Torsten Merz - CSIRO - Towards autonomous unmanned aircraft for low-altitude remote sensing
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Torsten Merz - CSIRO - Towards autonomous unmanned aircraft for low-altitude remote sensing

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Torsten Merz, Senior Research Scientist, CSIRO delivered the presentation at the 2014 Unmanned Aerial Vehicles (UAV) in the Resources Industry. ...

Torsten Merz, Senior Research Scientist, CSIRO 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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  • 1. Towards Autonomous Unmanned Aircraft for Low-Altitude Remote Sensing Torsten Merz Unmanned Aerial Vehicles in the Resources Industry – Perth, 26-27 June 2014 Autonomous Systems Program – CSIRO Computational Informatics www.csiro.au
  • 2. informa: Slide 2 of 31 Overview • Definitions, concepts and incentives • Aircraft system selection and operational considerations • Remote sensing workflow • Prototype systems developed at CSIRO • Autonomous aircraft and regulations • Conclusion
  • 3. informa: Slide 3 of 31 Definitions • Remote sensing: ”Remote sensing is a way of collecting and analysing data to get information about an object without the instrument used to collect the data being in direct contact with the object.” [ESA] • Autonomous unmanned aircraft: ”An aircraft that does not re- quire a human pilot to fly a given mission in a specified environ- ment.” • Low-altitude: airborne to a height manned aircraft typically don’t cruise (<400ft AGL)
  • 4. informa: Slide 4 of 31 Unmanned Aircraft Terminology • UAV/S → RPA(S): Remotely Piloted Aircraft (System) [CASA/ICAO] • Drone: stingless male bee making no honey; term introduced by the military for an unmanned aircraft used as training target • Autonomous aircraft: ”An unmanned aircraft that does not allow pilot intervention in the management of flight” [CASA/ICAO] • Remote pilot: ”The person who manages the flight controls of a remotely piloted aircraft during flight time.” [CASA/ICAO]
  • 5. informa: Slide 5 of 31 Why Remote Sensing? • Coverage of large (possibly difficult to access) areas • High temporal and spatial resolution • Lower costs, faster and safer compared to ground-based inves- tigation/monitoring • Low-altitude: – typically higher temporal and spatial resolution – possibly lower costs (operations, sensors) – easier from a regulatory perspective – BUT coverage of large areas problematic → swarm of un- manned aircraft
  • 6. informa: Slide 6 of 31 Applications in the Resources Industry • Structural geology • Mineral identification • Regolith mapping • Geophysical surveys • Site scoping and mapping • Stockpile volume mapping • Stope mapping • Inspection of fixed plant and to get a better analysis of potential shutdown for repair • Monitor for any subsidence above or adjacent to underground mines • Thermal monitoring of coal stockpiles • Pit mapping for highwalls, pit walls and development faces • Surveying and monitoring offshore oil/gas infrastructure • New uses for aerial real time truck management, site and remote infras- tructure monitoring, and machinery tracking • Remove risk and increase safety for site personnel • Monitor the performance of mine tailings facilities
  • 7. informa: Slide 7 of 31 Autonomy • No clear definition • Autonomy is regarded as a relational notion • Relative autonomy: relation between a system, its human op- erator, the mission and the environment (ALFUS framework, NIST) • Level of autonomy can be related to human independence, mis- sion complexity and environmental complexity • We do not consider self-motivated systems here • Full autonomy → no human intervention required to complete a given mission • Automation can be seen as low-level autonomy • Autonomy does not necessarily imply dependability
  • 8. informa: Slide 8 of 31 Dependability • Term introduced by Jean-Claude Laprie in the early 80s for computing systems • ”ability to deliver service that can justifiably be trusted” • Three parts: attributes of dependability, threats to dependability, means to attain dependability • Attributes: availability, reliability, safety, confidentiality, integrity, maintainability • Threats: faults, errors, failures • Means: fault prevention, fault tolerance, fault removal, fault fore- casting • Our research is towards dependable autonomy of unmanned aircraft systems
  • 9. informa: Slide 9 of 31 Why Autonomous Unmanned Aircraft? • Autonomous aircraft can consistently perform flights which are difficult for human pilots → better remote sensing data • Costs and availability argument • Autonomy → relaxed communication requirements • Facilitates operations of swarms of unmanned aircraft • Human piloted unmanned aircraft not necessarily safer: – situational awareness and control authority in case of fail- ures typically limited for pilots of unmanned aircraft com- pared to manned aircraft – human errors – human machine interaction prone to problems
  • 10. informa: Slide 10 of 31 Aircraft System Selection • Rotorcraft vs. fixed-wing vs. aerostats • Type depends on remote sensing task and type of operation: – flight envelope (hover requirement?) – payload capacity and endurance – propulsion (combustion/electric) – available area for takeoff and landing – handling (transport, piloting) • Size/gross weight of aircraft • Costs, dependability, certification
  • 11. informa: Slide 11 of 31 Type of Operations • Interactive vs. non-interactive operations (→ level of autonomy) • Operations from fixed base locations vs. operations from ran- dom locations • Tethered vs. un-tethered flight • Single aircraft vs. swarm • COTS solution vs. custom development: fully integrated sys- tems optimised for a specific tasks often not available → com- promised solution
  • 12. informa: Slide 12 of 31 Remote sensing workflow Pre-mission (mission specification) • mission area definition • sensor selection/configuration • flight plan creation with image capture positions incorporating resolution, coverage, timing requirements Mission execution Post-mission • check data (image quality, on-site) • check coverage (on-site) • data analysis
  • 13. informa: Slide 13 of 31 Dependable Autonomous Flight Services • Take off • Hover (including heading change) • Straight line and circular curve path following • Waypoint flight with obstacle avoidance • (Waypoint flight with air traffic control interaction) • Terrain following • Close-range inspection • Duck manoeuver (aircraft avoidance) • (Landing) (CSIRO single-rotor research helicopters)
  • 14. informa: Slide 14 of 31 Infrastructure Inspection Helicopter Prototype Inspection camera (forward−looking) 2D LIDAR (vertical scan) • 12.3kg gross weight • 1.78m rotor diameter • ∼1h endurance • Petrol engine Smart Skies Project (www.arcaa.net/research/smart-skies-project)
  • 15. informa: Slide 15 of 31 Infrastructure Inspection Mission 1.4km
  • 16. informa: Slide 16 of 31 Phenocopter Remote sensing cameras (downward−looking)
  • 17. informa: Slide 17 of 31 Mission Planning Tools • Tools for missions in flat (shown above) and mountainous terrain • Platform independent software (Linux, Windows, Mac OS)
  • 18. informa: Slide 18 of 31 Setup • Helicopter + wireless operator interface + transport vehicle • Takeoff area: ∼4 m diameter, even ground, <5 degrees slope • Operation: 1-2 people, <30 min setup time • 4 helicopters, 55 days, 37.5 flight hours
  • 19. informa: Slide 19 of 31 Typical Crop Monitoring Mission 50m Autonomous flight Ground station First waypoint Last waypoint landing and Take off Manual flight [m] [s] Autonomous flight Manual flight Height Reference
  • 20. informa: Slide 20 of 31 Post-flight Analysis Tools • Visualisation of camera poses and quick access of correspond- ing images recorded during flight • Automated image analysis
  • 21. informa: Slide 21 of 31 Wheat Trial - Multispectral Imaging • Visual, NIR, LWIR spectral range • Separate calibrated cameras • All images recorded on-board in raw format with camera pose
  • 22. informa: Slide 22 of 31 Wheat Trial - Image Analysis • Plant coverage estimation • DEM for lodging analysis • Contact: Scott Chapman, CSIRO Plant Industry
  • 23. informa: Slide 23 of 31 Weed Survey Helicopter Prototype Pilotless low-altitude flight in mountainous terrain Project ResQu (www.arcaa.net/research/resqu)
  • 24. informa: Slide 24 of 31 Video - Miconia Weed Survey El Arish near Cairns
  • 25. informa: Slide 25 of 31 Unmanned Rotorcraft for Sponcom Monitoring • Thermal infrared imagery on-board a quadcopter • Enables automatic mapping and monitoring of large areas for heatings • Field trials at old coal mine workings • Contact: John Malos, CSIRO Earth Science and Resource En- gineering (CESRE)
  • 26. informa: Slide 26 of 31 Unmanned Rotorcraft for Surface Characterisation • Acquire high resolution spatial data (∼100µm) of fractures in slope or mine bench • Enables estimation of surface roughness for geotechnical and hydrogeological analysis • Contact: Peter Dean, CSIRO Earth Science and Resource En- gineering (CESRE)
  • 27. informa: Slide 27 of 31 3D Mapping • CSIRO Zebedee 3D mapping technology integrated on a quad- copter • Faster mapping and maps with less gaps • Demonstrated at a marble quarry in NSW • Contact: Robert Zlot, CSIRO Computational Informatics (CCI)
  • 28. informa: Slide 28 of 31 Autonomous Systems and Regulations • CASA/ICAO currently have different understanding of meaning of autonomy • Current and proposed new regulations don’t consider opera- tions of pilotless aircraft in Australia • Pilotless does not mean unsupervised • It used to be feasible to legally conduct R&D work on pilotless aircraft in Australia • We proposed a way to safely operate pilotless aircraft based on scientific evidence • Current focus on certification of remote pilots and organisations rather than autonomous systems ↔ autonomous cars in the US • This may hinder the deployment of unmanned aircraft in the re- sources industry especially considering future automation pro- grams
  • 29. informa: Slide 29 of 31 RPAs and Regulations • RPAs may include autonomous system components but a re- mote pilot must be able to manage the flight controls • Commercial operations: RPA operator certificate + certified re- mote pilots • Less restrictions for operations of RPAs: – with gross weight <150/100kg (2kg) – operations in Class G airspace <400ft AGL >3NM from aerodromes – clear of populous areas – in daylight VMC conditions – within line-of-sight
  • 30. informa: Slide 30 of 31 Conclusion • Unmanned aircraft useful for the resources industry pending fu- ture regulatory restrictions • RPAs commercially available and maturing but dependable pi- lotless aircraft still in their infancy • Carefully choose systems and how to operate • Integrated systems optimised for specific tasks may not be avail- able off-the-shelf • Provided incentives for autonomous aircraft and discussed reg- ulatory restrictions • Pilotless aircraft technology is being developed in CSIRO and validated in real-world applications
  • 31. Autonomous Systems Program – CSIRO Computational Informatics www.csiro.au CCI Autonomous Systems Program Dr.-Ing. Torsten Merz t +61 7 3327 4123 e torsten.merz@csiro.au w http://www.csiro.au