Automatic in-bucket volume estimation for dragline operations

Abstract

This paper presents a method for calculating the in-bucket payload volume on a dragline for
the purpose of estimating the material’s bulk density in real-time. Knowledge of the bulk
density can provide instant feedback to mine planning and scheduling to improve blasting and
in turn provide a more uniform bulk density across the excavation site.
Furthermore costs and emissions in dragline operation, maintenance and downstream
material processing can be reduced. The main challenge is to determine an accurate position
and orientation of the bucket with the constraint of real-time performance.
The proposed solution uses a range bearing and tilt sensor to locate and scan the bucket
between the lift and dump stages of the dragline cycle. Various scanning strategies are
investigated for their benefits in this real-time application. The bucket is segmented from the
scene using cluster analysis while the pose of the bucket is calculated using the iterative
closest point (ICP) algorithm. Payload points are segmented from the bucket by a fixed
distance neighbour clustering method to preserve boundary points and exclude low density
clusters introduced by overhead chains and the spreader bar. A height grid is then used to
represent the payload from which the volume can be calculated by summing over the grid cells. We show volume calculated on a scaled system with an accuracy of greater than 95 per
cent.