ROS2 Path Planning and Maze Solving with Computer Vision

Why take this course?
🎉 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:
- Custom Robot Creation
- Gazebo and Rviz Integration
- Localization
- Navigation
- 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! 🚀⚫️🧮
Course Gallery




Loading charts...