Vision-based control of a knuckle boom crane with online cable length estimation

Abstract

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 the estimation of payload angles and angular velocity. The length of the payload cable is also estimated using the 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.