Infrastructure-based localisation of automated coal mining equipment

• Main Authors: Chad O. Hargrave, Craig A. James, Jonathon C. Ralston
• Format: Article
• Language: English
• Published: SpringerOpen 2017-08-01
• Series: International Journal of Coal Science & Technology
• Subjects: Localisation; Waypoint navigation; Machine learning; Radar; Underground; Longwall mining
• Online Access: http://link.springer.com/article/10.1007/s40789-017-0180-3
• ISSN: 2095-8293; 2198-7823

Abstract A novel radar-based system for longwall coal mine machine localisation is described. The system, based on a radar-ranging sensor and designed to localise mining equipment with respect to the mine tunnel gate road infrastructure, is developed and trialled in an underground coal mine. The challenges of reliable sensing in the mine environment are considered, and the use of a radar sensor for localisation is justified. The difficulties of achieving reliable positioning using only the radar sensor are examined. Several probabilistic data processing techniques are explored in order to estimate two key localisation parameters from a single radar signal, namely along-track position and across-track position, with respect to the gate road structures. For the case of across-track position, a conventional Kalman filter approach is sufficient to achieve a reliable estimate. However for along-track position estimation, specific infrastructure elements on the gate road rib-wall must be identified by a tracking algorithm. Due to complexities associated with this data processing problem, a novel visual analytics approach was explored in a 3D interactive display to facilitate identification of significant features for use in a classifier algorithm. Based on the classifier output, identified elements are used as location waypoints to provide a robust and accurate mining equipment localisation estimate.