What you would learn in ROS2 Path Planning and Maze Solving with Computer Vision course?
This course focuses on the Maze Solving behavior of robots in a simulation built on ROS2. Computer Vision is the critical subject matter, incorporating crucial robotics algorithms for Motion Planning. We will use the kind of robot is Differential Drive Robot with a Wheel for casters. The course is organized with the below principal headings.
Custom Robot Creation
Gazebo, as well as Rviz
Localization
Navigation
Path Plan
From our robots to our final computer vision Node, we will design and build every item from scratch. Python Object Oriented programming practices will be used to aid in better development.
Learning Results
Simulation Part
Creation Custom Robot Design in Blender ( 3D modeling )
Maze Bot to ROS Simulation. Maze Bot to ROS Simulation powered by Gazebo and RVIZ
Use Nodes to drive your robot
Add Sensors to improve the perception of the Environment
Make diverse Mazes that have to be solved
Algorithm Part
Localization using Fore or Background extraction
Mapping using Graphs Data Structure
Path Planning using
A* search
Dijikstra
DFS Trees
Min Heap
Navigation, while avoiding obstacles and GTG behavior
Pre-Course Requirements
Software-Based
Ubuntu 20.04 (LTS)
ROS2 - Foxy Fitzroy
Python 3.6
Opencv 4.2
Skill-Based
All the code references can be found on the Github repository for this course.
Course Content:
- Create your Self-Driving Car with Simulation (ROS2)
- Learn how to build four Essential Self Drive features (Lane Assist and Cruise Control Navigation. The T-Junc, and Cross Intersections)
- Master ComputerVision techniques, e.g. (Detection, Localization, Tracking)
- Deep Dive using Custom-built Neural Networks (CNN's)
Download ROS2 Path Planning and Maze Solving with Computer Vision from below links NOW!
ROS2 Path Planning and Maze Solving with Computer Vision.part5.rar (Size: 446.0 MB - Date: 4/28/2022 12:06:44 PM)
