Research and Development at ARCAA, QUT: Advancing Aerospace Automation

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)

6. A dedicated research, development and commercialisation facility Space for ~40 researchers, developers World class simulation and testing facilities to be developed Fostering international collaboration ARCAA Facilities

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

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 • Axelradrms 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. Sensor Architecture 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

25. Results Abnormal Flight - Stall

26. GVSS Controlled Flight

27. Conclusion 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

28. Conclusion QUT Research Sampler #6 • 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!