  A vision-based controller for a knuckle boom crane is presented. The
controller is used to control the motion of the crane tip and at the same time
compensate for payload oscillations. The oscillations of the payload are
measured with three cameras that are fixed to the crane king and are used to
track two spherical markers fixed to the payload cable. Based on color and size
information, each camera identifies the image points corresponding to the
markers. The payload angles are then determined using linear triangulation of
the image points. An extended Kalman filter is used for estimation of payload
angles and angular velocity. The length of the payload cable is also estimated
using a least squares technique with projection. The crane is controlled by a
linear cascade controller where the inner control loop is designed to damp out
the pendulum oscillation, and the crane tip is controlled by the outer loop.
The control variable of the controller is the commanded crane tip acceleration,
which is converted to a velocity command using a velocity loop. The performance
of the control system is studied experimentally using a scaled laboratory
version of a knuckle boom crane.
