ICRA 2011 Paper Abstract


Paper TuP1-InteracInterac.42

Zhou, Hang (University of Sydney), Hatherly, Peter (University of Sydney), Ramos, Fabio (University of Sydney), Nettleton, Eric (The University of Sydney)

An Adaptive Data Driven Model for Characterizing Rock Properties from Drilling Data

Scheduled for presentation during the Poster Sessions "Interactive Session II: Systems, Control and Automation" (TuP1-InteracInterac), Tuesday, May 10, 2011, 13:40−14:55, Hall

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, Learning and Adaptive Systems, Recognition


Autonomous operation of blast hole drill rigs requires monitoring of drilling parameters known as ``Measurement While Drilling" (MWD) data. From these data, rock properties can be inferred. A supervised classification scheme is usually used to map MWD data inputs to rock type outputs given some labeled training data. However, the geology has no definite ground truth that can allow a reliable labeling of the training data, nor is there a clear input-output pair connection between the MWD data and the rock types. In this paper, an adaptive unsupervised approach is proposed to estimate the rock types in a data driven way by minimizing the entropy gradient of the characterizing measure - ``Optimized Adjusted Penetration Rate" (OAPR). Neither data labeling nor fixed model parameters are required because of the data driven nature of the algorithm. Experimental results illustrate the effectiveness of our solution.



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