Model-Driven Sensor Operation Assistance for a Transport Helicopter Crew in Manned-Unmanned Teaming Missions: Selecting the Automation Level by Machine Decision-Making

  • Christian RufEmail author
  • Peter Stütz
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 499)


One of the research fields at the Institute of Flight Systems (IFS) of the University of the Armed Forces (UniBwM) focuses on the integration of reconnaissance sensor operation support in manned-unmanned teaming (MUM-T) helicopter missions. The purposive deployment of mission sensors carried by a team of unmanned aerial vehicles (multi-UAV) in such missions is expected to bring in new and impactful aspects, especially in workload-intensive situations. Paradigms of variable automation in the sensor domain and cognitive assistant systems are intended to achieve an operationally manageable solution. This paper provides an overview of the sensor assistant system to be deployed in a MUM-T setup. To manage sensor deployment automation functions, a machine decision making process represented by an agent system will be described. Depending on a workload state input, a suitable level of automation will be chosen from a predefined set. A prototype system of such agent with its capability to react on varied stimuli will be demonstrated in a reduced toy problem setup.


MUM-T Multi-UAV Mission sensors Human operator Assistant system Adaptive automation Machine decision making Rational agent 


  1. 1.
    Onken, R., Schulte, A.: System-ergonomic design of cognitive automation: dual-mode cognitive design of vehicle guidance and control work systems. Springer Publishing Company, Incorporated (2012).
  2. 2.
    Strenzke, R., Uhrmann, J., Benzler, A., Maiwald, F., Rauschert, A., Schulte, A.: Managing cockpit crew excess task load in military manned-unmanned teaming missions by dual-mode cognitive automation approaches. AIAA Guid. Navig. Control Conf. (2011)Google Scholar
  3. 3.
    Uhrmann, J., Schulte, A.: Concept, design and evaluation of cognitive task-based UAV guidance. J. Adv. Intell. Syst. 5(1) (2012)Google Scholar
  4. 4.
    Endsley, M.R., Kiris, E.O.: The out-of-the-loop performance problem and level of control in automation. Hum. Factors J. Hum. Factors Ergon. Soc. 37(2), 381–394 (1995)CrossRefGoogle Scholar
  5. 5.
    Billings, C.E.: Aviation Automation: The Search for a Human-Centered Approach (1997) Lawrence Erlbaum Associates, Incorporated, NJ, USA Google Scholar
  6. 6.
    Wiener, E.L.: Human factors of advanced technology (glass cockpit) transport aircraft. (Nasa-Cr-177528), 222 (1989)Google Scholar
  7. 7.
    Parasuraman, R., Riley, V.: Humans and automation: use, misuse, disuse, abuse. Hum. Factors J. Hum. Factors Ergon. Soc. 39, 230–253 (1997)CrossRefGoogle Scholar
  8. 8.
    Muir, B.M.: Trust in automation: Part I. theoretical issues in the study of trust and human intervention in automated systems. Ergonomics 37 (1994)Google Scholar
  9. 9.
    Llinas, J., Liggins, M.E., Hall, D.L.: Handbook of Multisensor Data Fusion: Theory and Practice. CRC press (2008)Google Scholar
  10. 10.
    Onken, R.: Funktionsverteilung Pilot-Maschine: Umsetzung von Grundlagenforderungen im Cockpitassistenzsystem CASSY. In: Gärtner, K.-P. (ed.) DGLR-Bericht 94–01, Berlin: Deutsche Gesellschaft für Luft- und Raumfahrt (1994)Google Scholar
  11. 11.
    Sheridan, T.B.: adaptive automation, level of automation, allocation authority, supervisory control, and adaptive control: distinctions and modes of adaptation. Syst. Man Cybern. Part A Syst. Hum. IEEE Trans. 41(4), 662–667 (2011) Google Scholar
  12. 12.
    Endsley, M.: Level of automation effects on performance, situation awareness and workload in a dynamic control task. Ergonomics, 462–492 (1999)Google Scholar
  13. 13.
    Parasuraman, R., Sheridan, T.B., Wickens, C.D.: 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)Google Scholar
  14. 14.
    Scerbo, M.W.: Adaptive automation. In: Karwowsky, W. (ed.) International Encyclopedia of Human Factors, pp. 1077–1079. Taylor & Francis, London, U.K. (2001)Google Scholar
  15. 15.
    Russ, M., Schmitt, M., Hellert, C., Stütz, P.: Airborne sensor and perception management: a conceptual approach for surveillance UAS. In: Information Fusion (15th FUSION) (2012)Google Scholar
  16. 16.
    Hellert, C., Smirnov, D., Russ, M., Stütz, P.: A High Level Active Percpetion Concept For UAV Mission Scenarios. Dtsch Luft- und Raumfahrtkongress (2012)Google Scholar
  17. 17.
    Ruf, C., Stütz, P.: Ergonomische Einbindung des Sensor-Operateurs in eine MUM-T/ Multi-UAV Umgebung: Problemanalyse, Konzeptdarstellung und erste Modellbildung. Kooperation und kooperative Systeme in der Fahrzeug- und Prozessführung 2015–01, 79–96 (2015)Google Scholar
  18. 18.
    Honecker, F., Schulte, A.: Konzept für eine automatische evidenzbasierte Online-Pilotenbeobachtung in bemannt-unbemannten Hubschraubermissionen. In 4 Interdisziplinärer Workshop Kognitive Systeme: Mensch, Teams, Systeme und Automaten, Bielefeld (2015)Google Scholar
  19. 19.
    Rohr, K.: Landmark-Based Image Analysis: Using Geometric and Intensity Models. Kluwer Academic Publishers, Norwell, MA, USA (2001)CrossRefzbMATHGoogle Scholar
  20. 20.
    Burgin, M., Dodig-Crnkovic, G.: A Systematic Approach to Artificial Agents (2009).

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Institute of Flight SystemsUniversity of the Bundeswehr Munich (UniBwM)NeubibergGermany

Personalised recommendations