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Queensland's to fund research program for automated underground mining equipment
September 20, 2016

The Queensland government has committed more than $400,000 to fund a project to develop technologies to automate underground mining machines.

The research project that aims to increase productivity, efficiency and safety in the underground mining sector will be carried out at the Australian Centre for Robotic Vision at the Queensland University of Technology (QUT). Dr Thierry Peynot will be involved in the project, ABC Australia reported.

While driverless mine trucks are not a rarity, particularly in Western Australian mines, they operate above ground with GPS.

Peynot said one of the big issues researchers faced in developing driverless underground mine vehicles was the harsh environment, while also navigating the machinery through a maze of dark tunnels.

He said the team would focus on developing camera-based positioning systems for locating and tracking underground mining vehicles, as well as multi-sensor systems for accurate positioning.

“The vision technology is going to be the core of that project [and] it is looking at recognising places that it [the vehicle] has been before,” Peynot said.

“Usually we have complex maps of the mines available, and it is about knowing where that vehicle is in that map.

“What we can do is navigate one vehicle with one camera around the mine first, once, and then we can recognize where we have been once we come back to the same place.

“There will be some changes in the mine obviously, but we can accommodate for that by recognizing all the things that did not change from the first passage.”

Those steps will eventually lead to driverless vehicles working underground.

In addition to the improved productivity from automated machines, Peynot believes his research will also lead to safer mining environments.

“If you can localize precisely the vehicles in the mine, then you can improve safety, and once they are automated, you can remove people from the dangerous areas and have them monitor from the outside,” Peynot said.

“In terms of productivity, if you can localize those vehicles precisely, then you can do two things — you can analyze the process better, and how to best use them from what they are doing at the moment.

“Later down the track you can really optimize the spaces between the vehicles and when one vehicle should do what.”

The project is expected to run for two years in partnership with international mining equipment manufacturer Caterpillar.

 

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