Abstract
Deep space habitation for long periods of time will generate exciting challenges for human-robot interaction and human-machine teaming more broadly. Human-robot interactions on Earth can take place in the form of a robotic vehicle that is remotely operated by a human (teleoperation), or supervised by a human (supervisory control). In these scenarios, the human plays a critical role in the success of the robot’s activity and they work cooperatively in complex environments. In space, there will be new challenges for both near real time and delayed supervisory control for complex tasks in a harsh environment, in terms of maintaining situational awareness and trust. Human-robot interaction will be a key part of a higher proportion of activities off Earth, due to the harsh environment. We therefore also consider challenges associated with human-robot interaction over extended periods of time for remote teams. We review recent results relevant to these problems, including the cognitive implications of human-robot teaming, and discuss the potential implications for crewing for long term deep space habitation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abbass, A., Tang, J., Amin, R., Ellejmi, M., & Kirby, S. (2014). Augmented cognition using real-time EEG-based adaptive strategies for air traffic control. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 58, No. 1, pp. 230–234). SAGE Publications.
Allison, R. S., Harris, L. R., Jenkin, M., Jasiobedzka, U., & Zacher, J. E. (2001). Tolerance of temporal delay in virtual environments. Proceedings IEEE Virtual Reality, 2001, 247–254. https://doi.org/10.1109/VR.2001.913793
Caldwell, S., Sweetser, P., O’Donnell, N., Knight, M. J., Aitchison, M., Gedeon, T., Johnson, D., Brereton, M., Gallagher, M., & Conroy, D. (2022). An agile new research framework for hybrid human-AI teaming: Trust, transparency, and transferability. ACM Transactions on Interactive Intelligent Systems, 12(3), 1–36. https://doi.org/10.1145/3514257
Comstock, J. R., & Amegard, R. J. (1992). The multi attribute task battery for human operator workload and strategic behaviour research. NASA Technical Memorandum No. 104174.
Cong, P., Zhou, J., Li, L., Cao, K., Wei, T., & Li, K. (2020). A survey of hierarchical energy optimization for Mobile edge computing: A perspective from end devices to the cloud. ACM Computing Surveys, 53(2), Article 38. https://doi.org/10.1145/3378935
Crawford, I. A. (2012). Dispelling the myth of robotic efficiency. Astronomy & Geophysics, 53(2), 2.22–2.26.
Crusan, J. C., Craig, D. A., & Herrmann, N. B. (2017). NASA’s deep space habitation strategy. IEEE Aerospace Conference, 2017, 1–11. https://doi.org/10.1109/AERO.2017.7943624
de Winter, J. C. F., Happee, R., Martens, M. H., & Stanton, N. A. (2014). Effects of adaptive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence. Transportation Research Part F: Traffic Psychology and Behaviour, 27, 196–217. https://doi.org/10.1016/j.trf.2014.06.016
Dehais, F., Peysakhovich, V., Scannella, S., Fongue, J., & Gateau, T. (2015). “Automation surprise” in aviation: Real-time solutions. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems – CHI ’15, 2525–2534. https://doi.org/10.1145/2702123.2702521
Di Nocera, F., Camilli, M., & Terenzi, M. (2007). A random glance at the flight deck: Pilots’ scanning strategies and the real-time assessment of mental workload. Journal of Cognitive Engineering and Decision Making, 1(3), 271–285. https://doi.org/10.1518/155534307X255627
Diez, M., Boehm-davis, D. A., Holt, R. W., Pinney, M. E., & Hansberger, J. T. (2001). Tracking pilot interactions with flight management systems through eye movements. Ohio State University.
Endsley, M. R. (2017). From here to autonomy: Lessons learned from human–automation research. Human Factors, 59(1), 5–27. https://doi.org/10.1177/0018720816681350
Howard, R. L. (2018). Justification of crew function and function capability for long duration deep space habitation. 2018 AIAA SPACE and Astronautics Forum and Exposition. 2018 AIAA SPACE and Astronautics Forum and Exposition, Orlando, FL. https://doi.org/10.2514/6.2018-5357
Matthews, G., Neubauer, C., Saxby, D. J., Wohleber, R. W., & Lin, J. (2019). Dangerous intersections? A review of studies of fatigue and distraction in the automated vehicle. Accident; Analysis and Prevention, 126, 85–94. https://doi.org/10.1016/j.aap.2018.04.004
Ma-Wyatt, A., Abbass, H., & Fidock, J. (2018a). Quantifying and predicting performance for human-autonomy teaming. International Conference on Science and Innovation for Land Power.
Ma-Wyatt, A., Johnstone, D., Fidock, J., & Hill, S. (2018b). Cognitive implications of HMIs for tele-operation and supervisory control of robotic ground vehicles. Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (pp. 189–190). https://doi.org/10.1145/3173386.3177043.
Ma-Wyatt, A., Fidock, J., & Hill, S. (2019). Cognitive implications of supervisory control and tele-operation of robotic ground vehicles. Final report for ITC-PAC RDECOM grant, funded by the US Department of Defence.
Neubauer, C., Matthews, G., Langheim, L., & Saxby, D. (2012). Fatigue and voluntary utilization of automation in simulated driving. Human Factors, 54(5), 734–746. https://doi.org/10.1177/0018720811423261
Poole, A., & Ball, L. J. (2006). Eye tracking in human-computer interaction and usability research: Current status and future prospects. 13. Encyclopedia of Human Computer Interaction (pp. 211–219). IGI Global.
Regis, N., Dehais, F., Tessier, C., & Gagnon, J.-F. (2012). Ocular metrics for detecting attentional tunnelling. 12. Proceedings HFES Europe Chapter Conference Toulouse.
Scannella, S., Peysakhovich, V., Ehrig, F., Lepron, E., & Dehais, F. (2018). Assessment of ocular and physiological metrics to discriminate flight phases in real light aircraft. Human Factors, 60(7), 922–935. https://doi.org/10.1177/0018720818787135
Schaefer, K. E., Chen, J. Y. C., Szalma, J. L., & Hancock, P. A. (2016). A meta-analysis of factors influencing the development of trust in automation: Implications for understanding autonomy in future systems. Human Factors, 58(3), 377–400. https://doi.org/10.1177/0018720816634228
Schmidt, J., Laarousi, R., Stolzmann, W., et al. (2018). Eye blink detection for different driver states in conditionally automated driving and manual driving using EOG and a driver camera. Behavior Research Methods, 50, 1088–1101. https://doi.org/10.3758/s13428-017-0928-0
Shen, S., & Neyens, D. M. (2017). Assessing drivers’ response during automated driver support system failures with non-driving tasks. Journal of Safety Research, 61, 149–155. https://doi.org/10.1016/j.jsr.2017.02.009
Solís-Marcos, I., Galvao-Carmona, A., & Kircher, K. (2017). Reduced attention allocation during short periods of partially automated driving: An event-related potentials study. Frontiers in Human Neuroscience, 11. https://doi.org/10.3389/fnhum.2017.00537
Svensson, Å. (n.d.). Analysis of work patterns as a foundation for human- automation communication in multiple remote towers. 10.
Ulutas, B. H., Özkan, N. F., & Michalski, R. (2020). Application of hidden Markov models to eye tracking data analysis of visual quality inspection operations. Central European Journal of Operations Research, 28(2), 761–777. https://doi.org/10.1007/s10100-019-00628-x
Waltemate, T., Senna, I., Hülsmann, F., Rohde, M., Kopp, S., Ernst, M., & Botsch, M. (2016). The impact of latency on perceptual judgments and motor performance in closed-loop interaction in virtual reality. Proceedings of the 22nd ACM Conference on Virtual Reality Software and Technology (pp. 27–35). https://doi.org/10.1145/2993369.2993381.
Wilson, G. F., Russell, C. A., Monnin, J. W., Estepp, J. R., & Christensen, J. C. (2010). How does day-to-day variability in psychophysiological data affect classifier accuracy? Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 54(3), 264–268. https://doi.org/10.1177/154193121005400317
Acknowledgements
The authors wish to thank members of the Andy Thomas Space Resource Centre Deep Space Habitation Group, and Jean-Philippe Diguet and Cedric Buche for helpful discussions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Ma-Wyatt, A., Fidock, J., O’Rielly, J., Long, H., Culton, J. (2023). The Challenges and Opportunities of Human-Robot Interaction for Deep Space Habitation. In: de Zwart, M., Henderson, S., Culton, J., Turnbull, D., Srivastava, A. (eds) Human Uses of Outer Space. Issues in Space. Springer, Singapore. https://doi.org/10.1007/978-981-19-9462-3_4
Download citation
DOI: https://doi.org/10.1007/978-981-19-9462-3_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-9461-6
Online ISBN: 978-981-19-9462-3
eBook Packages: Law and CriminologyLaw and Criminology (R0)