ROS2 Path Planning and Maze Solving with Computer Vision
Mobile Robot Localization , Navigation and Motion Planning with Robot Operating System 2
What you will learn
Build your own Maze Solving Simulation (ROS2)
Write Search algorithms [A* , Dijikstra, Min Heap]
Computer Vision techniques e.g. (Detection, Segmentation)
Deep Dive with Custom-built Navigation Graphs
Why take this course?
This course is focus on Maze Solving behavior of robot In a Simulation based on ROS2. Computer Vision is the key focus with integrated important robotics algorithms of Motion Planning . The type of robot we will be using is Differential Drive Robot with a caster wheel . Course is structured with below main headings .
Custom Robot Creation
Gazebo and Rviz Integerations
Localization
Navigation
Path Planning
From our robot to last computer vision Node ,we will create every thing from scratch . Python Object Oriented programming practices will be utilized for better development.
Learning Outcomes
- Simulation Part
Creation Custom Robot Design in Blender ( 3D modeling )
Bringing Maze Bot into ROS Simulation powered by Gazebo and RVIZ
Drive your robot with Nodes
Add Sensor for better perception of Environment
Build different Mazes to be solved
- Algorithm Part
Localization with Fore and Back ground extraction
Mapping with Graphs Data Structure
Path Planning with
A* search
Dijikstra
DFS Trees
Min Heap
Navigation while avoiding Obstacles and GTG behavior
Pre-Course Requirments
Software Based
Ubuntu 20.04 (LTS)
ROS2 - Foxy Fitzroy
Python 3.6
Opencv 4.2
Skill Based
Basic ROS2 Nodes Communication
Launch Files
Gazebo Model Creation
Motivated mind :)
All the codes for reference are available on git hub repository of this course .
Get a good idea by going through all of our free previews available and feel free to Contact in case of any confusion :)
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