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.
Search Results for: inertia
When it comes to describing a blob we can do more than just area, centroid position and bounding box. By looking at second order moments we can compute an ellipse that has the same moments of inertia as the blob, and we can use its aspect ratio and orientation to describe the shape and orientation […]
We can factorise the joint torque expression into an elegant matrix equation with terms that describe the effects of inertia, Coriolis and centripetal and gravity effects.
We recap the important points from this masterclass.
A number of strategies exist to reduce the effect of these coupling torques between the joints, from introducing a gearbox between the motor and the joint, to advanced feedforward strategies.
In a serial-link manipulator arm each joint has to support all the links between itself and the end of the robot. We introduce the recursive Newton-Euler algorithm which allows us to compute the joint torques given the robot joint positions, velocities and accelerations and the link inertial parameters.
We will learn about the forces that are exerted on a robot’s joint by gravity acting on links, friction, and the coupling forces where the motion of one joint imparts a force on other joints.
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.
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.