Mining robotics

Robotics and automation have a significant role to play in improving the productivity and safety of mining operations, above and below ground. Automation in mining refers to quite a range of topics, from making the haul trucks move across fields by themselves, to creating algorithms that ensure drilling and sorting material is able to be streamlined. The processes involved in creating these solutions involve image segmentation and recognition, machine learning, gps data aggregation and a large amount of control systems.

QUT’s roboticists are forerunners in research will have a major impact on the global mining industry, dramatically improving existing mining operations and facilitating automation in underground environments.

We welcome industry collaborations in this field. For more information about collaborating with our team, please contact us.

Projects

Automation-enabling positioning for underground mining

Advance Queensland Innovation Partnership, Caterpillar, Mining3

The project offers potential solutions to the challenge of accurately estimating the position of vehicles in underground mining environments.

Semantic Mapping for Robotic Maintenance

01/12/2018 - 30/09/2019

Mining3

This project investigates the use of semantic mapping methods for the purpose of robotic maintenance in mining

Robotic Manipulation for Automated Maintenance

01/01/2019 - 30/09/2019

Mining3

The project evaluates the feasibility of current state-of-the-art robotic manipulation solutions to be applied to the task of automated vehicle maintenance

A baseline dataset for performance evaluation of visual detection and classification techniques in mining environments

01/03/2018 - Ongoing

Mining3

This project aims at building a reference dataset to evaluate the performance of state-of-the-art visual-based object detection and classification methods in mining contexts

Effect of lighting on visual odometry performance in underground mines

02/01/2017 - Ongoing

Mining3

This project evaluates the effect of illumination on the performance of Visual Odometry (VO) in underground mining environments to identify suitable illumination configurations that should be used to obtain the best performance of VO in these environments.

Team

Distinguished Professor Peter Corke
Distinguished Professor Peter Corke
Director, QUT Centre for Robotics
Professor Michael Milford
Professor Michael Milford
Deputy Director, QUT Centre for Robotics
Associate Professor Thierry Peynot
Associate Professor Thierry Peynot
Associate Professor & Mining3 Chair in Robotics
Dr Juxi Leitner
Dr Juxi Leitner
Research Fellow
 Andrew Keir
Andrew Keir
Director, QUT Research Engineering Facility
 Dmitry Bratanov
Dmitry Bratanov
QUT Research Engineering Facility
Dr Rune Rasmussen
Dr Rune Rasmussen
QUT Research Engineering Facility
 Guenes Minareci
Guenes Minareci
Research Fellow
 Ian Greyvensteyn
Ian Greyvensteyn
Postgraduate Student
 Faris Azhari
Faris Azhari
HDR student
Dr Adam Jacobson
Dr Adam Jacobson
Research Fellow [Alumni now at Caterpillar as an Automation Engineering Specialist]

Partners and Collaborators