We learn the principles behind ‘gyros’, sensors that measure angular velocity with respect to the universe.
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We learn the principles behind accelerometers, sensors that measure acceleration due to motion and due to the Earth’s gravitational field.
There are a lot of pixels in a typical image which makes them take up a lot of memory. Images can be compressed to take up less storage. Compression can be lossless or lossy, where we tradeoff size for quality.
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.
Let’s look at numerical approaches to inverse kinematics for a couple of different robots and learn some of the important considerations. For RTB10.x please note that the mask value must be explicitly preceded by the ‘mask’ keyword. For example: >> q = p2.ikine(T, [-1 -1], ‘mask’, [1 1 0 0 0 0])
We consider a robot with three joints that moves its end-effector on a plane.