Search Results for: rotation matrix

We learn how to create smoothly varying orientation in 3D by interpolating Euler angles and Quaternions. In the MATLAB example starting at 5:44 I use the Quaternion class.  For Toolbox version 10 (2017) please use UnitQuaternion instead.

We consider the simplest possible robot, which has one rotary joint and an arm.

We introduce serial-link robot manipulators, the sort of robot arms you might have seen working in factories doing tasks like welding, spray painting or material transfer. We will learn how we can compute the pose of the robot’s end-effector given knowledge of the robot’s joint angles and the dimensions of its links.

We summarise the important points from this lecture.

We extend what we have learnt to a 3-link planar robot where we can also consider the rotational velocity of the end-effector.

We consider a robot, which has two rotary joints and an arm.

Now we introduce a variant of the Jacobian matrix that can relate our angular velocity vector back to our rates of change of the roll, pitch and yaw angles.

When a camera moves in the world, points in the image move in a very specific way. The image plane or pixel velocity is a function of the camera’s motion and the position of the points in the world. This is known as optical flow. Let’s explore the link between camera and image motion.

For real robots such as those with 6 joints that move in 3D space the inverse kinematics is quite complex, but for many of these robots the solutions have been helpfully derived by others and published. Let’s explore the inverse kinematics of the classical Puma 560 robot.

For a redundant robot the inverse kinematics can be easily solved using a numerical approach.