Anthony Finn - University of South Australia - Unmanned Aerial Vehicles: Global review of technology, roadmaps, roles, challenges, opportunities and predictions
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Anthony Finn - University of South Australia - Unmanned Aerial Vehicles: Global review of technology, roadmaps, roles, challenges, opportunities and predictions

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Anthony Finn, Director Defence and Systems Institute, University of South Australia delivered the presentation at the 2014 Unmanned Aerial Vehicles (UAV) in the Resources Industry. ...

Anthony Finn, Director Defence and Systems Institute, University of South Australia delivered the presentation at the 2014 Unmanned Aerial Vehicles (UAV) in the Resources Industry.

The 2014 Unmanned Aerial Vehicles (UAV) in the Resources Industry explored the enormous potential of UAVs within mining and resources operations.

For more information about the event, please visit: http://www.informa.com.au/uavresourcesconference14


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    Anthony Finn - University of South Australia - Unmanned Aerial Vehicles: Global review of technology, roadmaps, roles, challenges, opportunities and predictions Anthony Finn - University of South Australia - Unmanned Aerial Vehicles: Global review of technology, roadmaps, roles, challenges, opportunities and predictions Presentation Transcript

    • Part II Professor Anthony Finn Director, Defence & Systems Institute University of South Australia
    • Reduce human resources • Replace dull, dirty, dangerous work • Act as a force multiplier (reach & access) • Reduce costs, risks and liabilities Improve performance • Perform tasks not previously possible • Perform existing tasks better • Greater precision, higher accuracy • Rapid response to developing situations Increase survivability • Reduce susceptibility to communication failures • Reduces susceptibility of information interception • Reduce human and other machine interaction REDUCING HUMAN INTERACTION IS KEY, BUT NEED TO MAINTAIN REQUISITE LEVEL OF PERFORMANCE Military Benefits of UAVs IFR, 2009
    • Challenges & Issues Energy & Power Navigation & Mapping Sensing & Perception Learning & Behaviour Planning & Cognition Human-Robot Interaction Cooperation/Collaboration Technology/Software re-use Trustworthiness & -ability Evaluation, Metrics, T&E Operational Concepts/Uses Legal, Policy, Ethical …
    • Can they do the job? How will we use them? What technology trade-offs are involved? What systems & infrastructure required? What impact will technology have on work function? The Big Ones (1): Value of UAVs Time TRL 1 TRL 2 TRL 3 TRL 4 TRL 5 TRL 6 TRL 7 TRL 8 TRL 9 ABSI R&D CTDs Industry uptake Future Capability Acquisition (SEA4000, AIR6000, LAND400, …) Legal Framework Capability Exploitation Technology Development Conceive FMOC Experiment Analyse Implement StrategicContext * White Paper * ADF Joint Vision * Future Warfighting Concept * Allied Concepts * Technology Futures * Political Futures * Social/Demographic Futures Experimentation * War games * Tech Demonstrators * Operations Research * Operations Analysis * Sea Trials * Exercises Post-Experimentation * 2nd Order Analysis * Operations Research * Headmark Planning Conference * FMOC Annual Report * Headmark Annual Report * Capability Impact Statement Implementation * Capability Implications * Plan Green * Plan Blue * DCPG * Maritime Cap Dev Plan * Doctrine Technical Feasibility & Cost-Benefit for Unknown Capabilities (desirable vs. possible)
    • The Big Ones (2): Not About the UAV When you have worked out what you want the UAV to do (a) Do you just buy a UAV and use it ‘as is’ or (b) Do you change your system to accommodate the UAV or (c) Do you change both Organisational fit/business model
    • The Big Ones (3): Certification Airworthiness: the design of the aircraft must be approved; the aircraft must be manufactured in accordance with this design; and, the aircraft must be maintained in accordance with appropriate maintenance and configuration control procedures. Flight rules: the responsibilities and authority of the ‘pilot’ must be defined, as must operating rules for different classes of airspace, weather conditions, etc and any equipment that may be required onboard the aircraft. Operator qualifications: the licensing and training regimes for any pilots or crew need to be defined, together with any periodic activities required to maintain the currency of these qualifications.
    • Some General Trends for UAVs Before 1970’s Special Projects 1970’s Technical Research 1980’s Scientific Applications 1990’s Military Applications 2000’s Commercial Applications 2010’s Growth of New Industry Economic Viability Time Proof of Concept Build Confidence Increased Acceptance Routine Operational Use Commercial Products Emerge New Sampling Strategies 1980 201020001990 Single to multi-vehicle cooperatives (heterogeneous mix) Segregated use to integrated operations (incr. autonomy) Single to general/multi-purpose use (novel applications)
    • System Trends: HW vs. SW Engineers Programmers System Factor 4.3 x 106 Hardware Factor 1,000 Processors & Sub-Systems IC Devices Chip Architecture Memory Bus Bandwidth Architecture Software Factor 43,000 Compiler(s) Mathematical Functions Algorithmic Implementation Operating System(s) Users Gilder’s Law O(2.9)/10yrs Moore’s Law O(1.5)/10yrs Brooks Law O(1.3)/10yrs Chess Playing, Voice & Facial Recognition O(2.9)/10yrs Moore’s Law O(1.5)/10yrs FFT/DFT & PDE Solver O(N2) - O(NlogN) Fastest Supercomputers O(2.8)/10yrs Linear Programming O(3.4)/10yrsBW 25%/year Latency 5%/year
    • SW acquisitions 46% over-budget by avg 47% Even “successful” projects have only 68% of specified features (Niddifer, 2011) 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 Testing & Stability Implications
    • UAVs, Networks & Payloads  EO/IR/Thermal  Multi/Hyper-spectral  Foliage Penetrating Radar  SAR and mmWave Radar  Communications Relay  Acoustic Detection & ID  Acoustic Countermeasures  Meteorology (Tomography)  Router/Internet in the Sky  Electronic Warfare/Systems  Radar Target/Repeaters  Meteorological Sensors  LIDAR & 3-D Imaging  Polarimetric Sensors  Biomimetic Cameras  Chemical/Biological Sensors Large Files eg. Images & Video Multi-Hop Multiple Paths & Networks Extended Terrestrial Footprint Relay Trials Data DSTO 2009
    • Electro- optic vs thermal vs IR … Hyper/multi-spectral
    • 3-D Terrain Reconstruction Trials Data DSTO 2005 Van Den Hengel et al, 2005
    • LIDAR & EO Trials Data DSTO 2006 3-D Terrain Mapping Gibbins, Finn & Swierkowski, 2006
    • Feature Analysis Feature estimation aimed at determining terrain types (e.g. ground, vegetation, buildings) and ground vehicle traversability Gradient estimation based on local surface fitting to the raw 3D scatter-point data (bright regions indicate steep terrain) Local Curvature Estimation based on local surface fitting to the raw 3D scatter-point data (bright/dark regions indicate potentially undulating surfaces)
    • UAV Image Enhancement Low-resolution UAV images High-resolution reconstruction Buffer (9 consecutive frames) Register Reconstruct Low-resolution High- resolution Trials Data DSTO 2005Gibbins & Swierkowski, 2006
    • Self noise level – 110dB • Engine/Prop NB • Engine/Prop BB • Air Flow Noise • Mechanical Noise • Electrical noise • Eddies Aerosonde (k-twin) – 110dB DA-42 Twin Star – 137dB Noise from target • Engine/prop NB & BB • Propagation loss Acoustic Detection (360⁰ FOV) Air & Ground Targets All Weather
    • Tomography Used in physics, medicine & remote sensing Radon Transform
    • 3D Wind & Temp Profiles
    • Modelling Biological Motion Brinkworth & O'Carroll (2009) EMD Full bio-inspired vision model accurate across a range of images for 3 decades of motion
    • Practical Outcomes 1 Traditional cameras see either the dark parts… …or the light parts of an image Visual processing in retina allows both to be seen… …and remove redundancy before sending it to the visual cortex Time domain processing on a per-pixel basis – algorithm extremely parallel.
    • Practical Outcomes 2 Biomimetic Original Post-processed Brinkworth & O'Carroll (2008) Good edge detection Hard target detection Small target detection
    • TYPICAL PERFORMANCE METRICS Utilization of Operator • % of time the operator is busy servicing interactions • Can be used to predict performance according to an Yerkes-Dodson curve (Nehme et al., 2009). Mean/Max number of serviceable units/interactions Utilization of Autonomous Machine • Average time of autonomous machines waiting for interaction • Average number of autonomous machines waiting to be serviced • Probability that autonomous machine must wait > X for interaction Modelling HMI
    • Questions
    • Polarisation Compass & Micro UAV Avionics Polarisation sensitive facets of insect eyes Rayleigh scattering causes sky polarisation Mag compass unreliable in many situations Sky Polarisation Pattern MarsSteel SheetReinforced Concrete Micro Avionics All UAV/UAS functionality on a single board incl. antenna