We will learn the essentials of inertial navigation, about sensors such as accelerometers, gyroscopes and magnetometers and how we can use the information they provide to estimate our motion and orientation in 3D space.
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We learn how accelerometers and gyroscopes can be combined into an inertial navigation system capable of estimating position and orientation of a vehicle, without GPS.
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.
We consider the simplest possible robot, which has one rotary joint and an arm.
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])
A problem arises when using three-angle sequences and particular values of the middle angle leads to a condition called a singularity. This mathematical phenomena is related to a problem that occurs in the physical world with mechanical gimbal systems. Note that in Robotics, Vision & Control (second edition) and RTB10.x the default definition of roll-pitch-yaw […]
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.
We will use Simulink to create a dynamic model of a single robot joint and simulate its operation.
We learn how to describe the orientation of an object by a 2×2 rotation matrix which has some special properties. Try your hand at some online MATLAB problems. You’ll need to watch all the 2D “Spatial Maths” lessons to complete the problem set.
We revisit the simple 2-link planar robot and determine the inverse kinematic function using simple geometry and trigonometry.