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

Frequently Asked Questions

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