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Abstract

Mining machinery in loading and digging tasks requires well-trained operators. The nature of the loading task is repetitive, yet tedious due to changing digging and loading points, material weight, grade and digging resistance. There exists an optimum digging and loading kinematics, i.e. the family of motion trajectories for each task in which the control of the bucket tool is the easiest to execute. A given mining machine may not match this most convenient kinematics for a given job and working conditions. To help, a new, software-controlled virtual kinematics is developed to assist the operator and ease the task as well as increase energy efficiency and save cost. The paper describes this new technique and its implementation on an experimental excavator, as well as laboratory and benchmarking results. The potential benefits of the new hybrid machine control are discussed regarding reduced loading cycle time, fuel savings and ease of operation.

Keywords

Robotic excavation Mining machinery Kinematics

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References

Alami

, Chatila

, Fleury

, Ghallab

, Ingrand

1998. An architecture for autonomy, Int. J. Robot. Res., 17, (4), 315–337.

Ariel Dynamics Inc. 2004. Ariel performance analysis system (APAS), http://www.arielnet.com/start/apas/default.html.

Carr

2008. Application of computer-assisted control architecture of the operation of large surface mining shovels and excavators, Master's dissertation, Mackay School of Earth Science and Engineering, University of Nevada, Reno, NV, USA, 1–84.

Chiang

, Huang

. 2004. Experimental implementation of complex path tracking control for large robotic hydraulic excavator, Int. J. Adv. Manuf. Technol., 23, 126–132.

Craig

. 1989. Introduction to robotics: mechanics and control, 19–60, Reading, MA, Addison-Wesley.

Danko

2001. Operator control architecture, Proc. 4th Regional Sympos. on ‘Computer applications in the mineral industries’, Tampere, Finland, September, Helsinki University of Technology, 253–266.

Danko

2007. Integrated robot-human control in mining operations, Final Project Report for April 2004–September 2007, Award DE-FC26-04NT42087. Submitted to DOE.

Danko

, Knowles

, Tiwari

2006. Robot–human control interactions in mining operations, SME Transactions. 320, 85–93.

Elevi

, Elevi

2010. Performance measurement of mining equipments by utilizing OEE, Acta Montan. Slov., 15, 95–101.

10.

Fimpong

, Hu

, Inyang

2008. Dynamic modeling of hydraulic shovel excavators for geomaterials, Int. J Geomech. ASCE, 20–29, http://pubs.asce.org .

11.

, Taylor

, Seward

2004. Proportional-integral-plus control of an intelligent excavator, Computer-Aided Civ. Infrastruct. Eng., 19, 16–27.

12.

Haga

, Hiroshi

, Fujishima

2001. Digging control system for hydraulic excavator, Mechatronics, 11, 665–676.

13.

Hall

2002. Characterizing the operation of a large hydraulic excavator, PhD thesis, School of Engineering, University of Queensland, Brisbane, Australia, 1–126.

14.

Płonecki

, Cendrowicz

1998. A concept of digital control system to assist the operator of hydraulic excavators, Autom. Constr., 7, (5), 401–411.

15.

Sheridan

. 1992. Telerobotics, automation, and human supervisory control, Cambridge, MA, MIT Press.

16.

Stentz

, Bares

, Singh

, Rowe

1999. A robotic excavator for autonomous truck loading, Auton. Robots, 7, 175–186.

17.

van der Horst

, Mourik

2010. Automating excavators to reduce cycle times and maintain safety & accuracy, IHC Syst. Publ., 1–11.

18.

van der Horst

, Mourik

2011. Automated excavator, first experiences in Germany, Proc. WEDA XXXI Technical Conf. & TAMU 42 Dredging Semin., Nashville TN, USA, June, TAMU, 92–103.

Mining machine control in virtual working kinematics

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