Abstract
Recent advances in autonomy have highlighted opportunities for tight coordination between humans and autonomous agents in many current and future applications. For operations involving cooperating humans and robots, autonomous teammates must have flexibility to respond to the inherently unpredictable behavior of their human counterparts. To investigate this issue in detail, this paper uses an unmanned aerial vehicle (UAV) simulation to evaluate flexible human performance models over traditional static modeling approaches for multi-agent task allocation and scheduling. Additional comparisons are drawn between adaptive human models, which are adjusted in real-time by the autonomous planner according to realized human performance, and adaptable human models, where the human operator is given sole authority over model adjustments. Results indicate that adaptive human performance models significantly increase total mission reward over both the baseline static modeling framework (p = 0.0012) as well as the adaptable modeling technique (p = 0.0028) for this system.
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References
Unmanned Systems Integrated Roadmap: FY2013-2038. Washington, DC, USA (2013)
Chen, J.Y.C., Barnes, M.J.: Human-agent teaming for multirobot control: a review of human factors issues. IEEE Trans. Human-Mach. Syst. 44, 13–29 (2014)
Kaber, D.B., Prinzel, L.J.: Adaptive and Adaptable Automation Design: A Critical Review of the Literature and Recommendations for Future Research (2006)
Alami, R., Clodic, A., Chatila, R., Lemaignan, S.: Reasoning about humans and its use in a cognitive control architecture for a collaborative robot. In: Human-Robot Interaction Workshop (2014)
Shah, J.A., Wiken, J., Williams, B.C., Breazeal, C.: Improved human-robot team performance using Chaski, a human-inspired plan execution system. In: 6th International Conference on Human-Robot Interaction, pp. 29–36 (2011)
Savla, K., Frazzoli, E.: A dynamical queue approach to intelligent task management for human operators. Proc. IEEE 100, 672–686 (2012)
Srivastava, V., Surana, A., Bullo, F.: Adaptive attention allocation in human-robot systems. In: American Control Conference (2012)
Kidwell, B., Calhoun, G.L., Ruff, H.A., Parasuraman, R.: Adaptable and adaptive automation for supervisory control of multiple autonomous vehicles. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 56, 428–432 (2012)
Clare, A.S., Cummings, M.L., How, J.P., Whitten, A.K., Toupet, O.: Operator object function guidance for a real-time unmanned vehicle scheduling algorithm. J. Aerosp. Comput. Inf. Commun. 9, 161–173 (2012)
Sauer, J., Kao, C.-S., Wastell, D.: A comparison of adaptive and adaptable automation under different levels of environmental stress. Ergonomics 55, 840–853 (2012)
Pew, R.W.: The speed-accuracy operating characteristic. Acta Psychol. (Amst) 30, 16–26 (1969)
Nehme, C.E.: Modeling Human Supervisory Control in Heterogeneous Unmanned Vehicle Systems (2009)
Yerkes, R.M., Dodson, J.D.: The relation of strength of stimulus to rapidity of habit-formation. J. Comp. Neurol. Psychol. 18, 459–482 (1908)
Shannon, C.J., Johnson, L.B., Jackson, K.F., How, J.P.: Adaptive mission planning for coupled human-robot teams. In: American Control Conference (2016)
Quigley, M., Gerkey, B., Conley, K., Faust, J., Foote, T., Leibs, J., Berger, E., Wheeler, R., Ng, A.: ROS: an open-source robot operating system. ICRA Work. 3 (2009)
Weisstein, E.W.: Bonferroni Correction (2004)
Gombolay, M.C., Gutierrez, R.A., Sturla, G.F., Shah, J.A.: Decision-making authority, team efficiency and human worker satisfaction in mixed human-robot teams. Robot. Sci. Syst. X. (2014)
Hoeting, J.A., Madigan, D., Raftery, A.E., Volinsky, C.T.: Bayesian model averaging: a tutorial. Stat. Sci. 14, 382–417 (1999)
Bertuccelli, L.F., Choi, H., Cho, P., How, J.P.: Real-time multi-UAV task assignment in dynamic and uncertain environments. In: AIAA Conference on Guidance, Navigation, and Control (2009)
Bishop, C.M.: Pattern Recognition. Machine Learning (2006)
Acknowledgments
The authors would like to thank Draper (Cambridge, MA, USA) for funding this research as well as Professor Julie Shah for her insight and guidance throughout this effort.
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Shannon, C.J., Horney, D.C., Jackson, K.F., How, J.P. (2017). Human-Autonomy Teaming Using Flexible Human Performance Models: An Initial Pilot Study. In: Savage-Knepshield, P., Chen, J. (eds) Advances in Human Factors in Robots and Unmanned Systems. Advances in Intelligent Systems and Computing, vol 499. Springer, Cham. https://doi.org/10.1007/978-3-319-41959-6_18
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DOI: https://doi.org/10.1007/978-3-319-41959-6_18
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