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Self Driving and ROS - Learn by Doing! Odometry & Control

Self Driving and ROS - Learn by Doing! Odometry & Control

Loại khoá học: Data Science

Create a Self-Driving robot and learn about Robot Localization and Sensor Fusion using Kalman Filters

image
50.000 VND
1.599.000 VND
Đầy Đủ Bài Giảng
Học Online Tiện Lợi
Kích Hoạt Nhanh 2-5 Phút
Thanh toán tự động
Được phép tải xuống

Tổng quan

Nội dung khoá học

Đánh giá

Mô tả

Would you like to build a real Self-Driving Robot using 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:

  • Theoretical explanation of the concept and functionality

  • Usage of the concept in a simple Practical example

  • 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.

Bạn sẽ học được gì

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

Yêu cầu

  • Basic knowledge of Python or C++
  • Basic knowledge of Linux
  • No prior knowledge of ROS required
  • No prior knowledge of Robotics theory required
  • No hardware required. All the course can be followed also using only the PC

Nội dung khoá học

12 sections

Introduction

7 lectures
Course Motivation
02:53
The Self-Driving Program
03:29
Course Presentation
06:16
Meet your Teacher
01:45
[BONUS]: Boost your Robotics Software Developer Career
00:44
Get the Most out of the Course
03:49
Course Material
01:02

Setup

4 lectures
Install Ubuntu on Virtual Machine
02:16
Install Ubuntu on Dual Boot
03:32
Install ROS
03:53
Configure the Development Environment
06:43

ROS Introduction

14 lectures
Why a Robot Operating System?
04:50
What is ROS
03:24
Hardware Abstraction
03:11
Low-Level Device Control
01:40
Messaging between Process
07:56
Package Management
01:46
Architecture of a ROS Application
02:48
Introduction to ROS
6 questions
<LAB>Create and Activate a Workspace</LAB>
11:07
<PY>Simple Publisher</PY>
17:50
<C++>Simple Publisher</C++>
17:38
<PY>Simple Subscriber</PY>
11:56
<C++>Simple Subscriber</C++>
12:21
Workspaces, Publishers, Subscribers
5 questions

Locomotion

16 lectures
Robot Locomotions
06:39
Mobile Robots
04:53
Friction Effects
09:54
Robot Description
04:03
URDF
05:28
<LAB>Create the URDF Model</LAB>
23:01
RViz
05:55
Parameter Server
04:56
<LAB>Parameter Server</LAB>
06:16
<LAB>Visualize the Robot</LAB>
08:02
Launch Files
04:26
<LAB>Visualize the Robot with Launch Files</LAB>
08:48
Add a Can on top of your Robot
2 questions
Gazebo
05:03
<LAB>Simulate the Robot</LAB>
17:13
<LAB>Launch the Simulation</LAB>
13:49

Control

6 lectures
ROS Control
09:42
Control Types
06:05
<LAB>ROS Control with Gazebo</LAB>
08:29
YAML Configuration File
03:47
<LAB>YAML Configuration File</LAB>
07:56
<LAB>Launch the Controller</LAB>
13:37

Kinematics

10 lectures
Robot Kinematics
03:52
Pose of a Mobile Robot
03:53
Translation Vector
04:46
<LAB>Introduction to Turtlesim</LAB>
13:24
<PY>Translation Vector</PY>
21:46
<C++>Translation Vector</C++>
27:09
Rotation Matrix
08:14
<PY>Rotation Matrix</PY>
10:10
<C++>Rotation Matrix</C++>
10:32
Transformation Matrix
03:39

Differential Kinematics

11 lectures
Differential Kinematics
01:36
Velocity of a Mobile Robot
03:17
Linear Velocity
06:04
Angular Velocity
05:28
Velocity in World Frame
05:05
Differential Forward Kinematics
04:08
Simple Speed Controller
02:03
<PY>Simple Speed Controller</PY>
31:48
<C++>Simple Speed Controller</C++>
35:16
<LAB>Teleoperating with Joystick</LAB>
11:25
<LAB>Using the diff_drive_controller</LAB>
22:14

TF Library

23 lectures
The TF Library
05:23
Operations with Transformations
07:09
Static and Dynamic Transformations
03:25
<PY>Simple TF Static Broadcaster</PY>
16:54
<C++>Simple TF Static Broadcaster</C++>
20:15
ROS Timer
05:12
<PY>ROS Timer</PY>
06:00
<C++>ROS Timer</C++>
06:16
<PY>Simple TF Broadcaster</PY>
12:38
<C++>Simple TF Broadcaster</C++>
14:14
ROS Services
05:48
<PY>Service Server</PY>
14:49
<C++>Service Server</C++>
17:11
<PY>Service Client</PY>
12:18
<C++>Service Client</C++>
13:56
<PY>Simple TF Listener</PY>
19:22
<C++>Simple TF Listener</C++>
19:57
Angle Rapresentations
02:02
Euler Angles
04:46
Quaternion
04:29
<PY>Euler to Quaternion</PY>
11:00
<C++>Euler to Quaternion</C++>
10:50
<LAB>TF Tools</LAB>
08:13

Odometry

15 lectures
Where is the Robot?
03:08
The Local Localization Challenge
06:37
Wheel Odometry
07:49
Differential Inverse Kinematics
05:33
<PY>Differential Inverse Kinematic</PY>
16:08
<C++>Differential Inverse Kinematic</C++>
18:01
Wheel Odometry - Position
03:17
Wheel Odometry - Orientation
03:38
<PY>Wheel Odometry</PY>
10:04
<C++>Wheel Odometry</C++>
09:14
<PY>Publish Odometry Message</PY>
14:22
<C++>Publish Odometry Message</C++>
14:51
Draw the Robot's Trajectory
3 questions
<PY>Broadcast Odometry Transform</PY>
12:09
<C++>Broadcast Odometry Transform</C++>
12:44

Probability for Robotics

11 lectures
Motivation
07:09
Random Variables
08:55
Conditional Probability
07:18
Probability Distributions
08:40
Gaussian Distributions
04:52
Total Probability Theorem
05:44
Bayes Rule
05:11
Sensor Noise
02:37
<PY>Adding Noise to Robot Motion</PY>
14:48
<C++>Adding Noise to Robot Motion</C++>
18:44
<LAB>Odometry Comparison</LAB>
08:05

Sensor Fusion

18 lectures
Advantages of having Multiple Sensors
06:28
Gyroscope
03:38
Accelerometer and IMU
03:30
<LAB>Simulate IMU Sensor</IMU>
12:23
Kalman Filter
06:28
<PY>Filter Initialization</PY>
14:18
<C++>Filter Initialization</C++>
19:30
Measurement Update
02:23
<PY>Measurement Update</PY>
04:58
<C++>Measurement Update<C++>
06:02
State Prediction
02:33
<PY>State Prediction</PY>
12:55
<C++>State Prediction</C++>
11:48
<LAB>Localization with Kalman Filter</LAB>
06:51
Extended Kalman Filter (EKF)
04:24
<PY>IMU Republisher</PY>
04:47
<C++>IMU Republisher</C++>
06:16
<LAB>Sensor Fusion with robot_localization</LAB>
18:44

Conclusions

3 lectures
Recap
02:34
What's Next?
01:51
[BONUS]: Continue Learning
00:44

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