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.
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
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
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
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
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.
Partners and Collaborators