Rate My Professor Ross McAree

Professor Ross McAree is Head of the School of Mechanical and Mining Engineering at the University of Queensland. His research interests center on machinery dynamics and control, with a particular emphasis on mining equipment automation. He led the development and demonstration of the world’s first fully autonomous mining excavator through a 10-year collaboration with Joy Global Surface Mining (now part of Komatsu) and collaborated with Caterpillar Inc. to develop and trial the world’s first autonomous bulldozer capable of production dozing using the ‘pivot push’ method. From 2007 to 2015, he held an industry-funded Chair in Mechanical Engineering and served as Vice President for Automation and Automation Program Leader with the Cooperative Research Centre for Mining from 2009 to 2015. Professor McAree established the Mechatronic Engineering Program at UQ in 2002 and has provided leadership within his school as Chair of the Research Committee (2009-2015) and Teaching and Learning Committee (2015-2017).

Professor McAree has attracted over $12 million in Category 1-3 funding and $8.5 million in Category 4 funding, contributing to successful bids for CRCMining and CRCMining2 totaling $35.2 million. His significant external service includes Chairing the Australian Academy of Science National Committee on Mechanical Sciences (2001-2006), serving as the Australian representative to the International Federation for the Theory of Machines and Mechanisms (2001-present), acting as Associate Editor for the journal Mechatronics (1999-2011), and membership in Australian Standards Committees ME-27 and IT-06P. He is a Fellow of the Australian Academy of Technology and Engineering (ATSE) and received the UQ Partners in Research Excellence Award in 2018. His research group has developed several automation technologies commercialized in the mining sector, estimated to benefit the global mining industry by over $1 billion. Key publications include “Minimal configuration point cloud odometry and mapping” (2024, The International Journal of Robotics Research), “Real-time 6-DOF pose estimation of known geometries in point cloud data” (2023, Sensors), and “Can hyperspectral imaging and neural network classification be used for ore grade discrimination at the point of excavation?” (2022, Sensors).