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

TL;DR

This paper presents a vision-based control system for a knuckle boom crane that estimates payload angles and cable length in real-time, enabling precise tip control and oscillation damping through a cascade control approach.

Contribution

It introduces a novel integrated system combining vision-based payload tracking, cable length estimation, and cascade control for improved crane operation.

Findings

Effective payload angle and cable length estimation using cameras and Kalman filtering.

Successful experimental validation on a scaled crane demonstrating oscillation damping.

Enhanced control accuracy and stability in crane tip positioning.

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 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.