Autonomous Haulage Trucks

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Autonomous Haulage Trucks
APSC 150 Engineering Case Studies Case Study 3
Sustainable Mining Part III Automation in
Mining Lecture 3.8

• 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_at_dccnet.com

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How does Automation relate to Sustainability?

• Automation
• 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

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Is Automation Obvious?

• Mining operations are often chaotic
• Complex systems are not easy to automate
• Too many heuristic issues (environment)
• Unions dont 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?

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Equipment being Automated

• Underground digging and hauling (LHD)
• Underground communication systems
• Underground drilling
• Underground surveying
• Open Pit drilling
• Slope stability monitoring
• Truck Hauling and Dumping

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Autonomous Vehicles

• DARPA Grand and Urban Challenges
• 2004, 2005, and 2007
• Vehicles drove by themselves autonomously

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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

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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

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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

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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
• Thunderbots RoboCup Soccer

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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
• 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/
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Snowstorm
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CAT797F
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Mining Truck Accidents
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Komatsus AHS
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Open Pit Mining Costs (/t)
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Operating Cost Breakdown for CAT793
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Operating Cost Breakdown for CAT789
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Evolution of Mining Truck Capacities
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Key Components
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System Requirements

• 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

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GPS-based navigation
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PLM III is a payload monitoring system that gives
the operator accurate weight of payload, gross
vehicle weight, cycle times and empty vehicle
weight.
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Obstacle Detection System
Millimeter wave radar
System focuses only on the route. Perceivable
target a human 100 m distance
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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.
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ASIs Obstacle Avoidance System
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Radomiro Tomic Mine in Chile

• 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
• Accidents none (2 in 2007)
• Cost per tonne U.S.1.36/t gtgtgtgt 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

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Software / Sensor Features

• 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?)
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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

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Truck Operation

• The basic truck cycle considers ore/waste being
  loaded at a shovel and delivered to a surface
  stockpile/Crusher.

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Http//ssabh188.blogspot.com
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Pit Layout
Dump
link5
extension
extension
link6
Waste
Ore Shovel
link1
link7
extension
link2
link4
Maintenance
Waste Shovel
Ore
Parking
link3
Crusher
Http//ssabh188.blogspot.com
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Traffic Management

• Speed limits
• 2 way traffic
• No passing
• Minimum separation between vehicles(50m)

Http//ssabh188.blogspot.com
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Batching of Resources trucks drivers
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Digging and Loading Module

• ExtendSim Blocks for Discrete-Event Modeling

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Maintenance and Delay Module
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Driver Attributes

• Work Period 14 days Shift
  Duration 12 hours
• Breaks Shift
  Time (hr) Duration (hr)
• Shift Change
  0/12 0.25
• Lunch
  6 0.75
• Bathroom 3 and
  9 0.17
• Total
  - 1.34 (11)
• Attribute
  Efficiency
• 
  short term long term
• learner 80 90
• experienced 85 95
• tired (shift start) 85 90
• tired (shift end) 80 85
• tired (start work period) 85
  90
• tired (end of work period) 80
  85
• time since trained (short) 85
  95
• time since trained (long) 75
  80
• personality (aggressive) 80 90
• personality (conservative) 85
  95

In addition, variance will be higher for
negative attributes.
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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
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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
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Rimpull vs. Speed
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Sub-Questions in the Research
Key Performance Indicators - Targets
AHS
AHS
AHS
AHS
AHS
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8
-7
-10
-12
Manual
Manual
Manual
Manual
Manual
Investment cost per truck
Fuel consumption
Truck haulage speeds
Mechanical Availability
Tire Wear
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Sub-Questions in the Research
Key Performance Indicators - Targets
AHS
AHS
AHS
AHS
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5
-15
-14
-80
Manual
Manual
Manual
Manual
Increased Productivity
Increased Truck life
Maintenance costs
Labour costs
Labour savings depend on current mine
circumstances union and turnover issues
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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

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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

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Mine Design Issues

• Steepen pit slopes to reduce stripping ratio
• Reduce haulage road width
• Select smaller trucks to increase flexibility

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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

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Komatsus Autonomous Haulage System
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Autonomous Solutions Applications
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ASIs Remote Dozer Control
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Fuzzy Control of an Autonomous Vehicle
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Snowbots Robot Racing 2009
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Tele-robotic Drilling Underground