We start by considering the effect of gravity acting on a robot arm, and how the torque exerted will disturb the position of the robot controller leading to a steady state error. Then we discuss a number of strategies to reduce this error.
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Let’s recap what robots are, where they’ve come from and what they can do. Robots use sensors to understand their world and plan an action to achieve their goal.
A robot can use a camera to capture an image of the world. The image contains millions of pixels, but the value of each pixel is not particularly informative about what’s present in the scene. We need a more concise or ‘higher level’ way to represent the information, and this is what we refer to […]
Let’s recap the important points from the topics we have covered about image features, blobs, connectivity analysis, and blob parameters such as centroid position, area, bounding box, moments, equivalent ellipse, and perimeter.
We will compare and contrast the terms image processing, computer vision and robotic vision — they have much in common but there are some subtle but important distinctions. When it comes to interpreting an image we typically try to find and describe regions, lines and interest points.
For an image stored as a variable in the MATLAB workspace let’s look at how we access the values of individual pixels in an image using their row and column coordinates. Using the MATLAB colon operator we can extract an intensity profile, extract a submatrix which is a region of the image, flip the image […]
Once a digital image exists as a matrix in the MATLAB workspace we can manipulate it to extract information that a robot could use. We will discuss some fundamental algorithms that operate on single images.
We recap the important points from this masterclass.
We summarise the important points from this lecture.