ICRA 2011 Paper Abstract


Paper TuP112.4

Bewley, Alex (CRCMining, University of Queensland), Shekhar, Rajiv (University of Queensland), Leonard, Sam (CRCMining, University of Queensland), Upcroft, Ben (University of Queensland), Lever, Paul (CRCMining, University of Queensland)

Real-Time Volume Estimation of a Dragline Payload

Scheduled for presentation during the Regular Sessions "Field and Underwater Robotics I" (TuP112), Tuesday, May 10, 2011, 14:25−14:40, Room 5H

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on March 30, 2020

Keywords Mining Robotics, Field Robots, Range Sensing


This paper presents a method for measuring the in-bucket payload volume on a dragline excavator for the purpose of estimating the material's bulk density in real-time. Knowledge of the payload's bulk density can provide feedback to mine planning and scheduling to improve blasting and therefore provide a more uniform bulk density across the excavation site. This allows a single optimal bucket size to be used for maximum overburden removal per dig and in turn reduce costs and emissions in dragline operation and maintenance.

The proposed solution uses a range bearing laser to locate and scan full buckets between the lift and dump stages of the dragline cycle. The bucket is segmented from the scene using cluster analysis, and the pose of the bucket is calculated using the Iterative Closest Point (ICP) algorithm. Payload points are identified using a known model and subsequently converted into a height grid for volume estimation. Results from both scaled and full scale implementations show that this method can achieve an accuracy of above 95%.



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