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Paper WeD06.1

Zhou, Hang (University of Sydney), Hatherly, Peter (University of Sydney), Monteiro, Sildomar (University of Sydney), Ramos, Fabio (University of Sydney), Oppolzer, Florian (University of Sydney), Nettleton, Eric (The University of Sydney), Scheding, Steven (The University of Sydney)

Automatic Rock Recognition from Drilling Performance Data

Scheduled for presentation during the Regular Session "Space Robotics" (WeD06), Wednesday, May 16, 2012, 16:30−16:45, Meeting Room 6 (Oya'te)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on October 24, 2017

Keywords Mining Robotics, Recognition

Abstract

Automated rock recognition is a key step for building a fully autonomous mine. When characterizing rock types from drill performance data, the main challenge is that there is not an obvious one-to-one correspondence between the two. In this paper, a hybrid rock recognition approach is proposed which combines Gaussian Process (GP) regression with clustering. Drill performance data is also known as Measurement While Drilling (MWD) data and a rock hardness measure - Adjusted Penetration Rate (APR) is extracted using the raw data in discrete drill holes. GP regression is then applied to create a more dense APR distribution, followed by clustering which produces discrete class labels. No initial labeling is needed. Comparisons are made with alternative measures of rock hardness from MWD data as well as state-of-the-art GP classification. Experimental results from an actual mine site show the effectiveness of our proposed approach.

 

 

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