Advertisement

Model-Driven Payload Sensor Operation Assistance for a Transport Helicopter Crew in Manned–Unmanned Teaming Missions: Assistance Realization, Modelling Experimental Evaluation of Mental Workload

  • Christian Ruf
  • Peter Stütz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10275)

Abstract

One of the research fields at the Institute of Flight Systems (IFS) of the University of the Armed Forces (UniBwM) concerns the integration of reconnaissance sensor operator support in manned-unmanned teaming (MUM–T) transport helicopter (HC) missions. The purposive deployment of mission sensors carried by several unmanned aerial vehicles (multi–UAV) in such missions brings in new and impactful aspects, specifically in workload-intensive situations. An associate system offering variable automation levels supports the HC’s crew by deploying machine-executable functionalities and high-level capabilities. The crews’ work-processes to handle the reconnaissance payload as well as to derive and include relevant information in the mission progress are expected to induce additional mental workload (MWL) during operation. First, this paper gives an overview of the assistance concept for sensor operation to minimize the crews’ MWL. Furthermore, an instance of a combined task- and resource model that describes MWL for several levels of automation in sensor guidance and payload sensor data evaluation is presented. Model parameters of human interaction for a holistic task- and activity set will be described. Finally, a method for demand parameter value determination from a dataset gained by an experimental campaign and results are presented.

Keywords

Human factors Mental workload Workload modelling MUM-T multi–UAV Mission sensors Levels of automation Assistant system 

References

  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, Heidelberg (2012)Google Scholar
  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. In: AIAA Guidance, Navigation, and Control Conference, pp. 1–24 (2011)Google Scholar
  3. 3.
    Honecker, F., Brand, Y., Schulte, A.: A task-centered approach for workload-adaptive pilot associate systems. In: Proceedings of the 32nd Conference of the European Association for Aviation Psychology – Thinking High AND Low: Cognition and Decision Making in Aviation (2016)Google Scholar
  4. 4.
    Onken, R.: Funktionsverteilung Pilot-Maschine: Umsetzung von Grundlagenforderungen im Cockpitassistenzsystem CASSY. In: Gärtner, K.-P. (ed.) DGLR-Bericht 94-01, vol. 94–1, pp. 73–89. Deutsche Gesellschaft für Luft- und Raumfahrt, Berlin (1994)Google Scholar
  5. 5.
    Baker, A.L., Keebler, J.R.: Factors affecting performance of human-automation teams. Adv. Hum. Factors Robot. Unmanned Syst. 499, 331–340 (2016)CrossRefGoogle Scholar
  6. 6.
    Ruf, C., Stütz, P.: Model-driven sensor operation assistance for a transport helicopter crew in manned-unmanned teaming missions: selecting the automation level by machine decision-making. Adv. Hum. Factors Robot. Unmanned Syst. 499, 253–265 (2016)CrossRefGoogle Scholar
  7. 7.
    Hellert, C., Stütz, P.: Performance prediction and selection of aerial perception functions during UAV missions. In: AeroConf 2017 (2017, in press)Google Scholar
  8. 8.
    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
  9. 9.
    Billings, C.E.: Aviation Automation: The Search for a Human-Centered Approach. Lawrence Erlbaum Associates, Mahwah (1997)Google Scholar
  10. 10.
    Wiener, E.L.: Human factors of advanced technology (glass cockpit) transport aircraft (1989)Google Scholar
  11. 11.
    Parasuraman, R., Riley, V.: Humans and automation: use, misuse, disuse, abuse. Hum. Factors J. Hum. Factors Ergon. Soc. 39(2), 230–253 (1997)CrossRefGoogle Scholar
  12. 12.
    Sheridan, T.B.: Adaptive automation, level of automation, allocation authority, supervisory control, and adaptive control: distinctions and modes of adaptation. IEEE Trans. Syst. Man Cybern. - Part A Syst. Hum. 41(4), 662–667 (2011)CrossRefGoogle Scholar
  13. 13.
    Endsley, M.: The application of human factors to the development of expert systems for advanced cockpits. In: Human Factors Society 31st Annual Meeting, pp. 1388–1392 (1987)Google Scholar
  14. 14.
    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 – Verstehen, Beschreiben und Gestalten Kognitiver (Technischer) Systeme (2015)Google Scholar
  15. 15.
    Wickens, C.D.: Multiple resources and performance prediction. Theor. Issues Ergon. Sci. 3(2), 159–177 (2002)CrossRefGoogle Scholar
  16. 16.
    Uhrmann, J., Schulte, A.: Concept, design and evaluation of cognitive task-based UAV guidance. J. Adv. Intell. Syst. 5(1) (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

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

Personalised recommendations