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Let’s recap the important points from the topics we have covered in our discussion of optical flow and visual servoing.

Building a highly accurate robot is not trivial yet we can perform fine positioning tasks like threading a needle using hand-eye coordination. For a robot we call this visual servoing.

We will extend our coverage of image processing. We will some previously discussed techniques in more depth, and introduce some additional ones.

We can derive a linear relationship between the coordinates of points on an arbitrary plane in the scene and the coordinate of that point in the image. This is the planar homography and it has a number of everyday uses which might surprise you.

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.

Actuators have finite capability, that is they have a maximum torque, velocity and power rating.

We will use Simulink to create a dynamic model of a single robot joint and simulate its operation.

A robot joint controller is a type of feedback control system which is an old and well understood technique. We will learn how to assemble the various mechatronic components such as motors, gearboxes, sensors, electronics and embedded computing in a feedback configuration to implement a robot joint controller.

All mechanical systems exhibit friction and we learn about two broad classes of friction: linear and non-linear.

Electric motors are typically quite weak, they produce a low torque, so it’s very common to add a reduction gearbox.