Self Driving and ROS – Learn by Doing! Odometry & Control
Self Driving and ROS – Learn by Doing! Odometry & Control, available at $94.99, has an average rating of 4.43, with 134 lectures, 2 quizzes, based on 185 reviews, and has 2693 subscribers.
You will learn about Create a Real Self-Driving Robot Mastering ROS, the Robot Operating System Implement Sensor Fusion algorithms Simulate a Self-Driving robot in Gazebo Develop a Controller Odometry and Localization Kalman Filters and Extended Kalman Filter Probability Theory Differential Kinematics Create a Digital Twin of a Self-Driving Robot Master the TF library This course is ideal for individuals who are Self-Driving enthusiast or Makers and Hobbists keen on robotics or Software developers taht wants to learn ROS and Robotics or Students or Engineers that wants to learn how to buid a robot from scratch or Developers that already knows ROS and that want to use it in a real world application or Robotics Engineers that wants to develop skills in Autonomous Navigation or Beginner Python developers curious about Self-Driving or Beginner C++ developers curious about Self-Driving It is particularly useful for Self-Driving enthusiast or Makers and Hobbists keen on robotics or Software developers taht wants to learn ROS and Robotics or Students or Engineers that wants to learn how to buid a robot from scratch or Developers that already knows ROS and that want to use it in a real world application or Robotics Engineers that wants to develop skills in Autonomous Navigation or Beginner Python developers curious about Self-Driving or Beginner C++ developers curious about Self-Driving.
Enroll now: Self Driving and ROS – Learn by Doing! Odometry & Control
Summary
Title: Self Driving and ROS – Learn by Doing! Odometry & Control
Price: $94.99
Average Rating: 4.43
Number of Lectures: 134
Number of Quizzes: 2
Number of Published Lectures: 134
Number of Published Quizzes: 2
Number of Curriculum Items: 138
Number of Published Curriculum Objects: 138
Original Price: €99.99
Quality Status: approved
Status: Live
What You Will Learn
- Create a Real Self-Driving Robot
- Mastering ROS, the Robot Operating System
- Implement Sensor Fusion algorithms
- Simulate a Self-Driving robot in Gazebo
- Develop a Controller
- Odometry and Localization
- Kalman Filters and Extended Kalman Filter
- Probability Theory
- Differential Kinematics
- Create a Digital Twin of a Self-Driving Robot
- Master the TF library
Who Should Attend
- Self-Driving enthusiast
- Makers and Hobbists keen on robotics
- Software developers taht wants to learn ROS and Robotics
- Students or Engineers that wants to learn how to buid a robot from scratch
- Developers that already knows ROS and that want to use it in a real world application
- Robotics Engineers that wants to develop skills in Autonomous Navigation
- Beginner Python developers curious about Self-Driving
- Beginner C++ developers curious about Self-Driving
Target Audiences
- Self-Driving enthusiast
- Makers and Hobbists keen on robotics
- Software developers taht wants to learn ROS and Robotics
- Students or Engineers that wants to learn how to buid a robot from scratch
- Developers that already knows ROS and that want to use it in a real world application
- Robotics Engineers that wants to develop skills in Autonomous Navigation
- Beginner Python developers curious about Self-Driving
- Beginner C++ developers curious about Self-Driving
Would you like to build a real Self-Driving Robotusing ROS, the Robot Operating System?
Would you like to get started with Autonomous Navigation of Robot and dive into the theoretical and practical aspects of Odometry and Localization from industry experts?
The philosophy of this course is the Learn by Doing and quoting the American writer and teacher Dale Carnegie
Learning is an Active Process. We learn by doing, only knowledge that is used sticks in your mind.
In order for you to master the concepts covered in this course and use them in your projects and also in your future job, I will guide you through the learning of all the functionalities of ROS both from the theoretical and practical point of view.
Each section is composed of three parts:
-
Theoreticalexplanation of the concept and functionality
-
Usage of the concept in a simple Practicalexample
-
Application of the functionality in a real Robot
There is more!
All the programming lessons are developed both using Python and C++ . This means that you can choose the language you are most familiar with or become an expert Robotics Software Developer in both programming languages!
By taking this course, you will gain a deeper understanding of self-driving robots and ROS, which will open up opportunities for you in the exciting field of robotics.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Course Motivation
Lecture 2: The Self-Driving Program
Lecture 3: Course Presentation
Lecture 4: Meet your Teacher
Lecture 5: [BONUS]: Boost your Robotics Software Developer Career
Lecture 6: Get the Most out of the Course
Lecture 7: Course Material
Chapter 2: Setup
Lecture 1: Install Ubuntu on Virtual Machine
Lecture 2: Install Ubuntu on Dual Boot
Lecture 3: Install ROS
Lecture 4: Configure the Development Environment
Chapter 3: ROS Introduction
Lecture 1: Why a Robot Operating System?
Lecture 2: What is ROS
Lecture 3: Hardware Abstraction
Lecture 4: Low-Level Device Control
Lecture 5: Messaging between Process
Lecture 6: Package Management
Lecture 7: Architecture of a ROS Application
Lecture 8: <LAB>Create and Activate a Workspace</LAB>
Lecture 9: <PY>Simple Publisher</PY>
Lecture 10: <C++>Simple Publisher</C++>
Lecture 11: <PY>Simple Subscriber</PY>
Lecture 12: <C++>Simple Subscriber</C++>
Chapter 4: Locomotion
Lecture 1: Robot Locomotions
Lecture 2: Mobile Robots
Lecture 3: Friction Effects
Lecture 4: Robot Description
Lecture 5: URDF
Lecture 6: <LAB>Create the URDF Model</LAB>
Lecture 7: RViz
Lecture 8: Parameter Server
Lecture 9: <LAB>Parameter Server</LAB>
Lecture 10: <LAB>Visualize the Robot</LAB>
Lecture 11: Launch Files
Lecture 12: <LAB>Visualize the Robot with Launch Files</LAB>
Lecture 13: Gazebo
Lecture 14: <LAB>Simulate the Robot</LAB>
Lecture 15: <LAB>Launch the Simulation</LAB>
Chapter 5: Control
Lecture 1: ROS Control
Lecture 2: Control Types
Lecture 3: <LAB>ROS Control with Gazebo</LAB>
Lecture 4: YAML Configuration File
Lecture 5: <LAB>YAML Configuration File</LAB>
Lecture 6: <LAB>Launch the Controller</LAB>
Chapter 6: Kinematics
Lecture 1: Robot Kinematics
Lecture 2: Pose of a Mobile Robot
Lecture 3: Translation Vector
Lecture 4: <LAB>Introduction to Turtlesim</LAB>
Lecture 5: <PY>Translation Vector</PY>
Lecture 6: <C++>Translation Vector</C++>
Lecture 7: Rotation Matrix
Lecture 8: <PY>Rotation Matrix</PY>
Lecture 9: <C++>Rotation Matrix</C++>
Lecture 10: Transformation Matrix
Chapter 7: Differential Kinematics
Lecture 1: Differential Kinematics
Lecture 2: Velocity of a Mobile Robot
Lecture 3: Linear Velocity
Lecture 4: Angular Velocity
Lecture 5: Velocity in World Frame
Lecture 6: Differential Forward Kinematics
Lecture 7: Simple Speed Controller
Lecture 8: <PY>Simple Speed Controller</PY>
Lecture 9: <C++>Simple Speed Controller</C++>
Lecture 10: <LAB>Teleoperating with Joystick</LAB>
Lecture 11: <LAB>Using the diff_drive_controller</LAB>
Chapter 8: TF Library
Lecture 1: The TF Library
Lecture 2: Operations with Transformations
Lecture 3: Static and Dynamic Transformations
Lecture 4: <PY>Simple TF Static Broadcaster</PY>
Lecture 5: <C++>Simple TF Static Broadcaster</C++>
Lecture 6: ROS Timer
Lecture 7: <PY>ROS Timer</PY>
Lecture 8: <C++>ROS Timer</C++>
Lecture 9: <PY>Simple TF Broadcaster</PY>
Lecture 10: <C++>Simple TF Broadcaster</C++>
Lecture 11: ROS Services
Lecture 12: <PY>Service Server</PY>
Lecture 13: <C++>Service Server</C++>
Lecture 14: <PY>Service Client</PY>
Lecture 15: <C++>Service Client</C++>
Lecture 16: <PY>Simple TF Listener</PY>
Lecture 17: <C++>Simple TF Listener</C++>
Lecture 18: Angle Rapresentations
Lecture 19: Euler Angles
Lecture 20: Quaternion
Lecture 21: <PY>Euler to Quaternion</PY>
Lecture 22: <C++>Euler to Quaternion</C++>
Lecture 23: <LAB>TF Tools</LAB>
Chapter 9: Odometry
Instructors
-
Antonio Brandi
Robot Autonomous Navigation Engineer
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 0 votes
- 3 stars: 8 votes
- 4 stars: 54 votes
- 5 stars: 122 votes
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