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Overview of Activities at the Australian Research Centre for

Aerospace Automation (ARCAA), Queensland University of

Technology Troy BruggemannA/Prof Rod Walker

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Outline

• ARCAA Background

• Current Research

• Research Samples (Vision and GNSS)

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What is ARCAA?• A successful QLD Government Smart State

Research Facility Fund bid ($4M +)• Joint venture: CSIRO ICT Centre and QUT

Airborne Avionics Research group • Wide-spread support of industry and

government (DSTO, DITR, BAL, Boeing PW, SMEs)

• Initial focus on Civil Unmanned Air Vehicle (UAV) research for high-autonomy applications

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Research• Research to remove impediments facing the

routine use of UAVs for civilian applications– Focus on safety (vision, GNSS)– Reliability – Certification by regulators– Reduced operator requirements– Robustness (GNSS)– Reduced cost (increased automation)– Public acceptance (societal issues)

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ARCAA Facilities

• A dedicated research, development and commercialisation facility

• Space for ~40 researchers, developers

• World class simulation and testing facilities to be developed

• Fostering international collaboration

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Who we are?

• CRC for Satellite Systems• 18 PhD students in 2006• 30 undergraduate

Avionics students/year• 5 full-time CSIRO staff• 8 full-time QUT staff

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ARCAA Workshop• Major Sponsor

IEEE AESS• 100+

delegates• Workshop to

drive ARCAA research programs

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ARCAA Workshop

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NWorkshop OutcomesWorkshop Outcomes

Flight Systems & Safety

– Reliable low cost systems (GNSS)

– Safety of Autonomous Aircraft• ‘Virtual safety bubble’• See-N-Avoid • Advanced FTS (Forced

Landing)– Future air traffic management

technologies (pFMS)– Increased onboard autonomy – Intuitive Operator interfaces

(Drag-N-Fly)

OPPORTUNITIES

Civ. Applications

– Infrastructure• Powerlines• Pipelines• Buildings• Towers / Bridges

– Environmental• Bushfires• Farms / Land• Rivers / Reef

– Search and support– Surveillance

Political, Social & Regulatory

– Insurance

– Regulations – next generation ‘101’

– UAV Risk management

– Certification standards and industry ‘best practice framework

– Community acceptance

– UAV training

KEY IMPEDIMENTS

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Current QUT Research Areas• advanced collision avoidance systems • intelligent mission planning • flight termination systems • vision-based navigation and GNSS attitude

determination systems • onboard flight performance analysis and adaptive

control • investigation into UAV risk identification and certification • airborne Ground-based Regional Augmentation System

(GRAS) receiver

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QUT Research Sampler #1• Vision-Based method of

estimating Pitch and Roll• Real-Time implementation

on standard computers• Developed for wide range

of cameras• Provides a level of virtual

redundancy

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QUT Research Sampler #2

• UAV Collision Avoidance– Current FAA regulations require UAVs to be

provided with…

“a method that provides an equivalent level of safety, comparable to the see-and-avoid requirements of manned aircraft”

[U.S. FAA Order 7106.4 Chapter 12, Section 9]

– Can computer vision be used to provide a reliable, cost-effective see and avoid capability?

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QUT Research Sampler #2

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QUT Research Sampler #2

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QUT Research Sampler #3• UAV Forced Landing

Research• Human pilots trained for

forced landings– Detect and evaluate slope,

surface, shape, field surroundings, proximity to emergency services

• Why not UAVs?

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QUT Research Sampler #4

• GRAS Airborne Navigation Receiver Augmentation using Low CostMEMS Inertial Sensors and aerodynamic modelling for General Aviation Aircraft

• This research is funded by the ARC, Airservices Australia and GPSat Systems Australia.

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QUT Research Sampler #4

• Require Signal-in-Space of GRAS and GPS– Coverage limited by line-of-sight and modulation

scheme– What areas are out of coverage at altitudes where

GA are operating?

• Cannot account for local effects – Un-modelled atmospheric effects (eg. scintillation)– Multi-path, receiver errors, equipment failures

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QUT Research Sampler #4

• Development of a framework and architecture for high integrity navigation for G.A aircraft using– GRAS technology– MEMS technology

• Evaluate the benefits and remaining challenges of using low-cost MEMS inertial devices for approach navigation in G.A.

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QUT Research Sampler #4

• Research new strategies for aerodynamic modelling to improve GPS integrity monitoring for general aviation

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QUT Research Sampler #5• Single Antenna GPS

Attitude Algorithm for non-uniform Antenna Gain Pattern

• New algorithm RMS error = 13.8 deg.

Previous algorithms:• Duncan rms error =

21.5 deg

• Axelrad rms error = 16.4 deg

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QUT Research Sampler #6

• Fixed Wing UAV Navigation and Control through IntegratedGNSS and Vision(GVSS)

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QUT Research Sampler #6• Optic Flow

Method

• Image flow generated from image stream– Gradient based

method being utilised

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QUT Research Sampler #6• Optic Flow Method

– Looking at characteristics of gradient based optic flow and the possibility of utilizing other methods

– Feature tracking approach for sparse velocity measurements

Sensor Architecture

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Abnormal Flight - StallResults

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GVSS Controlled Flight

Mean [deg] Std. [deg]

Roll -0.25 0.9388

Pitch

-0.1465 0.1898

Yaw 0.1651 0.3178

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QUT Research Sampler #6• The GVSS shows potential in the confines

of the simulation environment– Sub-degree Euler angle accuracy– Capable of being used to drive the control

loop– Flight path information– Flight control information– Collision avoidance information

Conclusion

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QUT Research Sampler #6• Tightly Coupled GNSS / Vision

Information for Improved Fault Tolerant UAV Flight Control

• GNSS / Vision using Multiple Image Sources

Conclusion

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Conclusions

• ARCAA welcomes collaboration

• Opportunity for further GNSS research at ARCAA

• Thankyou!