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 labelling 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.

There is a strong push within the mining sector to develop and adopt automation technology, including autonomous vehicles such as excavators, trucks and drills. However, for autonomous systems to operate effectively in this domain, new perception capabilities are required to build rich models of a mine. A key element of this is an ability to sense and model the sub-surface geological structure as well as the more traditional robotic models, which typically estimate terrain and obstacles. This paper presents a new automated geological perception system to support autonomous mining. It uses hyperspectral imaging sensors and a supervised learning algorithm to detect and classify geological structures, and ultimately build a rich model of the operating environment. The presented algorithm uses Gaussian Processes (GPs) and an Observation Angle Dependent (OAD) covariance function. Further, the resulting geological model can be improved by fusing data from two hyperspectral scanners which measure different regions of the spectrum. The approach is demonstrated using data from an operational iron-ore mine. Fusion of classification results from the two sensors shows better agreement with ground truth mapping done in the field, compared to results from individual sensors.

UN METODO Y SISTEMA PARA REGULAR EL MOVIMIENTO DE UNA ENTIDAD ENTRE UNA PRIMERA ZONA Y UNA SEGUNDA ZONA ADYACENTE A LA PRIMERA ZONA, EN UN SISTEMA DE AUTOMATIZACION DE MINAS, QUE COMPRENDE: A) SUMINISTRAR UNA JERARQUIA DE CONTROLADORES QUE CORRESPONDE A UNA JERARQUIA DE ZONAS EN UNA REGION GEOGRAFICA DEFINIDA, LA JERARQUIA DE CONTROLADORES COMPRENDE: UN PRIMER CONTROLADOR ASOCIADO CON LA PRIMERA ZONA, UN SEGUNDO CONTROLADOR ASOCIADO CON LA SEGUNDA ZONA; Y UN CONTROLADOR DE SUPERVISION OPERABLE PARA SUPERVISAR EL PRIMER Y EL SEGUNDO CONTROLADOR EN LA JERARQUIA DE CONTROLADORES, EL CONTROLADOR DE SUPERVISION ASOCIADO CON UNA ZONA MADRE QUE ENCAPSULA LA PRIMERA Y SEGUNDA ZONA; B) EL CONTROLADOR DE SUPERVISION BUSCA AUTORIZACION DEL PRIMERO Y EL SEGUNDO CONTROLADOR PARA QUE LA ENTIDAD SE MUEVA DE LA PRIMERA ZONA A LA SEGUNDA ZONA; C) EL CONTROLADOR DE SUPERVISION ORDENA AL PRIMER CONTROLADOR INSTRUIR A LA ENTIDAD MOVERSE A UNA ZONA DE TRANSICION QUE ABARCA UN LIMITE ENTRE LA PRIMERA ZONA Y LA SEGUNDA ZONA; D) EL CONTROLADOR DE  SUPERVISION ORDENA AL SEGUNDO CONTROLADOR REGISTRAR LA ENTIDAD DE TAL MANERA QUE LA ENTIDAD RESPONDA AL CONTROL DE SUPERVISION DEL SEGUNDO CONTROLADOR EN LA MEDIDA EN QUE LA ENTIDAD SE MUEVE DE LA ZONA DE TRANSICION A LA SEGUNDA ZONA; Y E) EL CONTROLADOR DE SUPERVISION ORDENA AL PRIMER CONTROLADOR CANCELAR EL REGISTRO DE LA ENTIDAD DE TAL MANERA QUE LA ENTIDAD YA NO RESPONDA AL CONTROL DE SUPERVISION POR PARTE DEL PRIMER CONTROLADOR.

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.