Advertisement

Tactical Reconnaissance Using Groups of Partly Autonomous UGVs

  • Peter Svenmarck
  • Dennis Andersson
  • Björn Lindahl
  • Johan Hedström
  • Patrik Lif
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5639)

Abstract

This paper investigates how one operator can control a multi-robot system for tactical reconnaissance using partly autonomous UGVs. Instead of controlling individual UGVs, the operator uses supervisory control to allocate partly autonomous UGVs into suitable groups and define areas for search. A state-of-the-art pursuit-evasion algorithm then performed the detailed control of available UGVs. The supervisory control was evaluated by allowing subjects to control either six or twelve UGVs for tactical reconnaissance along the route of advance for a convoy traveling through an urban environment with mobile threats. The results show that increasing the number of UGVs improve the subjects situation awareness, increase the number of threats that are detected, and reduce the number of hits on the convoy. More importantly, these benefits were achieved without any increase in mental workload. The results support the common belief in autonomous functions as an approach to reduce the operator-to-vehicle ratio in military applications.

Keywords

Supervisory Control UGV Operator-to-Vehicle Ratio Reconnaissance Multi-Robot Systems 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Barnes, M.J., Cosenzo, K.A., Mitchell, D.K., Chen, J.Y.C.: Human robot teams as soldier augmentation in future battlefields: an overview. In: Proceedings of HCI International - 11th International Conference on Human Computer Interaction, Las Vegas, NV (2005)Google Scholar
  2. 2.
    Svenmarck, P.: Principles of human-robot coordination for improved manned-unmanned teaming. FOI Memo 1507. FOI – Swedish Defence Research Agency, Linköping, Sweden (2005)Google Scholar
  3. 3.
    Lif, P.J., Jander, H., Borgvall, J.: Tactical evaluation of an unmanned ground vehicle during a MOUT exercise. In: Proceedings of the 50th Annual Meeting Human Factors and Ergonomics Society, pp. 2557–2561. HFES, San Francisco (2006)Google Scholar
  4. 4.
    Chadwick, R.: Multiple robots and display views: an urban search and rescue simulation. In: Proceedings of the 49th Annual Meeting Human Factors and Ergonomics Society, pp. 387–391. HFES, Orlando (2005)Google Scholar
  5. 5.
    Lif, P., Hedström, J., Svenmarck, P.: Operating multiple semi-autonomous UGVs: target detection, strategies, and instantaneous performance. In: Harris, D. (ed.) HCII 2007 and EPCE 2007. LNCS, vol. 4562, pp. 731–740. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Chadwick, R.A.: Operating multiple semi-autonomous robots: monitoring, responding, detecting. In: Proceedings of the Human Factors and Ergonomics Society 50th Annual Meeting, pp. 329–333. HFES, San Francisco (2006)Google Scholar
  7. 7.
    Trouvain, B., Schlick, C.M.: A comparative study of multimodal displays for multirobot supervisory control. In: Harris, D. (ed.) HCII 2007 and EPCE 2007. LNCS, vol. 4562, pp. 184–193. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  8. 8.
    Crandall, J.W., Cummings, M.L., Nehmez, C.E.: A predictive model for human-unmanned vehicle systems. Journal of Aerospace Computing, Information, and Communication (submitted, 2008)Google Scholar
  9. 9.
    Goodrich, M.A., Quigley, M., Cosenzo, K.: Task switching and multi-robot teams. In: Parker, L.E., Schneider, F.E., Schultz, A.C. (eds.) Multi-Robot Systems. From Swarms to Intelligent Automata. Proceedings from the 2005 International Workshop on Multi-Robot Systems, The Netherlands, vol. III. Springer, Heidelberg (2005)Google Scholar
  10. 10.
    Crandall, J.W., Goodrich, M.A., Olsen, D.R., Nielsen, C.W.: Validating human-robot interaction schemes in multi-tasking environments. IEEE Transactions on Systems Man, and Cybernetics, Part A: Systems and Humans 35, 438–449 (2005)CrossRefGoogle Scholar
  11. 11.
    Trouvain, B., Wolf, H.L., Schneider, F.E.: Impact of autonomy in multirobot systems on teleoperation performance. In: Schultz, A.C., Parker, L.E., Schneider, F.E. (eds.) Multi-Robot Systems: From Swarms to Intelligent Automata. Proceedings from the 2003 International Workshop on Multi-Robot Systems, vol. II. Kluwer, Dordrecht (2003)Google Scholar
  12. 12.
    Wang, J., Lewis, M.: Human control for cooperating robot teams. In: Proceeding of the Second ACM/IEEE International Conference on Human-Robot Interaction, HRI 2007. IEEE, Los Alamitos (2007)Google Scholar
  13. 13.
    Parasuraman, R., Galster, S., Squire, P., Furukawa, H., Miller, C.: A flexible delegation-type interface enhances system performance in human supervision of multiple robots: empirical studies with roboflag. IEEE Systems, Man and Cybernetics-Part A, Special Issue on Human-Robot Interactions 35, 481–493 (2005)CrossRefGoogle Scholar
  14. 14.
    Hollnagel, E.: Analysis of UAV scenarios using the extended control model. Final report NATO Working Group HFM-078-017, Uninhabited Military Vehicles (UMVs): Human Factors Issues in Augmenting the Force (2005)Google Scholar
  15. 15.
    Hollnagel, E., Woods, D.D.: Joint Cognitive Systems: Foundations of Cognitive Systems Engineering. CRC Press, Boca Raton (2005)CrossRefGoogle Scholar
  16. 16.
    Sarter, N.B., Woods, D.D., Billings, C.E.: Automation surprises. In: Salvendy, G. (ed.) Handbook of Human Factors and Ergonomics. Wiley, New York (1997)Google Scholar
  17. 17.
    Hollinger, G., Kehagias, A., Singh, S.: Probabilistic strategies for pursuit in cluttered environments with multiple robots. In: Proceedings of the 2007 IEEE International Conference on Robotics and Automation, ICRA 2007, Roma, Italy, pp. 3870–3876 (2007)Google Scholar
  18. 18.
    Hart, S.G., Staveland, L.E.: Development of NASA-TLX (Task Load Index): results of empirical and theoretical research. In: Hancock, P.A., Meshkati, N. (eds.) Human Mental Workload, pp. 139–183. Elsevier Science/North Holland, Amsterdam (1988)CrossRefGoogle Scholar
  19. 19.
    Taylor, R.M.: Situational awareness rating technique (SART): the development of a tool for aircrew systems design. In: Situational Awareness in Aerospace Operations. NATO-AGARD, Neuilly Sur Seine, France, AGARD-CP-478, pp. 3/1–3/17 (1990)Google Scholar
  20. 20.
    Muir, B.M.: Trust between humans and machines, and the design of decision aids. International Journal of Man-Machine Studies 27, 527–539 (1987)CrossRefGoogle Scholar
  21. 21.
    Muir, B.M.: Trust in automation: Part I. theoretical issues in the study of trust and human intervention in automated systems. Journal of Ergonomics 37, 1905–1922 (1994)CrossRefGoogle Scholar
  22. 22.
    Muir, B.M., Moray, N.: Trust in automation. Part II: experimental studies of trust and human intervention in a process control simulation. Ergonomics 39, 429–460 (1996)CrossRefGoogle Scholar
  23. 23.
    Wang, J., Wang, H., Lewis, M.: Assessing cooperation in human control of heterogeneous robots. In: Proceedings of the Third ACM/IEEE International Conference on Human-Robot Interaction, HRI 2008, pp. 9–16. IEEE, Los Alamitos (2008)Google Scholar
  24. 24.
    Crandall, J.W., Cummings, W.L.: A predictive model for human-unmanned vehicle systems. Report No. HAL 2008-5. MIT, Cambridge (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Peter Svenmarck
    • 1
  • Dennis Andersson
    • 1
  • Björn Lindahl
    • 1
  • Johan Hedström
    • 1
  • Patrik Lif
    • 1
  1. 1.Swedish Defence Research Agency (FOI)LinköpingSweden

Personalised recommendations