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 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.
Abstract-Trajectory generation and control of large equipment in open field environments involves systematically and robustly operating in uncertain and dynamic terrain. This paper presents an integrated motion planning and control system for tracked vehicles. Flexible path-end adjustments and adaptive look-ahead are introduced to a state lattice planning approach with waypoint control. For a given processing horizon, this increases search coverage and reduces planning error.This tramming approach has been successfully fielded on a 98-ton autonomous blast hole drill rig used in iron ore mining in Western Australia. The system has undergone extensive testing and is now integrated into a production environment. This work is a key element in a larger program aimed at developing a fully autonomous, remotely operated mine.
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