Autonomous Haulage Trucks:

1. Autonomous Haulage Trucks: The Future is Now! John A. Meech University of British Columbia

2. Charles Darwin It is not the strongest species that survive, nor the most intelligent, it is the one that is most adaptable to change.

3. The Heart of Man’s Ability to Adapt • Communication

4. The Heart of Man’s Ability to Adapt • Communication • Collaboration

5. The Heart of Man’s Ability to Adapt • Communication • Collaboration • Cooperation

6. Communication FUZZY LOGIC Collaboration Cooperation The Heart of Man’s Ability to Adapt • Communication • Collaboration • Cooperation • Fuzzy Logic • Ability to think rationally • Ability to reason about the truth • Ability to change one’s mind, i.e., ADAPT

7. Communication FUZZY LOGIC Collaboration Cooperation The Heart of Man’s Ability to Adapt • Communication • Collaboration • Cooperation • Fuzzy Logic • Ability to think rationally • Ability to reason about the truth • Ability to change one’s mind, i.e., ADAPT • But there is a fourth “C” COMPETITION

8. Is Competition the Opposite of Collaboration? In a Competitive World • You are “Against” Someone • Communication becomes “Intelligence” • Collaboration means forming “Alliances” • Forming an Alliance means “Cooperation” 100 0 Degree of Belief Competition Collaboration COMPETITION

9. Competition is the Complement of Collaboration In a Competitive World • You are “Against” Someone • Communication becomes “Intelligence” • Collaboration means forming “Alliances” • Forming an Alliance means “Cooperation” FUZZY 100 0 Degree of Belief Competition Collaboration Competition Collaboration COMPETITION

10. Key Issues • The “New” Mining Engineer for the 21st Century • The Autonomous Mine • Elements of an Autonomous Haulage System • Implementation of a Successful Project • Change Management Requirements • Modeling Haulage Systems - Role of Artificial Intelligence

11. Health, Safety, and Communities FUZZY LOGIC Production and Productivity Environment (Local and Global) The “New” Mining Engineer from UBC • Since 1997, UBC’s Mission >> SUSTAINABLE MINING MINING SUSTAINABLE

12. The “New” Mining Engineer from UBC SUSTAINABLE MINING • The Company • The Environment • The Community

13. The “New” Mining Engineer from UBC SUSTAINABLE MINING • “Culture of Mining" - empirical, heuristic, and non-linear • "Old" days – no real concern for the Neighbourhood or for Safety • Main Goal – Production targets and increases • We design a mine to fail after workers have left the area • 1960s – Environment and Safety became constraints • 1990s – Socio-political issues entered the decision-making • Today all 3 interlinked issues must be balanced, i.e., all are VALUES! • Mining has been using Fuzzy Logic for 1000s of years

14. The “New” Mining Engineer from UBC • Environmental Issues • 1994: Chair in Mining and the Environment funded by industry • 2001: a third of our Graduate students are focused on Environment • Benefits: • Teach engineering students about Environmental issues in Mining • Conduct R&D to solve specific legacy site problems in industry • Other Canadian Mining schools have followed: • Queen’s University – Mining and the Environment course • Laurentian – Environmental Impact of Processing course

15. The “New” Mining Engineer from UBC • Socio-Political Issues • 1997: accepting non-Engineering students into Graduate School • 2006: about 25% of our Graduate Students are non-Engineering • Two Major Benefits: • Teach engineering students about Socio-Political issues • Provide opportunity for non-Eng. students to learn about Technology • Other Canadian Mining schools have followed: • Queen’s University – have two socio-political Faculty Members • Laurentian – International School for Sustainable Mining

16. The “New” Mining Engineer from UBC • Process Control and Automation • 1989: only for Mineral Processing • 2003: became core for all Miners • Two new components: • Artificial Intelligence (FL, ANN, GA, and ES) • Robotics (hardware and software) • Other Canadian Mining schools have followed: • Queen’s University – added to their Mine-Mechanical option • Laurentian University – core course on Automation & Reliability

17. The “New” Mining Engineer from UBC • 2011 UBC Fourth Year Class in Mining What the Miners of the 21st Century are learning to do with Process Control, Robotics, and Artificial Intelligence (i.e., Fuzzy Logic)

18. The “New” Mining Engineers from UBC • 2011 UBC Fourth Year Class in Mining http://www.jmeech.mining.ubc.ca/Mine432/video2011/Group3.wmv

19. The “New” Mining Engineers from UBC • Understands the role of Automation • Improve production and productivity • Reduce haulage costs appreciable • Enhance workplace safety • Reduce fuel use, GHG emissions, and tire wear rates • Increase life cycle of mining equipment • Stabilize and Optimize (improve)

20. DARPA Grand and Urban Challenges • 2004 and 2005 – Mojave Desert • 2007 - Victorville • Vehicles drove autonomously http://www.jmeech.mining.ubc.ca/Mine432/DPG_highlights1.wmv

21. The Autonomous Mine • Open Pit Operations • Drilling • Blasting • Digging • Hauling • Refueling • Maintenance

22. The Autonomous Mine • KPIs • Production per day per truck per month • Fuel Use per hour per km per tonne • Tire Wear per hr per km per tonne • Cycle Time increased/decreased • Cycles per day increased/decreased • %Mechanical availability increased • %Utilization increased • O&M Costs decreased

23. Mining Truck Accidents

24. Basic Requirements • Localization – where am I? • Navigation – where do I want to go? • Obstacle Avoidance – what is in my path? • Condition Monitoring – how is my health?

25. Komatsu – VHMS >>> KOMTRAX

26. Elements of an Autonomous Haulage System • IEEE 802.11 Standard Communications network (WLAN) “n”Version - 2012 (250m) • Bandwidth = 20 - 40 MHz (MIMO) • Datestream = 54 - 600 Mbit/s “ac”Version - DRAFT • Bandwidth = 20 - 160 MHz • Data stream = 88 - 867 Mbit/s “ad”Version – Under development • Bandwidth = 2.4 - 60 GHz • Data stream = 7 Gbit/s

27. Elements of an Autonomous Haulage System • IEEE 802.11 n , ac , ad WLAN • Computer hardware on-board • Central data processing system • Supervisory Software Front Runner Modular Mining Systems Modular Mining’s DISPATCH® fleet management system and MASTERLINK® communication system

28. Elements of an Autonomous Haulage System • IEEE 802.11 n , ac , ad WLAN • Computer hardware on-board • Central data processing system • Supervisory Software COMMAND for hauling CAT’s MineStar System

29. Elements of an Autonomous Haulage System • IEEE 802.11 Communication network • Sensors for • Navigation >>> GPS and Radar • Object- Avoidance • GPS accurate to 10 cm (D-GPS)

30. Elements of an Autonomous Haulage System • IEEE 802.11 Communication network • Sensors for • Navigation • Object- Avoidance >>> Radar and LIDAR • Radar range to 80 m • Front • LIDAR range to 20 m • Sides and Rear mm-wave Radar Obstacle Detection System

31. Elements of an Autonomous Haulage System • IEEE 802.11 Communication network • Sensors for • Navigation • Object- Avoidance >>> Radar and LIDAR • Radar range to 80 m • Front • LIDAR range to 20 m • Sides and Rear IBEO and SICK scanning laser instruments

32. Elements of an Autonomous Haulage System • IEEE 802.11 Communication network • Sensors for • Navigation • Object- Avoidance >>> Radar and LIDAR • Radar range to 80 m • Front • LIDAR range to 20 m • Sides and Rear CAT’s Radar and LIDAR-based Obstacle Detection System

33. Obstacle Detection - System Reliability Measure of Success << ^ ^ Goal

34. Elements of an Autonomous Haulage System Additional Sensors • Wheel Speed • Steering Angle • Road Edge Guidance Lasers • Payload Monitoring • Tire Temperatures (embedded in tread) • Status Lights

35. Implementation of a Successful Project Manual Robotic Moonshot KPI (core) Replace KPI (core) 0 % Autonomous 100

36. Implementation of a Successful Project Manual Robotic ‘Baby’ steps KPI (core) Staged KPI (core) 0 % Autonomous 100

37. Implementation of a Successful Project Manual Robotic KPI (core) Integrate KPI (core) ? 0 % Autonomous 100

38. Implementation of a Successful Project • KPIs may decrease initially until full adaptation • Which plan is best? • Replace MHS with AHS in one step – no interaction • Isolate AHS from MHS : Separate routes, staged intro • Integrate AHS with MHS: Significant safety concerns • Safety concerns require careful design and planning • Is a back-up or fall-back system necessary or desired?

39. Develop Core Competencies • Process Control fundamentals • Understanding control stability • Supervisory control hierarchies • Software algorithms • Artificial Intelligence methods • Managing large databases • Sensor knowledge and maintenance • Remote operation of equipment

40. Change Management Requirements • Mine Personnel Issues • Truck Drivers >>> Hardware/Software Maintenance • Introduce AHS with all affected personnel involved • Humans in-the-loop must be accounted for • Machine Issues • Monitoring health of sensors on regular basis • Soft-sensors to confirm operational effectiveness • Data Collection to integrate into planning/scheduling

41. Change Management Requirements • Mine Management Issues • Must be on-side with all decisions about the changes • New safety/traffic rules required (some are positive) • More maintenance / less operational activities • Drilling and Blasting practices must change • Headquarter Issues • Move to Central Control must be done with care • Initial focus on integrating massive data collections • Decisions must support local mine site personnel

42. Who is in the Game?

43. Komatsu – Codelco • Radomiro Tomic mine - 2004

44. Komatsu – Codelco • Radomiro Tomic mine - 2005

45. Komatsu – Codelco • In 2006, at RT: 5 AHS 930E trucks; 32,000 tpd; 256 days • Mechanical Availability: > 90% • Cost per tonne reduced: $1.36 to $0.50 • Est. maintenance reduction: 7 % • Est. depreciation reduction: 3 % • Gaby mine AHS trucks: 2008 – 11 2010 – 12 2012 – 18 • Safety (accidents): 0 in 2006-07 / 2 in 2007

46. Komatsu – Codelco • AHS trucks operate in an “electronic bubble” • Each truck is aware of all other machines on site • Unknown machine in AHS area causes shutdown • Navigation is a hybrid of • High-precision GPS, and • Dead-reckoning IMU (accelerometer/gyroscope).

47. Komatsu – Codelco • Change how mine operations are planned & implemented • Must consider all vehicles, not only AHS trucks • Complexity increases exponentially with number of trucks “There are hardware restrictions...Information exchanged between trucks and central control is enormous. At Gaby, 11 trucks and 30 pieces of equipment...limit...information transfer.” – Jeffery Dawes, Komatsu Chile

48. Komatsu – Rio Tinto • Rio’s “Mine of the Future” concept • Began in 2008 at West Angelas Mine, Australia • First 24 months • 42 MT • 145,000 cycles (290 t) • Short haul distance ~1.5 km • 5 trucks – 25 min. cycles • Ave. Velocities (initial trial): • Loaded = 7-10 kph • Empty = 14-18 kph

49. Komatsu – Rio Tinto • AHS demonstrates value to Rio with respect to: • Health and Safety • Productivity • 5 Komatsu 930E trucks moved to Yandigoogina mine with 5 new AHS trucks to operate JSE open pit under control of mine management with support from AHS team in Perth • System fully deployed this year - 2012

50. Komatsu – Rio Tinto MOU: November 2011 • Agreement to deploy 150 AHS trucks by 2015 • Next step in large-scale implementation of Komatsu’s Front Runner AHS • First stage of 10 completed this year – 2012