Skip to main content

The Challenges and Opportunities of Human-Robot Interaction for Deep Space Habitation

  • Chapter
  • First Online:
Human Uses of Outer Space

Part of the book series: Issues in Space ((IS))

  • 355 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

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.

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Crawford, I. A. (2012). Dispelling the myth of robotic efficiency. Astronomy & Geophysics, 53(2), 2.22–2.26.

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Regis, N., Dehais, F., Tessier, C., & Gagnon, J.-F. (2012). Ocular metrics for detecting attentional tunnelling. 12. Proceedings HFES Europe Chapter Conference Toulouse.

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

Download references

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

Authors

Corresponding author

Correspondence to Anna Ma-Wyatt .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

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)

Publish with us

Policies and ethics