Introduction to ROS/GitHub/Linux
An introductory course on Robotic Operating Systems (ROS), GitHub, and Linux.
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Syllabus
This course is full of best-in-class content by leading faculty and industry experts in the form of videos and projects
Course Overview
- In this course, the students will learn about
- Python
- Various tools used in the industry like GitHub and Ubuntu.
- Modern trends and standard practices followed in the industry right now.
- This course is ideal for students who wish to pursue the field of robotics in their higher studies, as well as professionals who are looking to switch careers.
Course Syllabus
On a daily basis we talk to companies in the likes of Tata Elxsi and Mahindra to fine tune our curriculum.
Week 1 - Introduction to Autonomous Vehicles
- Introduction to Self-driving Vehicles
- Levels of Autonomy
- What is ROS?
- Why is ROS important for auto vehicles?
- Integration of ROS with Self-driving Vehicles
Week 2 - Introduction to Linux System
- Introduction to Linux
- Difference between Windows and Linux
- Distributions in Linux
- Install dual OS on Windows Laptop - Install Ubuntu
- Explain the working of Ubuntu
- Introduction to Shell Commands
- Introduction to Git
Week 3 - ROS Introduction
- ROS Architecture and Philosophy
- How to install ROS?
- Abstractions in ROS
- Applications of ROS
Week 4 - Programming Basics for Robotics
- Why python for robotics?
- Python/C++ Essentials
- How to create variables?
- Different Operators
- Conditional Statements and Loops
- If, else, and elif
- For loop
- While loop
- Functions
- Lists, tuples, and dictionary
- OOPs (object-oriented programming)
Week 5 - ROS File System Concepts
- ROS Filesystem Level
- Navigating in ROS (catkin, rosbuild)
- Creating Packages
- Building Packages (catkin_make)
- Package Dependencies
Week 6 - ROS Computation Graph Concepts -I
- ROS Graph Concepts
- Nodes
- Master Node
- Slave Node
- rostopic
- What is a topic in ROS?
- rostopic list/echo
- Messages and message structure
- Services(using rosservice) and Parameters(using rosparam)
- Bags (rosbags, rqt, rosplay, and rosrecord)
Week 7 - ROS Computation Concepts and Visualization
- Writing Publisher Node and Subscriber Node
- Working with Publishers and Subscribers
- 2D Visualization
- Plotting Message Data using rqt_plot
- Real-time Plotting using plotJuggler
- 3D Visualization
- rViz
- Gazebo simulator
Week 8 - ROS for Autonomous Vehicles
- Sensors
- Different Types of Sensors
- Sensory Data
- Visualization of Sensory Data in Rviz
- Dataset available - KITTI, nu scene, COCO, etc
- Introduction to the autonomous driving simulator: CARLA
- Installation of CARLA
- Integration of ROS and CARLA
- ROS bridge installation
Week 9 - Mobile Robotics: Basics
- Wheeled Vs Legged Robots
- Differential Drive Robot Basics
- Kinematic Bicycle Model
Week 10 - Mobile Robotics: Perception
- Introduction to Computer Vision
- OpenCV in Python
- Vision-based Sensors
- Feature Extraction
- Object Detection and Tracking
Week 11 - Mobile Robotics: Localization and Mapping
- Need for Localization for Autonomous Driving
- SLAM
- Grid Maps
- Sensors - Wheel Encoders and IMU
- Accumulation of Errors
- Kalman Filter and Uncertainty
Week 12 - Mobile Robotics: Path Planning
- Vision-based Navigation
- Path Execution and Obstacle Avoidance
- Static and Dynamic Obstacles
- Understanding of Cost and Grid Maps
- Path Planning Algorithms
- RRT
- A*
Our courses have been designed by industry experts to help students achieve their dream careers
Industry Projects
Our projects are designed by experts in the industry to reflect industry standards. By working through our projects, Learners will gain a practical understanding of what they will take on at a larger-scale in the industry. In total, there are 2 Projects that are available in this program.
Tele-Operating NASA ROVER
- In this project, you will develop a simple mock-up of the NASA Mars Landrover robot and teleoperate it.
- Here, you will set up your robot and teleoperation setup in Gazebo and program your robot for teleoperation command.
Use CARLA simulator for implementing simulation on a given model
In this project, you will use the CARLA simulator to create a skeleton model/robot structure for implementing Perception Localization Navigation.
Our courses have been designed by industry experts to help students achieve their dream careers
Ratings & Reviews by Learners
Skill-Lync has received honest feedback from our learners around the globe.
Instructors profiles
Our courses are designed by leading academicians and experienced industry professionals.
1 industry expert
Our instructors are industry experts along with a passion to teach.
4 years in the experience range
Instructors with 4 years extensive industry experience.
Areas of expertise
- Robotics and Autonomous Systems
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