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Apr 08, 2023 •534 likes •861 views

A learning model for RPAS sensor operators and its implications for training Remotely Piloted Aircraft (RPA) Ground Control Station ITEC, 14-16 May 2019, Stockholm, Sweden ITEC - 14-16 May 2019 - Stockholm - Sweden 1 R&D Team & Roles

  1. A learning model for RPAS sensor operators and its implications for training Remotely Piloted Aircraft (RPA) Ground Control Station ITEC, 14-16 May 2019, Stockholm, Sweden ITEC - 14-16 May 2019 - Stockholm - Sweden 1

  2. R&D Team & Roles Gerald Jan Joris Olaf Brouwer Joost van Oijen Poppinga Roessingh Simulation, Operator Defence Training, Artificial Performance Systems, Simulation Intelligence Artificial Intelligence ITEC - 14-16 May 2019 - Stockholm - Sweden 2

  3. Programme • Drones in the RNLAF • Required Knowledge at the RNLAF • Approach • Part task Training and Transfer • Modelling of Human Operators with AI for training requirements • Conclusions: – Overview – Applicability ITEC - 14-16 May 2019 - Stockholm - Sweden 3

  4. Drones at the Netherlands Air Force: today and tomorrow High Altitude Pseudo Satellite (HAPS) AGS RQ-4 (HALE) MQ-9 (MALE) Source: edrmagazine.eu Source: insideunmannedsystems.com 4 ITEC - 14-16 May 2019 - Stockholm - Sweden

  5. Drones at the Netherlands Air Force: future Unmanned Cargo Aircraft Unmanned Vertical Lift UCAV Source: defensesystems.com/articles/2016/03/07/darpa-vtol-x- Source: Airbus https://www.airbus.com/defence/uav.html plane-phase-2.aspx Source: Marcus Ruetten, DLR, researchgate.net, 2014 ITEC - 14-16 May 2019 - Stockholm - Sweden 5

  6. Remote Split Operations MALE Source: Policy Options for Unmanned Aircraft Systems , US Congressional Budget Office, 2011 ITEC - 14-16 May 2019 - Stockholm - Sweden 6

  7. Remote Split Ops: involved Flight Crew • 1 system : 4 aircraft • 1 CAP : 24/7 aircraft above area of interest Mission Control Launch & Recovery Processing, Element (2 GCS) Element (1 GCS) Exploitation & Dissemination Pilots 7 Pilots 3 Sensor Operators 7 Sensor Operators 3 Analists 52 Mission coordinators 5 Other 24 Other 53 Other 14 USAF-numbers (164 FTE in total for 1 system): Deptula, D. (2010). The Way Ahead: Remotely Piloted Aircraft in the United States Air Force, U.S. Air Force, briefing, downloaded December 2014 from http://www.daytonregion.com/pdf/UAV_Rountable_5.pdf. ITEC - 14-16 May 2019 - Stockholm - Sweden 7

  8. Knowledge required at the Air Force • Drones – Which requirements for education and training? • Training – How to realise higher yields for training at lower costs? • Artificial Intelligence / Machine Learning – How to model [ requirements for education and training ] with Machine Learning? Source: RNLAF Research & Technology Roadmap 2020 ITEC - 14-16 May 2019 - Stockholm - Sweden 8

  9. Training Analysis Approach Tasks of Flight Crew Skills Flight Crew Training Priorities (DIF) Operation Training Objectives Training Programme Type Initial Qualification Mission { AI Embedded – Simulator – Games - Class Training Media ITEC - 14-16 May 2019 - Stockholm - Sweden 9

  10. Tasks – Competencies – Training Priorities Flight Crew Focus: Sensor Operator Scope: During Flight ITEC - 14-16 May 2019 - Stockholm - Sweden 10

  11. Training Objectives - Tr. Programme Tr. Media Serious game Serious game Simulator Weapon system Source: camber.com Source: sds.com Source: USAF (af.mil) Which training strategies give the best ‘transfer -of- training’ ? ITEC - 14-16 May 2019 - Stockholm - Sweden 11

  12. Part task training A part task is a segment , a fraction , or a simplification of a whole task .. Part task 1 Part task 2 Part task 1 Feature Part task 2 Segmentation Fractionation Simplification .. or a combination thereof.

  13. Re-integration of part-tasks during training Bron: NLR-TP-2002-646 ITEC - 14-16 May 2019 - Stockholm - Sweden 13

  14. Example: Cumulative Part Task Training with AI Enemy/friendly Multiple targets Moving target Part-task 1/3 1/3 1/3 Non-part-task 75 million frames (~90 hours training) ITEC - 14-16 May 2019 - Stockholm - Sweden 14 14

  15. Cumulative Part Task Training with AI ITEC - 14-16 May 2019 - Stockholm - Sweden 15

  16. Modelling of Human Operators with A.I. ITEC - 14-16 May 2019 - Stockholm - Sweden 16

  17. Using AI to predict the human learning process ITEC - 14-16 May 2019 - Stockholm - Sweden 17

  18. AI Model: Serious Games as a learning environment Human Operator Serious game AI Model learning learning Data Data Predictive capability comparing ITEC - 14-16 May 2019 - Stockholm - Sweden 18

  19. Serious game for training complex task Space Fortress • Designed under DARPA LSP (Eighties) – Research of instructional strategies, human learning of complex skills • Contains complex cognitive and perceptual- motor tasks • Learned skills are transferable to the operational task environment ITEC - 14-16 May 2019 - Stockholm - Sweden 19

  20. Space Fortress ITEC - 14-16 May 2019 - Stockholm - Sweden 20

  21. Research • Can a machine learn a complex task such as Space Fortress of drone sensor handling? • How does this learning process compare with the human learning process? – Comparison between man and machine • Learning Curves • Part Task Training (Transfer) ITEC - 14-16 May 2019 - Stockholm - Sweden 21

  22. Learning of Atari games (DeepMind, 2014) ITEC - 14-16 May 2019 - Stockholm - Sweden 22

  23. Machine Learning of Space Fortress ITEC - 14-16 May 2019 - Stockholm - Sweden 23

  24. Comparing Human Learning with Machine Learning ITEC - 14-16 May 2019 - Stockholm - Sweden 24

  25. Part Task Training in Space Fortress • Examined as an instructional strategy for humans • Does part task training yield similar results as with machines? DEELTAAK 2 VOLLEDIGE TAAK DEELTAAK 1 IFF (GEEN MIJNEN) (+ MIJNEN) (+ IFF MIJNEN) ITEC - 14-16 May 2019 - Stockholm - Sweden 25

  26. Part Task Training in Space Fortress Whole Task IFF Part Task 1 IFF Part Task 2 IFF ITEC - 14-16 May 2019 - Stockholm - Sweden 26

  27. Leerning curves of man and Machine • Power Law of Practice Mens: ~ 20 hrs training RT Machine: ~ 800 hrs training RT ITEC - 14-16 May 2019 - Stockholm - Sweden 27

  28. Conclusions • A machine (AI model) is capable to learn a complex task • The machine has a diminished ‘sample - efficiency’ but, eventually, performs better than humans – General ‘ problem ’ with machine learning (amount of data) • Human-Machine Comparisons – Characteristic shape of the learning curves is comparable – Part Task Training : The machine exhibits similar transfer • Future work – To develop better predictors based on state-of-the-art AI algorithms ITEC - 14-16 May 2019 - Stockholm - Sweden 28

  29. Applicability for Sensor Operators • Results relevant for recruitment, selection and training of Sensor Operators • Prediction of transfer-of-training seems possible – Validation with NLR’s RPAS simulator – relevant tasks • Delay/ failure of data link • Hand-over between Ground Control Stations • Sense-and-Avoid taken ITEC - 14-16 May 2019 - Stockholm - Sweden 29

  30. Visit our booth! NLR ITEC - 14-16 May 2019 - Stockholm - Sweden 30

  31. Verrassend betrokken Nederlands Lucht- en Ruimtevaartcentrum NLR Amsterdam NLR Marknesse Anthony Fokkerweg 2 Voorsterweg 31 1059 CM Amsterdam 8316 PR Marknesse t ) +31 88 511 3113 f ) +31 88 511 3210 t ) +31 88 511 4444 f ) +31 88 511 4210 e ) info@nlr.nl i ) www.nlr.nl e ) info@nlr.nl i ) www.nlr.nl

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