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Springer Handbook of Robotics pp 1549–1576Cite as

Robotics in Mining

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Abstract

This chapter presents an overview of the state of the art in mining robotics, from surface to underground applications, and beyond. Mining is the practice of extracting resources for utilitarian purposes. Today, the international business of mining is a heavily mechanized industry that exploits the use of large diesel and electric equipment. These machines must operate in harsh, dynamic, and uncertain environments such as, for example, in the high arctic, in extreme desert climates, and in deep underground tunnel networks where it can be very hot and humid. Applications of robotics in mining are broad and include robotic dozing, excavation, and haulage, robotic mapping and surveying, as well as robotic drilling and explosives handling. This chapter describes how many of these applications involve unique technical challenges for field roboticists. However, there are compelling reasons to advance the discipline of mining robotics, which include not only a desire on the part of miners to improve productivity, safety, and lower costs, but also out of a need to meet product demands by accessing orebodies situated in increasingly challenging conditions.

Keywords

  • Situational Awareness
  • Underground Mining
  • Inertial Navigation System
  • Blast Hole
  • Longwall Mining

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Fig. 59.1
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Abbreviations

2-D:

two-dimensional

3-D-NDT:

three-dimensional normal distributions transform

3-D:

three-dimensional

4WD:

four wheel drive

ACARP:

Australian Coal Association Research Program

AFC:

armoured (or articulated) face conveyor

CAT:

collision avoidance technology

CMTE:

Cooperative Research Centre for Mining Technology and Equipment

CMU:

Carnegie Mellon University

CSIRO:

Commonwealth Scientific and Industrial Research Organisation

DGPS:

differential global positioning system

DOF:

degree of freedom

EHC:

enhanced horizon control

GLS:

global navigation satellite system

GPS:

global positioning system

ICP:

iterative closest point

IMU:

inertial measurement unit

INS:

inertial navigation system

IREDES:

International Rock Excavation Data Exchange Standard

ISO:

International Organization for Standardization

JPS:

jigsaw positioning system

LASC:

Longwall Automation Steering Committee

LHD:

load haul-dump

LIDAR:

light detection and ranging

MEMS:

microelectromechanical system

NASA:

National Aeronautics and Space Agency

NIOSH:

United States National Institute for Occupational Safety and Health

PAT:

proximity awareness technology

PDT:

proximity detection technology

PTU:

pan–tilt unit

PUMA:

programmable universal machine for assembly

RCS:

rig control system

RECS:

robotic explosive charging system

RFID:

radio frequency identification

ROC:

remote operations centre

ROM:

run-of-mine

ROV:

remotely operated underwater vehicle

RTK:

real-time kinematics

SLAM:

simultaneous localization and mapping

SPMS:

shearer position measurement system

TTC:

time-to-collision

UKF:

unscented Kalman filter

V2V:

vehicle-to-vehicle

WAAS:

wide-area augmentation system

WASP:

wireless ad-hoc system for positioning

XML:

extensible markup language

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Author information

Authors and Affiliations

  1. The Robert M. Buchan Department of Mining, Queen’s University, 25 Union Street, ON K7L 3N6, Kingston, Canada

    Joshua A. Marshall

  2. Department of Autonomous Systems, CSIRO, 1 Technology Court, QLD 4069, Pullenvale, Australia

    Adrian Bonchis

  3. Department of Aerospace, Mechanical and Mechatronic Engineering, University of Sydney, NSW 2006, Sydney, Australia

    Eduardo Nebot

  4. Rio Tinto Centre for Mine Automation, University of Sydney, NSW 2006, Sydney, Australia

    Steven Scheding

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  1. Joshua A. Marshall
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  2. Adrian Bonchis
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  3. Eduardo Nebot
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  4. Steven Scheding
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Corresponding author

Correspondence to Joshua A. Marshall .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Naples Federico II, Via Claudio 21, 80125, Naples, Italy

    Bruno Siciliano

  2. Department of Computer Sciences, Artificial Intelligence Laboratory, Stanford University, 450 Serra Mall, CA 94305, Stanford, USA

    Oussama Khatib

Video-References

Video-References

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Autonomous tramming available from http://handbookofrobotics.org/view-chapter/59/videodetails/142

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Autonomous haulage system available from http://handbookofrobotics.org/view-chapter/59/videodetails/145

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Autonomous loading of fragmented rock available from http://handbookofrobotics.org/view-chapter/59/videodetails/718

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Marshall, J.A., Bonchis, A., Nebot, E., Scheding, S. (2016). Robotics in Mining. In: Siciliano, B., Khatib, O. (eds) Springer Handbook of Robotics. Springer Handbooks. Springer, Cham. https://doi.org/10.1007/978-3-319-32552-1_59

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