Mark T. Dunn

Iterative Smoother-Based Variance Estimation

The minimum-variance smoother solution for input estimation is described and it is shown that the resulting estimates are unbiased. The smoothed input and state estimates are used to iteratively identify unknown process noise variances. The use of smoothed estimates, as opposed to filtered estimates, leads to improved approximate Cramér-Rao lower bounds for the unknown parameters. It is also shown that the sequence of iterates are monotonic and asymptotically approach the actual values under prescribed conditions. A nonlinear mining navigation application is described in which unknown parameters are estimated.

Human-Computer Interaction Experiments Immersive Virtual Reality Applications for the Mining Industry

The present work concerns immersive virtual reality (IVR) experiments carried out for prospective applications in the mining industry. Demonstrated technologies are also suitable for use in generic training and e-learning fields. Visualisation is handled by the Unity3D 2.5 multiplatform game development tool, which communicates over .Net socket servers with data feeds from a 5DT Data Glove Ultra that measures finger flexures, and an iPhone based touchpad device. The iPhone also provides orientation data pertaining to acceleration and rotational attributes, such as, pitch, roll and yaw. The user is placed at the focal point of a 4-m dome and experiences the immersive virtual reality environment display. Navigation and object manipulation by the user is made possible through the combination of hardwired buttons, in-world selection techniques and gesture recognition. The techniques are directly applicable to the monitoring of mining environments, in which mining equipment is surrounded by various sensor networks.

Control of Automated Mining Machinery Using Aided Inertial Navigation

This paper describes new research and development currently being undertaken to advance the automation capability of mining equipment in underground coal mining operations. The research effort targets the development of new inertial navigation solutions that aim to increase roadway development rates and provide a safer working environment for underground mine personnel. Specific developments have been undertaken to develop and integrate navigation, remote control, and visualisation technologies. Results obtained so far show that the navigation solution developed can provide the accuracy necessary for production-level underground mining.

Longwall Shearer Automation: From Research to Reality

This paper describes the development of an automation system for longwall mining machinery which delivers centimeter-order position accuracy with no drift. This system has been developed using high performance inertial navigation technologies coupled with novel aiding strategies to eliminate integration drift.

High accuracy inertial navigation for underground mining machinery

The underground mining industry is constantly driven by the need to improve mining productivity, increase personnel safety, and secure environmental sustainability. Mining automation technology has shown significant potential to meet these needs by providing more accurate mining methods, incorporating sensing to optimally control equipment and increasing personnel safety through remote process operation. This paper details research being undertaken by CSIRO Earth Science and Resource Engineering (CESRE) Mining Technology group to advance the automation capability of underground coal mining equipment by developing new inertial navigation solutions. The research specifically targets a navigation solution for roadway development and the results obtained so far demonstrate that the system can provide the navigation accuracy required for the production environment.