Technology developed to track automated underground machines

15 December 2017 (Last Updated December 15th, 2017 11:47)

Robotics researchers from Queensland University of Technology (QUT) in Australia have developed a technology to track automated mining vehicles operating underground.

Robotics researchers from Queensland University of Technology (QUT) in Australia have developed a technology to track automated mining vehicles operating underground.

The technology can be fitted onto the vehicles to help them navigate autonomously through the tunnels even in conditions such as dust, camera blur, and bad lighting.

It uses a combination of mathematics, biologically inspired algorithms and vehicle-mounted cameras to track the location of the vehicle in underground tunnels within a matter of metres.

Led by a team from the Australian Centre for Robotic Vision at QUT, the research has been carried out in collaboration with Catepillar, Mining3 and the Queensland Government.

Research has taken into consideration expensive sensing or infrastructure modifications installed on autonomous vehicles operating in underground mines.

QUT professor Michael Milford said: “It’s commercially important to be able to track the location of all your mobile assets in an underground mine, especially if you can do it cheaply without needing to install extra infrastructure or use expensive laser sensing.

“It’s commercially important to be able to track the location of all your mobile assets in an underground mine.”

“We have developed a positioning system that uses cameras rather than lasers, based on more than a decade of research in biologically inspired navigation technology.”

According to QUT, the GPS and wireless sensor networks are not suitable for tough terrain due to interference from the rock mass and the lack of access points.

During testing, the research team is said to have encountered challenging conditions at mine sites.

Milford further added: “We developed a system, which could intelligently evaluate the usefulness of the images coming in from the camera, and disregard ones that were blurry, dusty, or that were washed out from incoming vehicle lights.”

Researchers have already conducted two field trips to Australian mine sites and will start testing the second stage of the project later this month, which includes a more precise positioning technology.