perception
• Build complex Mazes to challenge your robot's navigation capabilities

Algorithm Part:

• Implement Localization using foreground and background extraction techniques
• Explore Mapping with Graph Data Structures
• Master Path Planning with various algorithms:
  • A* search algorithm
  • Dijikstra’s algorithm
  • Depth-First Search (DFS) trees
  • Min Heap data structure
• Navigate your robot while avoiding obstacles and implementing GTG (Global Transformation Group) behavior

Pre-Course Requirements: ✅

Software Based:

• Ubuntu 20.04 (LTS)
• ROS2 - Foxy Fitzroy
• Python 3.6
• Opencv 4.2

Skill Based:

• Basic understanding of ROS2 Nodes Communication
• Proficiency with Launch Files in YAML format
• Experience with Gazebo Model Creation
• A motivated mindset and eagerness to learn! 😄

Additional Resources:

All the codes for this course are available on the GitHub repository, offering a valuable reference throughout your learning journey.

Before you dive in, why not preview some of our free course materials? Get a taste of what's to come and clear up any doubts by reaching out to us with your questions! 📚✨

Join us today and transform your understanding of robotics with ROS2 and Computer Vision! 🚀⚫️🧮