APSC 150
Engineering Case Studies
Case Study 3: Sustainable Mining
Part III: Automation in Mining
Lecture 3.8
Autonomous Haulage Trucks
John A. Meech
Professor and Director of CERM3
The Centre for Environmental Research in
Minerals, Metals, and Materials
The University of British Columbia
Email: jameech@dccnet.com
How does Automation relate to Sustainability?
 Automation
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Removes workers from positions of danger
Improves the operating efficiency of mining
Reduces carbon emissions (better fuel use)
Decreases stripping ratio
Increases recovery of the orebody
Provides consistency and eliminates error
Makes a mine more competitive
Makes the work easier and more intelligent
Is Automation Obvious?
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Mining operations are often chaotic
Complex systems are not easy to automate
Too many heuristic issues (environment)
Unions don’t take kindly to labour replacement
Industry is conservative (who has done` it?)
Failure in high risk situations is unacceptable
Who does the installation and maintenance?
What about back-up systems?
Equipment being Automated
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Underground digging and hauling (LHD)
Underground communication systems
Underground drilling
Underground surveying
Open Pit drilling
Slope stability monitoring
Truck Hauling and Dumping
Autonomous Vehicles
 DARPA Grand and Urban Challenges
 2004, 2005, and 2007
 Vehicles drove by themselves autonomously
UBC Thunderbird Robotics
 Formed in 2004 to enter the DGC
 Over 450 students have participated in
numerous mobile robotic projects
 DARPA Grand and Urban Challenges
UBC Thunderbird Robotics
 Formed in 2004 to enter the DGC
 Over 450 students have participated in
numerous mobile robotic projects
 DARPA Grand and Urban Challenges
 Robot Racing Competition
UBC Thunderbird Robotics
 Formed in 2004 to enter the DGC
 Over 450 students have participated in
numerous mobile robotic projects
 DARPA Grand and Urban Challenges
 Robot Racing Competition
 NASA Moon Regolith Excavator Competition
UBC Thunderbird Robotics
 Formed in 2004 to enter the DGC
 Over 450 students have participated in
numerous mobile robotic projects
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DARPA Grand and Urban Challenges
Robot Racing Competition
NASA Moon Regolith Excavator Competition
Thunderbots RoboCup Soccer
UBC Thunderbird Robotics
At the Vancouver Auto Show
 Formed in 2004 to enter the DGC
 Over 450 students have participated in
numerous mobile robotic projects
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DARPA Grand and Urban Challenges
Robot Racing Competition
NASA Moon Regolith Excavator Competition
Thunderbots RoboCup Soccer
E-Beetle Electric Car
http://www.ubcecc.com/blog/
Snowstorm
CAT797F
Mining Truck
Accidents
Komatsu’s AHS
Open Pit Mining Costs ($/t)
Operating Cost Breakdown for
CAT793
Operating Cost Breakdown for
CAT789
Evolution of Mining Truck
Capacities
Key Components
System Requirements
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Communication network
Sensors for navigation and object- avoidance
GPS system accurate to 10cm (D-GPS)
Computer hardware on-board
Central processing system
Controller devices
Supervisory Software
GPS-based navigation
Autonomous Control Cabinet
Hydraulic controls sealed on left side,
electronic controls and truck interface
on the right side for ease of access.
Group includes PLM III.
Autonomous Status Lights
Mounted on all sides of the truck to
safely display truck operating status
Road Edge Guidance (REG)
A mounted laser guidance system
measures the distance to the road berm
to provide additional navigation accuracy
PLM III is a payload monitoring system
that gives the operator accurate weight
of payload, gross vehicle weight, cycle
times and empty vehicle weight.
Wheel Speed Sensors
Wheel speed sensors
and laser-ring gyro are
combined to produce
accurate navigation
control (IMU).
Steering Angle Sensors
The steering angle of the
wheels is measured at
the control arm
Autonomous Status Lights
Mounted on all sides of the truck to
safely display truck operating status
GPS
GPS technology is
combined with Modular
Mining’s Masterlink
system to accurately
track location of vehicles
Masterlink
Modular Mining’s
Masterlink system
monitors every vehicle in
the system.
Obstacle Detection System
System focuses only on the route.
Perceivable target: a human 100 m distance
Millimeter wave radar
Komatsu 930E-AT
First two units were field tested by
Komatsu at the Twin Butte mine near
Tucson. Tests show no major problems
with radar and GPS navigation. The
system is also examining other
equipment - bulldozer, front-end loader
and several smaller ancillary vehicles.
ASI’s Obstacle Avoidance System
Radomiro Tomic Mine in Chile
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Tonnage Hauled 2006 = 8,222,000t - 32,000 tpd for 256 days
Mechanical Availability = 90.5% AHS vs. 80.2% total fleet
Effective Utilization = 84.2%
Daily Haulage Time = 24 x 0.905 x 0.842 = 18.2 hrs
- percentage gain = 25.4%
- potential to 20.5 hours
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Accidents = none (2 in 2007)
Cost per tonne = U.S.$1.36/t >>>> U.S.$0.58/t
Maintenance Reduction = 7%
Depreciation Decrease = 3%
Impact on Mine Design - increased slope angle
- decreased road width
 Significant increase in safety
Software / Sensor Features
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Localization
Navigation
Obstacle Recognition
Obstacle Avoidance
Lane Following
(where am I?)
(where do I want to go?)
(what is in the way?)
(how do I avoid it?)
(what is best route?)
Shovel-Truck Modeling
 ExtendSim software to model discrete
event processes
 Probability of failures (maintenance)
 Model based on First-Principles
 Rimpull and speed
 Fuel consumption
 Fuzzy model of road conditions
 Tire wear based on empirical data
 Manual versus Autonomous operation
Truck Operation
The basic truck cycle considers ore/waste being
loaded at a shovel and delivered to a surface
stockpile/Crusher.
31
Http://ssabh188.blogspot.com
Pit Layout
Dump
link5
extension
extension
link6
Waste
link1
link7
link2
Maintenance
Parking
Ore Shovel
extension
link4
Ore
Waste Shovel
link3
Crusher
Http://ssabh188.blogspot.com
Traffic Management
 Speed limits
 2 way traffic
 No passing
 Minimum separation between vehicles(50m)
Http://ssabh188.blogspot.com
Batching of Resources – trucks &
drivers
Digging and Loading Module
 ExtendSim Blocks for Discrete-Event Modeling
Maintenance and Delay
Module
Driver Attributes
Work Period = 14 days
Breaks
Shift Change
Lunch
Bathroom
Total
Attribute
Shift Duration = 12 hours
Shift Time (hr) Duration (hr)
0/12
0.25
6
0.75
3 and 9
0.17
1.34 (11%)
Efficiency
short term long term
learner
experienced
tired (shift start)
tired (shift end)
tired (start work period)
tired (end of work period)
time since trained (short)
time since trained (long)
personality (aggressive)
personality (conservative)
80
85
85
80
85
80
85
75
80
85
90
95
90
85
90
85
95
80
90
95
In addition, variance will be
higher for negative attributes.
Components
 Two Shovel
 Eight Trucks
 One dump area
 One Crusher
 Auxiliary equipment such as graders, dozers,
water trucks, fuel trucks, drills and light vehicles
 Breakdown and maintenance events
Http://ssabh188.blogspot.com
Manual & AHT Model Output
The model outputs Benchmarking KPIs*:
 Productivity
 Safety
 Breakdowns
 Cycle times
 Maintenance costs
 Labour costs
 Fuel Consumption
 Tire Wear
 Truck Costs ( annualized)
 Reduced GHGs
* KPI = Key Performance Index
Rimpull vs. Speed
in the Research
Key Sub-Questions
Performance Indicators
- Targets
AHS
AHS
AHS
AHS
AHS
+ 30%
+8%
-7%
Manual
Manual
Investment cost Truck haulage
per truck
speeds
-10%
-12%
Manual
Manual
Manual
Fuel
consumption
Mechanical
Availability
Tire Wear
in the Research
Key Sub-Questions
Performance Indicators
- Targets
AHS
AHS
AHS
AHS
12%
+ 5%
-15%
-14%
Manual
Increased
Productivity
-80%
Manual
Manual
Maintenance
costs
Increased
Truck life
Manual
Labour
costs
Labour savings depend on current mine circumstances – union and turnover issues
Potential Labour Savings
 Current Situation:
 Drivers per truck = 4 (2 on / 2 off)
 Total = 55 trucks x 4 = 220 plus vacation subs (20)
 Drivers retrained every 6 months
 Annual turnover = 40%
 Annual Labour cost = $36,000,000 + O/H
 Annual training costs = $10,000,000 + O/H
 Future Situation:
 No drivers and no training
 Increased maintenance personnel (3 trucks/person)
 Annual costs = 48 x $150,000 = $7,200,000 + O/H
Potential Labour Savings
 Care must be taken when introducing
automation into a union operation
 Labour replacement must be done by
attrition, not by lay-offs
 Sabotage will result otherwise
 Time to implement will increase
 System may fail ultimately
Mine Design Issues
 Steepen pit slopes to reduce stripping ratio
 Reduce haulage road width
 Select smaller trucks to increase flexibility
Haul Road Width
 One-way straights and corners: 2.5 – 3 widths
 Two-way traffic: In straights, 3 – 3.5 truck widths
 Two-way traffic: In corners, 3.5 – 4 truck widths
One-way (straights/corners)
Two-way (In straights)
Two-way (In corners)
Komatsu’s Autonomous Haulage
System
Autonomous Solutions
Applications
ASI’s Remote Dozer Control
Fuzzy Control of an Autonomous Vehicle
Snowbots – Robot Racing 2009
Tele-robotic Drilling Underground