Field and service applications - Dragline automation- A dedade of development - Shared Autonomy for Improving Mining Equipment Productivity

Draglines are massive machines commonly used in surface mining to strip overburden, revealing the targeted minerals for extraction. Automating some or all of the phases of operation of these machines offers the potential for significant productivity and maintenance benefits. The mining industry has a history of slow uptake of automation systems due to the challenges contained in the harsh, complex, three-dimensional (3D), dynamically changing mine operating environment. Robotics as a discipline is finally starting to gain acceptance as a technology with the potential to assist mining operations. This article examines the evolution of robotic technologies applied to draglines in the form of machine embedded intelligent systems. Results from this work include a production trial in which 250,000 tons of material was moved autonomously, experiments demonstrating steps towards full autonomy, and teleexcavation experiments in which a dragline in Australia was tasked by an operator in the United States.

[1]  Sanjiv Singh,et al.  The State of the Art in Automation of Earthmoving , 1997 .

[2]  Peter I. Corke,et al.  Location of a dragline bucket in space using machine vision techniques , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[3]  Graham M. Brooker,et al.  Application of Millimetre Wave Radar Sensor to Environment Mapping in Surface Mining , 2006, 2006 9th International Conference on Control, Automation, Robotics and Vision.

[4]  Peter I. Corke,et al.  Dragline Automation: Experimental Evaluation through Productivity Trial , 2003, FSR.

[5]  Thomas B. Sheridan,et al.  Telerobotics, Automation, and Human Supervisory Control , 2003 .

[6]  Peter I. Corke,et al.  Dragline swing automation , 1997, Proceedings of International Conference on Robotics and Automation.

[7]  Peter Corke,et al.  Visual Control of Robots: High-Performance Visual Servoing , 1996 .

[8]  Leonhard E. Bernold,et al.  Principles of control for robotic excavation , 1992 .

[9]  Peter I. Corke,et al.  Robust sensing for a 3500 tonne field robot , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[10]  A. Hemami,et al.  Fundamental Analysis of Automatic Excavation , 1995 .

[11]  Sanjiv Singh.  The State of the Art in Automation of Earthmoving , 1997 .

[12]  Peter I. Corke,et al.  Three-Dimensional Imaging for a Very Large Excavator , 2003, Int. J. Robotics Res..

[13]  Peter I. Corke,et al.  Modeling and Control of a 3500 Tonne Mining Robot , 1997, ISER.

[14]  Peter I. Corke,et al.  Dragline modelling and control , 1997, Proceedings of International Conference on Robotics and Automation.

[15]  Hugh F. Durrant-Whyte,et al.  Robotic excavation in construction automation , 2002, IEEE Robotics Autom. Mag..

[16]  Paul Milgram,et al.  Telerobotic control using augmented reality , 1995, Proceedings 4th IEEE International Workshop on Robot and Human Communication.

[17]  Derek W. Seward,et al.  The Development, Control and Operation of an Autonomous Robotic Excavator , 1998, J. Intell. Robotic Syst..

[18]  Sanjiv Singh,et al.  A Robotic Excavator for Autonomous Truck Loading , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[19]  Peter I. Corke,et al.  Off-world Robotic Excavation for Large-scale Habitat Construction and Resource Extraction , 2006, AAAI Spring Symposium: To Boldly Go Where No Human-Robot Team Has Gone Before.