A recent innovation in underground mining operations is the use of automated Load-Haul- Dump (LHD) vehicles. These vehicles are able to transport ore from a stope to an ore-pass and return without the direct involvement of a human operator. An interesting, and unexpected, problem arising from the introduction of these vehicles is that the haul road on which they operate can become heavily degraded due to a lack of feedback on road condition. This results in increased maintenance and downtime. On non-automated LHD vehicles, the problem is prevented by the on-board operator who senses road condition by ride quality and reports back to his or her supervisor when the road becomes unacceptably degraded.
This thesis develops a simple tool, called the Road Surface Quality (RSQ) monitor, which aims to report road condition to the automated vehicle's supervisory control system. The monitor is intended to provide feedback on road condition analogous to that provided by an on-board operator. This feedback can in turn be used to guide decisions such as the operating speed over different sections of road and when to perform road maintenance such as re-grading.
The methodology used to determine road condition involves the capture of the vertical acceleration of the vehicle chassis and derivation of a measure of the "bounce energy" of the vehicle as it traverses the road. This serves as a proxy for the "quality of ride" This approach exploits the fact that automated LHD vehicles make repeatable passes from run to run. In particular, wheel tracks and speed are consistent during each transit from stope to ore-pass and back again. This makes possible the direct comparison of signals measured from run to run.
The main conclusion of the thesis is that it is possible to differentiate between roads of different condition using measurements of vertical acceleration on automated LHDs. This conclusion is supported by several case studies based on installation of the monitor on the Dynamic Automation Systems (DAS) Autotram LHD in operation at Olympic Dam Mine. Data collected from the monitor shows the system is also capable of detecting abnormally rough operation of the vehicle, e.g. unnecessarily heavy collisions of the bucket with the environment. A detailed plan for further testing of the monitor is also proposed.