We will introduce resolved-rate motion control which is a classical Jacobian-based scheme for moving the end-effector at a specified velocity without having to compute inverse kinematics.
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We will learn about how we make the the robot joints move to the angles or positions that are required in order to achieve the desired end-effector motion. This is the job of the robot’s joint controller and in this lecture we will learn how this works. This journey will take us in to the […]
A robot joint is a mechatronic system comprising motors, sensors, electronics and embedded computing that implements a feedback control system.
We recap the important points from this masterclass.
Let’s recap the important points from the topics we have covered in our discussion of optical flow and visual servoing.
Actuators have finite capability, that is they have a maximum torque, velocity and power rating.
We use MATLAB and some Toolbox functions to create a robot controller that moves a camera so the image matches what we want it to look like. We call this an image-based visual servoing system.
If your knowledge of dynamics is a bit rusty then let’s quickly revise the basics of second-order systems and the Laplace operator. Not rusty? Then go straight to the next section.
Visual servoing is concerned with the motion of points in the world. How can we reliably detect such points using computer vision techniques.
So far we have worked out the torques on a robot’s joints based on joint position, velocity and acceleration. For simulation we want the opposite, to know its motion given the torques applied to the joints. This is called the forward dynamics problem.