Visual servoing is concerned with the motion of points in the world. How can we reliably detect such points using computer vision techniques.
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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.
Electric motors are typically quite weak, they produce a low torque, so it’s very common to add a reduction gearbox.
There is a lot of information in an image which we need to summarize somehow. An intensity histogram is one form of summary that provides useful information about how well the exposure of our camera is adjusted.
It is common to think about an assembly task being specified in terms of coordinates in the 3D world. An alternative approach is to consider the task in terms of the relative position of objects in one or more views of the task — visual servoing.
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.
We can model a DC motor as a resistor and a voltage source, and then understand the implications of controlling either the voltage or current supplied to the motor. We also learn about common methods for motor control such as the H-bridge driver and pulse width modulation.
We describe the velocity coupling terms of the robot as a matrix which represents how the torque on one joint depends on the velocity of other joints.
We describe inertia of the robot as a matrix which represents how inertia of a joint depends on the position of all the joints, and how the torque on one joint depends on the acceleration of other joints.
We resume our analysis of the 6-link robot Jacobian and focus on the rotational velocity part.