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Operator Interaction with Centralized Versus Decentralized UAV Architectures

  • M. L. Cummings
Reference work entry

Abstract

There has been significant recent research activity attempting to streamline Unmanned Aerial Vehicle (UAV) operations and reduce staffing in order to invert the current many-to-one ratio of operators to vehicles. Centralized multiple UAV architectures have been proposed where a single operator interacts with and oversees every UAV in the network. However, a centralized network requires significant operator cognitive resources. Decentralized multiple UAV networks are another, more complex possible architecture where an operator interacts with an automated mission and payload manager, which coordinates a set of tasks for a group of highly autonomous vehicles. While a single operator can maintain effective control of a relatively small network of centralized UAVs, decentralized architectures are more scalable, particularly in terms of operator workload, and more robust to single points of failure. However, in terms of operator workload, the ultimate success of either a centralized or decentralized UAV architecture is not how many vehicles are in the network per se but rather how many tasks the group of vehicles generates for the operator and how much autonomy is onboard these vehicles. Task-based control of UAV architectures with higher degrees

Keywords

Unman Aerial Vehicle Situation Awareness Single Operator Centralize Architecture Multiple UAVs 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. A. Andre, C. Wickens, When users want what's not best for them. Ergon. Des. 3, 10–14 (1995)Google Scholar
  2. A.S. Clare, M.L. Cummings, Task-based interfaces for decentralized multiple unmanned vehicle control, in AUVSI Unmanned Systems North America, Washington, D.C., 2011Google Scholar
  3. M.L. Cummings, S. Guerlain, Developing operator capacity estimates for supervisory control of autonomous vehicles. Hum. Factors 49(1), 1–15 (2007)CrossRefGoogle Scholar
  4. M.L. Cummings, P.J. Mitchell, Automated scheduling decision support for supervisory control of multiple UAVs. AIAA J. Aerosp. Comput. Inf. Commun. 3(6), 294–308 (2006)CrossRefGoogle Scholar
  5. M.L. Cummings, P.J. Mitchell, Predicting controller capacity in supervisory control of multiple UAVs. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 38(2), 451–460 (2008)CrossRefGoogle Scholar
  6. M.L. Cummings, C.E. Nehme, J. Crandall, Predicting operator capacity for supervisory control of multiple UAVs, in Innovations in Intelligent Machines, vol. 70, ed. by J.S. Chahl, L.C. Jain, A. Mizutani, M. Sato-Ilic (Springer, Berlin/New York, 2007)Google Scholar
  7. M.L. Cummings, A. Clare, C. Hart, The role of human-automation consensus in multiple unmanned vehicle scheduling. Hum. Factors 52(1), 17–27 (2010)CrossRefGoogle Scholar
  8. M.L. Cummings, J. How, A. Whitten, O. Toupet, The impact of human-automation collaboration in decentralized multiple unmanned vehicle control. Proc. IEEE 100(3), 660–671 (2012)CrossRefGoogle Scholar
  9. M.L. Cummings, C. Mastracchio, K.M. Thornburg, A. Mkrtchyan, Boredom and distraction in multiple unmanned vehicle supervisory control. Interact. Comput. 25(1), 34–47 (2013)Google Scholar
  10. Defense Science Board, The role of autonomy in DoD systems. Department of Defense, 2012Google Scholar
  11. S.R. Dixon, C.D. Wickens, D. Chang, Comparing quantitative model predictions to experimental data in multiple-UAV flight control, in Human Factors and Ergonomics Society 47th Annual Meeting, Denver, 2003Google Scholar
  12. S. Dixon, C. Wickens, D. Chang, Mission control of multiple unmanned aerial vehicles: a workload analysis. Hum. Factors 47, 479–487 (2005)CrossRefGoogle Scholar
  13. B. Donmez, C. Nehme, M.L. Cummings, Modeling workload impact in multiple unmanned vehicle supervisory control. IEEE Syst. Man Cybern. Part A Syst. Hum. 99, 1–11 (2010)Google Scholar
  14. R.D. Dunlap, The evolution of a distributed command and control architecture for semi-autonomous air vehicle operations, in Moving Autonomy Forward Conference, Grantham (Muretex, 2006)Google Scholar
  15. M.R. Endsley, Toward a theory of situation awareness in dynamic systems. Hum. Factors 37(1), 32–64 (1995)CrossRefGoogle Scholar
  16. M.R. Endsley, D.J. Garland, Situation Awareness Analysis and Measurement (Lawrence Erlbaum, Mahwah, 2000)Google Scholar
  17. B. Hilburn, P.G. Jorna, E.A. Byrne, R. Parasuraman, The effect of adaptive air traffic control (ATC) decision aiding on controller mental workload, in Human-Automation Interaction: Research and Practice, ed. by M. Mouloua, J.M. Koonce (Lawrence Erlbaum, Mahwah, 1997), pp. 84–91Google Scholar
  18. Joint Chiefs of Staff, Chairman of the Joint Chiefs of Staff instruction 6212.01D. DoD, 2007Google Scholar
  19. M. Lewis, J. Polvichai, K. Sycara, P. Scerri, Scaling-up human control for large UAV teams, in Human Factors of Remotely Operated Vehicles, ed. by N. Cooke, H. Pringle, H. Pedersen, O. Connor (Elsevier, New York, 2006), pp. 237–250CrossRefGoogle Scholar
  20. K.L. Mosier, L.J. Skitka, Human decision makers and automated decision aids: made for each other? in Automation and Human Performance: Theory and Applications, Human Factors in Transportation, ed. by R. Parasuraman, M. Mouloua (Lawrence Erlbaum, Mahwah, 1996), pp. 201–220Google Scholar
  21. C.E. Nehme, Modeling human supervisory control in heterogeneous unmanned vehicle systems. Doctor of philosophy, Massachusetts Institute of Technology, 2009Google Scholar
  22. C.E. Nehme, J. Crandall, M.L. Cummings, Using discrete-event simulation to model situational awareness of unmanned-vehicle operators, in 2008 Capstone Conference, Norfolk, 2008Google Scholar
  23. D.R. Olsen, S.B. Wood, Fan-out: measuring human control of multiple robots, in SIGCHI conference on Human factors in Computing Systems, Vienna, 2004Google Scholar
  24. R. Parasuraman, T.B. Sheridan, C.D. Wickens, A model for types and levels of human interaction with automation. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 30(3), 286–297 (2000)CrossRefGoogle Scholar
  25. M.D. Rodgers, R.H. Mogford, B. Strauch, Post hoc assessment of situation awareness in air traffic control incidents and major aircraft accidents, in Situation Awareness Analysis and Measurement, ed. by M. Endsley, D.J. Garland (Lawrence Erlbaum, Mahwah, 2000), pp. 73–112Google Scholar
  26. W.B. Rouse, Systems Engineering Models of Human-Machine Interaction (North Holland, New York, 1983)Google Scholar
  27. H. Ruff, S. Narayanan, M.H. Draper, Human interaction with levels of automation and decision-aid fidelity in the supervisory control of multiple simulated unmanned air vehicles. Presence 11(4), 335–351 (2002)CrossRefGoogle Scholar
  28. D.K. Schmidt, A queuing analysis of the air traffic controller's workload. IEEE Trans. Syst. Man Cybern. 8(6), 492–498 (1978)CrossRefGoogle Scholar
  29. T.B. Sheridan, W. Verplank, Human and computer control of undersea teleoperators. Man-Machine Systems Laboratory, Department of Mechanical Engineering, MIT, Cambridge, 1978Google Scholar
  30. H.A. Simon, R. Hogarth, C.R. Piott, H. Raiffa, K.A. Schelling, R. Thaier, A. Tversky, S. Winter, Decision making and problem solving, in Research Briefings 1986: Report of the Research Briefing Panel on Decision Making and Problem Solving (National Academy Press, Washington D.C., 1986)Google Scholar
  31. K.W. Williams, A summary of unmanned aircraft accident/incident data: human factors implications. Federal Aviation Administration, Civil Aerospace Medical Institute, Oklahoma City, 2004Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Department of Aeronautics and AstronauticsMassachusetts Institute of TechnologyCambridgeUSA

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