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Robotic Vision: Making Robots See

Create your own robotic vision system as you take part in a vision project and learn how to write the code to make your robot see.

4,292 enrolled on this course

Robotic Vision: Making Robots See
  • Duration

    3 weeks
  • Weekly study

    3 hours

Learn about the functions you need to program a robotic vision system

This three-week course will guide you through the essential skills needed to make a robot see.

You’ll develop your knowledge of image geometry before learning the programming skills used in robotic vision.

You’ll gain an in-depth understanding of robotics and practical skills as you’re guided by experts at Queensland University of Technology to complete a robotic vision system.

Refine your skills using MATLAB

You’ll cement your understanding of robotic vision by completing MATLAB exercises to see these processes in action.

You’ll learn how to demonstrate basic coding in MATLAB for calibration, shape classification, and workspace coordination.

This will help you build the practical skills to use in robotic programming.

Grow your knowledge of computer vision to create a vision system

You’ll take part in a robotic vision programming project to hone your skills and learn important functions such as improving colour segmentation, detecting shape and size, improving your homography matrix, rectifying your image, and forming a complete vision system.

Along the way, you’ll be able to reflect on your robotic vision systems as well as your peers’ projects to understand what makes a successful system.

As an optional project, if you have built or bought a robot, you’ll also learn the information needed for integrating your vision system.

What topics will you cover?

  • homogeneous coordinates
  • image formation
  • planar homography
  • colour segmentation
  • blobs and their properties, such as size, shape and position
  • homography matrices
  • vision system integration (optional).

When would you like to start?

  • Date to be announced

Add to Wishlist to be emailed when new dates are announced

Learning on this course

You can take this self-guided course and learn at your own pace. On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.

What will you achieve?

By the end of the course, you‘ll be able to...

  • Apply your knowledge of computer vision to create a vision system
  • Demonstrate basic coding in MATLAB for calibration, shape classification and workspace coordination
  • Reflect on the success of your and your peers’ robotic vision systems

Who is the course for?

This course is designed for those with some programming knowledge and concepts from advanced high-school mathematics or undergraduate engineering,

You can enrol in the MATLAB Onramp tutorial here.

What software or tools do you need?

The course requires you to code your robot vision system in MATLAB. You will need to download the full MATLAB software to a computer. With support from MathWorks, free access to MATLAB will be provided for the duration of the course plus 30 days.

Optional robot arm project

The purpose of this course is to program a robotic vision system, and optionally to integrate it with a robot to perform a simple, visual task. If you completed the course Introducing Robotics: Build a Robot Arm, you may already have a working robot arm you can use; or you might choose to purchase a LEGO MINDSTORMS NXT or EV3 development kit or something equivalent to it, or to borrow hobby robot components. This course does not run through how to assemble your robot arm, but rather provides all of the task instructions, demonstrations and worksheets for programming the vision system.

There are many ways to integrate the vision system and some of the most common approaches are:

1. Computer vision and robotics control on your computer

An attached web camera is used to acquire images that you process, to display results and to send motion commands to the robot. You will require a 64-bit computer as well as the full MATLAB software. There are many options to control the robot depending on the technology that you use to create it, for example:

a. MINDSTORMS NXT toolbox (NXT kits) or EV3 require custom software toolboxes to control your robot.

b. Arduino or RaspberryPi robot controllers might require a serial, WiFi or Ethernet cable connection to allow the MATLAB code to command it.

2. Computer vision on your computer

An attached web camera is used to acquire images that you process and display results for. You will require a 64-bit computer as well as the full MATLAB software.

3. Computer vision in the cloud

Your image processing works in an offline mode: you capture images of the worksheet using any camera and upload them to MATLAB Online using MATLAB Drive, where it is accessible by your program.

You can discuss your design ideas and options with your peers and the course mentors.

Who will you learn with?

Professor of Robotic Vision at QUT and Director of the Australian Centre for Robotic Vision (ACRV). Peter is also an IEEE fellow and on the editorial board of several robotics research journals.

PhD Candidate with the Australian Centre for Robotic Vision researching towards robust visual object recognition to facilitate useful robotic tasks.

Dr Pepperell completed his PhD in robotic vision at QUT in 2016, with a research focus in vision-based place recognition.

Hello! I'm Obadiah, a sessional academic at Queensland University of Technology. I've taught robotics and control engineering courses and MOOCs, and have worked as a robotic vision researcher at QUT.

Who developed the course?

Queensland University of Technology

QUT is a leading Australian university ranked in the top 1% of universities worldwide by the 2019 Times Higher Education World University Rankings. Located in Brisbane, it attracts over 50,000 students.

  • Established

    1989
  • Location

    Brisbane, Australia
  • World ranking

    Top 180Source: Times Higher Education World University Rankings 2019

Learning on FutureLearn

Your learning, your rules

  • Courses are split into weeks, activities, and steps, but you can complete them as quickly or slowly as you like
  • Learn through a mix of bite-sized videos, long- and short-form articles, audio, and practical activities
  • Stay motivated by using the Progress page to keep track of your step completion and assessment scores

Join a global classroom

  • Experience the power of social learning, and get inspired by an international network of learners
  • Share ideas with your peers and course educators on every step of the course
  • Join the conversation by reading, @ing, liking, bookmarking, and replying to comments from others

Map your progress

  • As you work through the course, use notifications and the Progress page to guide your learning
  • Whenever you’re ready, mark each step as complete, you’re in control
  • Complete 90% of course steps and all of the assessments to earn your certificate

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