Robot Behavior for Enhanced Human Performance and Workload

  • Grace Teo
  • Lauren Reinerman-Jones
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8525)

Abstract

Advancements in technology in the field of robotics have made it necessary to determine integration and use for these in civilian tasks and military missions. Currently, literature is limited on robot employment in tasks and missions, and few taxonomies exist that guide understanding of robot functionality.As robots acquire more capabilities and functions, they will likely be working more closely with humans in human-robot teams. In order to better utilize and design robots that enhance performance in such teams, a better understanding of what robots can do and the impact of these behaviors on the human operator/teammate is needed.

Keywords

Human-robot teaming Robot behavior Performance Workload 

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References

  1. 1.
    ABB Australia. ABB robot keeps trailer make competitive with 60% productivity increase (2010), http://www.abbaustralia.com.au/cawp/seitp202/88d442f2225b9957c1257766003896be.aspx (retrieved January 21, 2014)
  2. 2.
    Aki, L.: 7 Must-have domestic robots – R2D2 & C3PO eat your hearts out (2012), http://www.amog.com/tech/153161-domestic-robots/ (retrieved January 21, 2014)
  3. 3.
    Belbin, M.: Management Teams. Heinemann, London (1981)Google Scholar
  4. 4.
    Belbin, M.: Method, reliability & validity, statistics and research: A comprehensive review of Belbin’s team roles (2013), http://www.belbin.com/content/page/5596/BELBINuk-2013-A%20Comprehensive%20Review.pdf (retrieved January 21, 2013)
  5. 5.
    Billings, C.E.: Aviation automation: The search for a human centered approach. Erlbaum, Mahwah (1997)Google Scholar
  6. 6.
    Bubblews, Entertainment Robots (2013), http://www.bubblews.com/news/552216-entertainment-robots (retrieved January 21, 2014)
  7. 7.
    Carmody, M.A., Gluckman, J.P.: Task specific effects of automation and automation failure on performance, workload and situational awareness. In: Jensen, R.S., Neumeister, D. (eds.) Proceedings of the 7th International Symposium on Aviation Psychology, pp. 167–171. Department of Aviation, The Ohio State University, Columbus (1993)Google Scholar
  8. 8.
    Current use of military robots (2014), http://www.armyofrobots.com/current-use-military.html (retrieved January 21, 2014)
  9. 9.
    Çalişir, F., Çalişir, F.: The relation of interface usability characteristics, perceived usefulness, and perceived ease of use to end-user satisfaction with enterprise resource planning (ERP) systems. Computers in Human Behavior 20(4), 505–515 (2004)CrossRefGoogle Scholar
  10. 10.
    da Vinci Surgery, Changing the experience of surgery (2013), http://www.davincisurgery.com/ (retrieved January 21, 2014)
  11. 11.
    DRC, DARPA Robotics Challenge 2013 (2013), http://www.theroboticschallenge.org/about (retrieved January 10, 2013)
  12. 12.
    Endsley, M.R.: The application of human factors to the development of expert systems for advanced cockpits. In: Proceedings of the Human Factors Society 31st Annual Meeting, pp. 1388–1392. Human Factors and Ergonomics Society, Santa Monica (1987)Google Scholar
  13. 13.
    Endsley, M.R., Kiris, E.O.: The out-of-the-loop performance problem and level of control in automation. Human Factors 37, 381–394 (1995)CrossRefGoogle Scholar
  14. 14.
    Guizzo, E., Ackerman, E.: How Rethink Robotics built its new Baxter robot worker. IEEE Spectrum, http://spectrum.ieee.org/robotics/industrial-robots/rethink-robotics-baxter-robot-factory-worker (retrieved January 21, 2014)
  15. 15.
    Hilburn, B., Jorna, P.G., Byrne, E.A., Parasuraman, R.: The effect of adaptive air traffic control (ATC) decision aiding on controller mental workload. In: Mouloua, M., Koonce, J. (eds.) Human-automation interaction: Research and practice, pp. 84–91. Erlbaum Associates, Mahwah (1997)Google Scholar
  16. 16.
    House, J.S.: Work Stress and Social Support. Addison-Wesley, Reading (1981)Google Scholar
  17. 17.
    Kessel, C.J., Wickens, C.D.: The transfer of failure-detection skills between monitoring and controlling dynamic systems. Human Factors 24, 49–60 (1982)Google Scholar
  18. 18.
    Kuka, Scara robots (2013), http://www.kuka-robotics.com/usa/en/products/industrial_robots/special/scara_robots/ (retrieved January 21, 2014)
  19. 19.
    Lamb, R.: How have robots changed manufacturing? (2010), http://science.howstuffworks.com/robots-changed-manufacturing.htm (retrieved January 21, 2014)
  20. 20.
    Liszewski, A.: Magnetic microbots perform eye surgery without a single incision (2013), http://gizmodo.com/magnetic-microbots-perform-eye-surgery-without-a-single-598784256 (retrieved January, 2014)
  21. 21.
    Martel, S.: Magnetic microbots to fight cancer. IEEE Spectrum (2012), http://spectrum.ieee.org/robotics/medical-robots/magnetic-microbots-to-fight-cancer (retrieved January 21, 2014)
  22. 22.
    McNickle, M.: 10 medical robots that could change healthcare. Information Week (2012)http://www.informationweek.com/mobile/10-medical-robots-that-could-change-healthcare/d/d-id/1107696 (retrieved)
  23. 23.
    Norman, D.: Cognitive Engineering. In: Norman, D., Draper, S. (eds.) User-Centered Design: New Perspectives on Human-Computer Interaction, pp. 31–62. Erlbaum Associates, Hillsdale (1986)Google Scholar
  24. 24.
    Parasuraman, R., Riley, V.: Humans and automation: use, misuse, disuse and abuse. Human Factors 39, 230–253 (1997)CrossRefGoogle Scholar
  25. 25.
    Parasuraman, R., Mouloua, M., Molloy, R.: Effects of adaptive task allocation on monitoring of automated systems. Human Factors 38, 665–679 (1996)CrossRefGoogle Scholar
  26. 26.
    Parasuraman, R., Mouloua, M., Molloy, R., Hilburn, B.: Adaptive function allocation reduces performance costs of static automation. In: Jensen, R.S., Neumeister, D. (eds.) Proceedings of the 7th International Symposium on Aviation Psychology, pp. 178–185. Department of Aviation, The Ohio State University, Columbus (1993)Google Scholar
  27. 27.
    Parasuraman, R., Sheridan, T., Wickens, C.: Model for types and levels of human interaction with automation (English). IEEE Transactions on Systems, Man, and Cybernetics. Part A. Systems and Humans 30(3), 286–297 (2000)CrossRefGoogle Scholar
  28. 28.
    Prewett, M.S., Johnson, R.C., Saboe, K.N., Elliott, L.R., Coovert, M.D.: Managing workload in human–robot interaction: A review of empirical studies. Computers in Human Behavior (2010), doi:10.1016/j.chb.2010.03.010Google Scholar
  29. 29.
    R & D, National Robotics Initiative invests $38 million in next-generation robotics (2013), http://www.rdmag.com/news/2013/10/national-robotics-initiative-invests-38-million-next-generation-robotics (retrieved 10 January 2013)
  30. 30.
    Rinaldo, K.: Paparazzi Bot, http://www.paparazzibot.com/ (retrieved January 21, 2014)
  31. 31.
    Rouse, W.B.: Human–computer interaction in multi-task situations. IEEE Transactions on Systems, Man and Cybernetics 7, 384–392 (1977)CrossRefMATHMathSciNetGoogle Scholar
  32. 32.
    Rouse, W.B.: Adaptive aiding for human/computer control. Human Factors 30, 431–438 (1988)Google Scholar
  33. 33.
    Save, L., Feuerberg, B.: Designing human-automation interaction: a new level of automation taxonomy. In: de Waard, D., Brookhuis, K., Dehais, F., Weikert, C., Rottger, S., Manzey, D., Biede, S., Reuzeau, F., Terrier, P. (eds.) Human Factors: a view from an integrative perspective. Proceedings HFES Europe Chapter Conference Toulouse, France (2012)Google Scholar
  34. 34.
    Scholtz, J.J.: Theory and evaluation of human robot interactions. In: Proceedings of the 36th Hawaii International Conference on System Science (2003), doi:10.1109/HICSS.2003.1174284Google Scholar
  35. 35.
    Sheridan, T.B.: Telerobotics, Automation, and Human Supervisory Control. MIT Press, Cambridge (1992)Google Scholar
  36. 36.
    Sheridan, T.B., Verplanck, W.L.: Human and computer control of undersea teleoperators. Cambridge University Press, Cambridge (1978)Google Scholar
  37. 37.
    Swan Robotics, Domestic robots (2014), http://www.swanrobotics.com/Domestic%20robots (retrieved January 21, 2014)
  38. 38.
    Taylor, G.S., Reinerman-Jones, L.E., Szalma, J.L., Mouloua, M., Hancock, P.A.: What to automate: Addressing the multidimensionality of cognitive resources through system design. Journal of Cognitive Engineering and Decision Making 7(4), 311–329 (2013)CrossRefGoogle Scholar
  39. 39.
    West, D.: Intel Museum tourist attraction (2008), https://suite101.com/a/intel-museum-sightseeing-attraction-a85734 (retrieved January 21, 2014)
  40. 40.
    Yanco, H.A., Drury, J.J.: A Taxonomy for Human-Robot Interaction. AAAI Technical report FS-02-03 (2002)Google Scholar
  41. 41.
    Yanco, H.A., Drury, J.J.: Classifying Human-Robot Interaction: An Updated Taxonomy. In: Proceedings of The 2004 IEEE International Conference on Systems, Man & Cybernetics, The Hague, The Netherlands, pp. 2841–2846 (2004)Google Scholar
  42. 42.
    Yeh, M., Wickens, C.D.: Attentional filtering in the design of electronic map displays: A comparison of color-coding, intensity coding, and decluttering techniques. Human Factors 43, 543–562 (2001)CrossRefGoogle Scholar
  43. 43.
    Young, L.R.A.: On adaptive manual control. Ergonomics 12, 635–657 (1969)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Grace Teo
    • 1
  • Lauren Reinerman-Jones
    • 1
  1. 1.Institute for Simulation and TrainingUniversity of Central FloridaOrlandoUSA

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