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Updated in [October 07th, 2023]
What does this course tell?
(Please note that the following overview content is from the original platform)
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 expertsThe 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 mindIn 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 throught the learning of all the functionalities of ROS both from the theoretical and practical point of viewEach section is composed od three parts:Theoretical explanation of the concept and functionalityUsage of the concept in a simple Practical exampleApplication of the functionality in a real RobotThere 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 progremming 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
We considered the value of this course from many aspects, and finally summarized it for you from two aspects: skills and knowledge, and the people who benefit from it:
(Please note that our content is optimized through artificial intelligence tools and carefully reviewed by our editorial staff.)
What skills and knowledge will you acquire during this course?
During this course, the learner will acquire the following skills and knowledge:
1. Understanding of the theoretical and practical aspects of Odometry and Localization.
2. Proficiency in using ROS (Robot Operating System) for building a real Self-Driving Robot.
3. Ability to navigate autonomously using the concepts learned.
4. Knowledge of the functionalities of ROS from both theoretical and practical perspectives.
5. Experience in applying the learned concepts in real-world robot applications.
6. Programming skills in both Python and C++, specifically for robotics development.
7. Option to choose the preferred programming language (Python or C++) for robotics software development.
8. Deeper understanding of self-driving robots and ROS, opening up opportunities in the field of robotics.
Who will benefit from this course?
This course will benefit individuals who are interested in building a real Self-Driving Robot using ROS, the Robot Operating System. It is suitable for both beginners and experienced individuals who want to dive into the theoretical and practical aspects of Odometry and Localization.
Professionals in the field of robotics, such as Robotics Software Developers, will benefit from this course as it provides a comprehensive understanding of ROS and its functionalities. It will enhance their skills and knowledge, making them more competitive in the job market.
Engineers and researchers working in the autonomous navigation industry will also benefit from this course. It will provide them with the necessary knowledge and practical examples to apply in their projects and future jobs.
Individuals interested in pursuing a career in robotics or autonomous systems will find this course valuable. It will give them a solid foundation in self-driving robots and ROS, opening up opportunities in the exciting field of robotics.
Moreover, the course offers programming lessons in both Python and C++, allowing learners to choose the language they are most familiar with or become proficient in both programming languages. This flexibility caters to a wide range of individuals with different programming backgrounds.
Course Syllabus
Introduction
Setup
ROS Introduction
Locomotion
Control
Kinematics
Differential Kinematics
TF Library
Odometry
Probability for Robotics
Sensor Fusion
Conclusions