Search Results for: joint control

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 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.

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 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.

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 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.

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.

Actuators are the components that actually move the robot’s joint. So let’s look at a few different actuation technologies that are used in robots.

As we did for the simple planar robots we can invert the Jacobian and perform resolved-rate motion control.