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Daniel Romaniuk
In this one hour long project-based course, you will tackle a real-world problem in robotics. We will be simulating a robot that can move around in an unknown environment, and have it discover its own location using only a terrain map and an elevation sensor. We will encounter some of the classic challenges that make robotics difficult: noisy sensor data, and imprecise movement.
Read more
In this one hour long project-based course, you will tackle a real-world problem in robotics. We will be simulating a robot that can move around in an unknown environment, and have it discover its own location using only a terrain map and an elevation sensor. We will encounter some of the classic challenges that make robotics difficult: noisy sensor data, and imprecise movement.
Read more
In this one hour long project-based course, you will tackle a real-world problem in robotics. We will be simulating a robot that can move around in an unknown environment, and have it discover its own location using only a terrain map and an elevation sensor. We will encounter some of the classic challenges that make robotics difficult: noisy sensor data, and imprecise movement.
We will tackle these challenges with an artificial intelligence technique called a particle filter.
By the end of this project, you will have coded a particle filter from scratch using Python and numpy.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/g5ahtd/robot
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What's inside
Syllabus
Project Overview
In this one hour long project-based course, you will tackle a real-world problem in robotics. We will be simulating a robot that can move around in an unknown environment, and have it discover its own location using only a terrain map and an elevation sensor. We will encounter some of the classic challenges that make robotics difficult: noisy sensor data, and imprecise movement.
We will tackle these challenges with an artificial intelligence technique called a particle filter.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Builds knowledge in robotics and related AI algorithms
Introduces learners to particle filters
Uses Python and Numpy, which are popular packages for this topic
Is a project-based course for hands-on training
Requires learners to have some knowledge of terrain mapping and elevation sensing
The duration of the project is only an hour
Save this course
Save Robot Localization with Python and Particle Filters to your list so you can find it easily later:
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Reviews summary
Confident tutorial on particle filters
According to students, Robot Localization with Python and Particle Filters is a well-received introduction to particle filters. Learners report that the course is engaging and thorough. Though some students have concerns about the coding environment, most learners say that the course is well structured and easy to follow.
Course is understandable and approachable.
"Nice introduction... this is a clear instruction that is easy to follow a long."
"very good course"
Issues with online coding environment.
"Problems with the online coding space."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Robot Localization with Python and Particle Filters with these
activities:
Review the syllabus for this course
Show steps
This syllabus contains important information on what you will learn and how you will be assessed in this course.
Show steps
-
Read through the syllabus carefully.
-
Make note of any important dates or deadlines.
-
Identify any areas where you may need additional support.
Coding in Python and numpy
Show steps
This course uses Python and Numpy. Make sure you are comfortable with the topics covered in this activity.
Show steps
-
Review the basics of Python programming.
-
Review the basics of Numpy.
-
Practice coding with a simple robotics problem.
Read 'Robot Modelling and Control' by Mark W. Spong and M. Vidyasagar
Show steps
Reading this foundational book on robotics will greatly help with understanding the concepts touched on in this course.
View
Robot Modeling and Control
on Amazon
Show steps
-
Read Chapter 1 on robot kinematics.
-
Read Chapter 2 on robot dynamics.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Coding particle filter in Python
Show steps
Helps reinforce the main topic of this course: particle filters.
Browse courses on
Particle Filters
Show steps
-
Implement a basic particle filter in Python.
-
Test your particle filter on a simple robotics problem.
-
Refine your particle filter to improve accuracy.
Write a blog post on particle filters
Show steps
Writing about what you've learned will reinforce those lessons
Browse courses on
Particle Filters
Show steps
-
Choose a specific topic related to particle filters.
-
Do some research on the topic.
-
Write a blog post explaining the topic.
Find a tutorial on advanced robotics
Show steps
Finding advanced tutorials will help expand your understanding of robotics
Browse courses on
Robotics
Show steps
-
Search for tutorials on advanced robotics topics.
-
Watch or read a few tutorials.
Contribute to an open-source robotics project
Show steps
Contributing to the robotics community
Browse courses on
Robotics
Show steps
-
Find an open-source robotics project to contribute to.
-
Make a contribution to the project.
Participate in a robotics competition
Show steps
Applying practical knowledge in a competitive environment
Browse courses on
Robotics
Show steps
-
Find a robotics competition to participate in.
-
Build a robot for the competition.
-
Compete in the robotics competition.
Review the syllabus for this course
Show steps
This syllabus contains important information on what you will learn and how you will be assessed in this course.
Show steps
Show steps
Show steps
- Read through the syllabus carefully.
- Make note of any important dates or deadlines.
- Identify any areas where you may need additional support.
Coding in Python and numpy
Show steps
This course uses Python and Numpy. Make sure you are comfortable with the topics covered in this activity.
Show steps
Show steps
Show steps
- Review the basics of Python programming.
- Review the basics of Numpy.
- Practice coding with a simple robotics problem.
Read 'Robot Modelling and Control' by Mark W. Spong and M. Vidyasagar
Show steps
Reading this foundational book on robotics will greatly help with understanding the concepts touched on in this course.
View
Robot Modeling and Control
on Amazon
Show steps
Show steps
Show steps
- Read Chapter 1 on robot kinematics.
- Read Chapter 2 on robot dynamics.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Coding particle filter in Python
Show steps
Helps reinforce the main topic of this course: particle filters.
Browse courses on
Particle Filters
Show steps
Show steps
Show steps
- Implement a basic particle filter in Python.
- Test your particle filter on a simple robotics problem.
- Refine your particle filter to improve accuracy.
Write a blog post on particle filters
Show steps
Writing about what you've learned will reinforce those lessons
Browse courses on
Particle Filters
Show steps
Show steps
Show steps
- Choose a specific topic related to particle filters.
- Do some research on the topic.
- Write a blog post explaining the topic.
Find a tutorial on advanced robotics
Show steps
Finding advanced tutorials will help expand your understanding of robotics
Browse courses on
Robotics
Show steps
Show steps
Show steps
- Search for tutorials on advanced robotics topics.
- Watch or read a few tutorials.
Contribute to an open-source robotics project
Show steps
Contributing to the robotics community
Browse courses on
Robotics
Show steps
Show steps
Show steps
- Find an open-source robotics project to contribute to.
- Make a contribution to the project.
Participate in a robotics competition
Show steps
Applying practical knowledge in a competitive environment
Browse courses on
Robotics
Show steps
Show steps
Show steps
- Find a robotics competition to participate in.
- Build a robot for the competition.
- Compete in the robotics competition.
Career center
Learners who complete Robot Localization with Python and Particle Filters will develop knowledge and skills
that may be useful to these careers:
Robotics Engineer
Robotics Engineer
Robotics Engineers design, build, and test robots to perform various tasks in a wide range of industries, including manufacturing, healthcare, and space exploration. This course provides the foundation for understanding the principles of robot localization and particle filters, which are fundamental to the development and operation of autonomous robots. By gaining proficiency in these techniques, aspiring Robotics Engineers will be well-equipped to design and build robots capable of navigating complex and uncertain environments.
Autonomous Vehicle Engineer
Autonomous Vehicle Engineer
Autonomous Vehicle Engineers play a critical role in the development and testing of self-driving cars and other autonomous vehicles. The course provides a solid foundation in robot localization and particle filters, which are essential for enabling autonomous vehicles to safely and efficiently navigate their surroundings. By mastering these concepts, aspiring Autonomous Vehicle Engineers will gain a competitive edge in this rapidly growing field.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and implement AI solutions for various applications across industries. The course introduces the fundamentals of particle filters, a powerful AI technique used for state estimation and localization. By gaining proficiency in this technique, aspiring AI Engineers will enhance their ability to develop intelligent systems capable of operating in uncertain and dynamic environments.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop and deploy machine learning models for solving complex problems in diverse fields such as finance, healthcare, and manufacturing. The course provides a foundation in particle filters, a type of sequential Monte Carlo method used in machine learning for state estimation and localization. By mastering these techniques, aspiring Machine Learning Engineers will expand their skillset and become more effective in building and implementing robust machine learning solutions.
Data Scientist
Data Scientist
Data Scientists leverage data to extract insights and solve problems in fields such as finance, healthcare, and marketing. This course provides an introduction to particle filters, which are used for state estimation and localization in robotics and other applications. By gaining familiarity with these techniques, aspiring Data Scientists will enhance their ability to analyze and interpret data in the context of real-world problems.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems for a wide range of applications. This course introduces the fundamentals of particle filters, a technique used for state estimation and localization in robotics and other domains. By understanding these concepts, aspiring Software Engineers will expand their skillset and become more effective in developing software solutions for complex and uncertain environments.
Control Systems Engineer
Control Systems Engineer
Control Systems Engineers design and implement control systems for various applications, including robotics, manufacturing, and aerospace. This course provides a foundation in particle filters, a technique used for state estimation and localization in control systems. By gaining familiarity with these techniques, aspiring Control Systems Engineers will enhance their ability to design and implement robust control systems for complex and uncertain environments.
Navigation Systems Engineer
Navigation Systems Engineer
Navigation Systems Engineers design and develop navigation systems for vehicles, aircraft, and other platforms. This course introduces the fundamentals of particle filters, a technique used for state estimation and localization in navigation systems. By mastering these concepts, aspiring Navigation Systems Engineers will gain a competitive edge in this specialized field.
Geospatial Analyst
Geospatial Analyst
Geospatial Analysts analyze geospatial data to extract insights and solve problems in fields such as urban planning, environmental management, and transportation. This course provides an introduction to particle filters, a technique used for state estimation and localization in robotics and other applications. By gaining familiarity with these techniques, aspiring Geospatial Analysts will enhance their ability to analyze and interpret geospatial data in the context of real-world problems.
Robotic Technician
Robotic Technician
Robotic Technicians install, maintain, and repair robots used in various industries, including manufacturing, healthcare, and space exploration. This course provides a foundation in robot localization and particle filters, which are essential for the operation and maintenance of autonomous robots. By gaining proficiency in these techniques, aspiring Robotic Technicians will be well-equipped to support the growing field of robotics.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to extract insights and solve problems in various fields such as finance, healthcare, and marketing. This course may provide some useful concepts in particle filters, a technique used for state estimation and localization in robotics and other applications. By gaining familiarity with these techniques, aspiring Data Analysts may enhance their ability to analyze and interpret data in the context of real-world problems.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers design and develop computer vision systems for applications such as image recognition, object detection, and facial recognition. This course may provide some useful concepts in particle filters, a technique used for state estimation and localization in robotics and other applications. By gaining familiarity with these techniques, aspiring Computer Vision Engineers may enhance their ability to develop robust computer vision systems.
Mathematician
Mathematician
Mathematicians solve mathematical problems and develop new mathematical theories. This course may provide some useful concepts in particle filters, a technique used for state estimation and localization in robotics and other applications. By gaining familiarity with these techniques, aspiring Mathematicians may expand their knowledge in the field of applied mathematics.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to extract insights and solve problems in various fields such as finance, healthcare, and marketing. This course may provide some useful concepts in particle filters, a technique used for state estimation and localization in robotics and other applications. By gaining familiarity with these techniques, aspiring Statisticians may enhance their ability to analyze and interpret data in the context of real-world problems.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. This course may provide some useful concepts in particle filters, a technique used for state estimation and localization in robotics and other applications. By gaining familiarity with these techniques, aspiring Actuaries may enhance their ability to model and assess risk in the insurance and finance industries.
For more career information including salaries, visit:
OpenCourser.com/course/g5ahtd/robot
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Robot Localization with Python and Particle Filters.
This comprehensive textbook on probabilistic robotics, covering topics such as state estimation, localization, mapping, and motion planning. It valuable reference for anyone interested in learning more about the theory and practice of robotics.
Provides a comprehensive introduction to Bayesian filtering and smoothing, with a focus on their applications in signal processing and control. It valuable resource for anyone who wants to learn more about the theory and practice of Bayesian filtering.
Provides a clear and concise introduction to probability theory. It valuable resource for anyone who wants to learn more about the foundations of probability and its applications in various fields.
Provides a comprehensive introduction to robotics, covering topics such as kinematics, dynamics, and control. It valuable resource for anyone who wants to learn more about the theory and practice of robotics.
Provides a clear and concise introduction to linear algebra. It valuable resource for anyone who wants to learn more about the foundations of linear algebra and its applications in various fields.
Provides a clear and concise introduction to calculus. It valuable resource for anyone who wants to learn more about the foundations of calculus and its applications in various fields.
Save
Provides a comprehensive overview of deep learning, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for anyone who wants to learn more about the theory and practice of deep learning.
Save
Provides a comprehensive overview of computer vision, covering topics such as image processing, object recognition, and video analysis. It valuable resource for anyone who wants to learn more about the theory and practice of computer vision.
Provides a comprehensive overview of natural language processing, covering topics such as text processing, machine learning, and natural language generation. It valuable resource for anyone who wants to learn more about the theory and practice of natural language processing.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/g5ahtd/robot
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Effort
1 hour
Level
Advanced
Via
Coursera
Institution
Coursera Project Network
Instructor
Daniel Romaniuk
Language
English
Good to know
Builds knowledge in robotics and related AI algorithms
Introduces learners to particle filters
Uses Python and Numpy, which are popular packages for this topic
Is a project-based course for hands-on training
Requires learners to have some knowledge of terrain mapping and elevation sensing
The duration of the project is only an hour
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