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

Strategic Context * 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 2010 2000 1990

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)/10yrs BW 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 2005 Gibbins & 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

Mars Steel Sheet Reinforced Concrete

Micro Avionics

All UAV/UAS

functionality

on a single

board incl.

antenna