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Trends for Autonomous Systems

Trends for Autonomous Systems. Professor Anthony Finn Defence & Systems Institute University of South Australia. Me working on this presentation. Technology Map. Numbers of Maritime AUV. 1,000. Westwood, 2012. Sales of AUVs. 1,000. OECD Labour Costs vs. Price of Robots. In 2009 –

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Trends for Autonomous Systems

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  1. Trends for Autonomous Systems Professor Anthony Finn Defence & Systems Institute University of South Australia

  2. Me working on this presentation

  3. Technology Map

  4. Numbers of Maritime AUV 1,000 Westwood, 2012

  5. Sales of AUVs 1,000

  6. OECD Labour Costs vs. Price of Robots

  7. In 2009 – 630 AUVs By 2020 – 1400 AUVs $2.3B over 2010-2019 40% 31% 13% 7% Underwater systems are among most costly robots median price US$850,000 Rest9% 1% 60% military

  8. Investment Profile for Robotics US DOD 2011 Projected No. UAVs

  9. Increases World 38% China 39% America 43% Germany 51% Brazil 125% Europe 43% Japan 27% Korea 9% GFC Robot Density Automotive 2006 - 36 per 10,000 employees 2011 - 141 per 10,000 employees Other sectors 10 robots per 10,000 employees

  10. Trend Analysis? There are now almost the same number of robots on earth as Australians World Domination (29 May 2031)

  11. Some General Trends for AS From single vehicle to cooperatives of vehicles – Homogeneous & heterogeneous packages From segregated use to integrated operations – Integrated operations air/maritime environment * From automated to autonomous – Dynamic & complex environments – Reacting to the unpredicted (trusted autonomy) * From single purpose to general/multi-purpose –Novel applications (atmospheric tomography) *

  12. Energy Delivery & Storage Trends Storage Constrained by fundamental chemistry, safety issues & economics (driver incl. consumer electronics) Delivery Dominated more by design than chemistry • Harvesting storage requirements relaxed (acquire in field) • Efficiencies storage requirements relaxed (use less energy) • Multi-vehicle storage requirements relaxed (less energy/sys) Specific Power vs. Specific Energy Density 6 x increase in 20 Years Li-ion $650/kWh (2009) to $325/kWh (2020) Hydrocarbons have advantage for ~ 20 years

  13. Communications Trends Bps Connectivity Gilder’s Law (www) Nielsen’s Law (BW) Edholm’s Law Cooper’s Law: Effectiveness of Spectrum Doubles every 30 months. 1,000,000 x improvement since 1950. Gains from … Frequency re-use (smaller cells) x 2700, More spectrum x 15, Frequency division (narrower spectral slices) x 5, Modulation techniques (FM, TDMA, SS) x 5

  14. Autonomous System

  15. System Trends – HW vs. SW Fastest Supercomputers O(2.8)/10yrs Linear Programming O(3.4)/10yrs BW 25%/year Latency 5%/year Moore’s Law O(1.5)/10yrs Lumpy but Predictable over 10+ yrs Smooth and Predicable over 2-5 yrs Gilder’s Law O(2.9)/10yrs Brooks Law O(1.3)/10yrs Chess Playing, Voice & Facial Recognition O(2.9)/10yrs FFT/DFT & PDE Solver O(N2) -O(NlogN) Moore’s Law O(1.5)/10yrs Multiple & Interacting – Concurrency & I/O

  16. “Computer Intelligence” vs. Time 1.5 x 104 MIPS Deep Junior X Kasparov Life Average X X Laptop SW ~ $50 1-3 x 106 MIPS SW ~ $10M Deep Blue

  17. Performance/Watt vs. Time Can Solve Same Problem More Quickly or Bigger Problem in Same Amount of Time Better Software = Fewer Computations = Slower Processors = Less Supply Voltage = Much Less Power Power α Voltage2 Koomey et al 2009

  18. Software Complexity is Growing F-22 B-2 F-16 F-15 F-111 A-7 F-4 Nidiffer, 2008 • Software growth for fighter aircraft (Augustine)

  19. Software Growth & Processing Typical embedded system for top-of-the range car 1985 - 13kLOC 1989 - 21kLOC 1998 - 1MLOC 2000 - 2MLOC 2008 - 16MLOC 2010 - 32MLOC 44% compound growth/yr • Complexity growth of embedded software over time (Jones, 2007) Operating systems are expanding at a similar rate (Edwards, 2009) • Evolution of embedded systems in cars. The upper line is the number of embedded control units (ECUs) per car and the lower line the total power consumption in kW (Ebert & Jones, 2009)

  20. Software & Innovation • 80% functionality depends on software (IFR) • 70% development cost defence systems from software (Frichshorn 2004, Ebert 2007) • 90% of innovations driven by electronics and software (Koopman2011, Nidiffer 2008) • Number of mechanical defects decreasing • Number from electronic systems increasing • Code defect density function of software size

  21. Software Acquisition vs. Time Data from Standish Group’s CHAOS Report

  22. Trends & Financial Implications • Software production fixed (~15LOC/day) • Typical ECU software costs $15 - $40/LOC • $100k/yr @ 2000hrs/yr @ 2LOC/hr→$25/LOC • Defence work (w/documentation) $100/LOC • Safety critical (space shuttle) $1,000/LOC

  23. Software Testing • Software acquisitions are 46% over-budget by an average of 47%. Even “successful” projects have only 68% of specified features (Niddifer, 2011) • Total number of defects = (LOC/100)1.22 • Total number test cases = (LOC/100)1.24 (Ebert & Jones, 2009) • Google cars 200-300MLOC (Frost/Sullivan 2009) • 65M potential defects, 20% of which are high severity • 90-95% of errors usually found prior to delivery • Still leaves 650,000 high severity defects • And needs 50-100 million test cases

  24. Issues for Certification – The Google Cars • 300,000 miles w/out incident • Is this enough? • How much software was actually exercised • How many inputs were activated? • Were all interdependencies of inputs covered? • What were the test conditions? • What unknown system behaviours still exist? • Would you feel safe travelling in this car? • Would you allow your kids to travel unaccompanied? • Would 500,000 or 1,000,000 miles change this?

  25. Need “Trusted Autonomy” = Trusted and effective ‘out-of-the box’ in a broad range of situations Easily adapted to change, often through reconfiguration or replacement Able to detect and deliver graceful degradation outside performance envelopes • Are my decision trees and state diagrams correct? – Given perfect knowledge of an expected situation, will Autonomous System make the correct decision? • What about the unexpected? – When AS encounters new, difficult or unexpected situation, will it make safe and/or rational decision? • Can I trust my and/or AS perception of the situation? – Can the probabilistic uncertainty (level of assurance) be determined for information used to feed decisions? – What types of decisions can be made with this info? • As Autonomous Sys interacts with other systems, how do we prevent undesired emergent behaviour? – Can system(s) be certified in a piece-wise fashion?

  26. ?

  27. Moore’s & Dennard’s Laws Noordhaus, 2010 Moore: No. transistors that can be placed on chip will approx. double every two years Dennard: Transistors faster & lower energy

  28. Chip Cost vs. Time Chip Performance vs. Time Koomey, 2011

  29. Dennard’s Law Repealed? Noordhaus, 2010

  30. More-Moore vs. More-than-Moore Economics, Packing Density, Heat/ Power Density, Infrastructure Establishment Costs, etc. … ITRS, 2012

  31. Basic Energy Calculations • Energy carried ~ D3 • Operating range • Thrust (drag) ~ D2V2 • Propulsive power ~ TV (i.e. ~ D2V3) • Optimum velocity ~ • Hence, high/low VOPT for high/low PH • Remus100 high PH & Slocum low PH • Transit Efficiency (m/J)

  32. Platform Design Cost & handling logistics ~ D3 ($3,300/kg)* Curtin, 2005 *OSD, 2004

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