Overview of Activities at the Australian Research Centre for Aerospace Automation (ARCAA), Queensland University of Technology - PowerPoint PPT Presentation

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Overview of Activities at the Australian Research Centre for Aerospace Automation (ARCAA), Queensland University of Technology

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Title: Overview of Activities at the Australian Research Centre for Aerospace Automation (ARCAA), Queensland University of Technology


1
Overview of Activities at the Australian Research
Centre for Aerospace Automation (ARCAA),
Queensland University of Technology
  • Troy Bruggemann
  • A/Prof Rod Walker

2
Outline
  • ARCAA Background
  • Current Research
  • Research Samples (Vision and GNSS)

3
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

4
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)

5
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6
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

7
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

8
ARCAA Workshop
  • Major Sponsor IEEE AESS
  • 100 delegates
  • Workshop to drive ARCAA research programs

9
ARCAA Workshop
10
Workshop Outcomes
OPPORTUNITIES Civ. Applications
KEY IMPEDIMENTS
Political, Social Regulatory
  • 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)
  • Infrastructure
  • Powerlines
  • Pipelines
  • Buildings
  • Towers / Bridges
  • Environmental
  • Bushfires
  • Farms / Land
  • Rivers / Reef
  • Search and support
  • Surveillance
  • Insurance
  • Regulations next generation 101
  • UAV Risk management
  • Certification standards and industry best
    practice framework
  • Community acceptance
  • UAV training

11
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

12
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

13
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?

14
QUT Research Sampler 2
15
QUT Research Sampler 2
16
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?

17
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.

18
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

19
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.

20
QUT Research Sampler 4
  • Research new strategies for aerodynamic modelling
    to improve GPS integrity monitoring for general
    aviation

21
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

22
QUT Research Sampler 6
  • Fixed Wing UAV Navigation and Control through
    IntegratedGNSS and Vision(GVSS)

23
QUT Research Sampler 6
  • Optic Flow Method
  • Image flow generated from image stream
  • Gradient based method being utilised

24
QUT Research Sampler 6
Sensor Architecture
  • 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

25
Abnormal Flight - Stall
Results
26
GVSS Controlled Flight
Mean deg Std. deg
Roll -0.25 0.9388
Pitch -0.1465 0.1898
Yaw 0.1651 0.3178
27
QUT Research Sampler 6
Conclusion
  • 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

28
QUT Research Sampler 6
Conclusion
  • Tightly Coupled GNSS / Vision Information for
    Improved Fault Tolerant UAV Flight Control
  • GNSS / Vision using Multiple Image Sources

29
Conclusions
  • ARCAA welcomes collaboration
  • Opportunity for further GNSS research at ARCAA
  • Thankyou!
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