Assessing Coordination Demand in Cooperating Robots

  • Michael Lewis
  • Jijun Wang


Controlling multiple robots substantially increases the complexity of the operator’s task because attention must constantly be shifted among robots in order to maintain situation awareness (SA) and exert control. In the simplest case an operator controls multiple independent robots interacting with each as needed. Control performance at such tasks can be characterized by the average demand of each robot on human attention. In this Chapter, we present several approaches to measuring, coordination demand, CD, the added difficulty posed by having to coordinate as well as operate multiple robots. Our initial experiment compares “equivalent” conditions with and without coordination. Two subsequent experiments attempt to manipulate and measure coordination demand directly using an extension of the Neglect Tolerance model.


Multiple Robot Participant Comparison Multirobot System Inspector Robot Robotic Team 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported in part by the Air Force Office of Scientific Research under Grant FA9550-07-1-0039.


  1. 1.
    Balakirsky, S., Carpin, S., Kleiner, A., Lewis, M., Visser, A., Wang, J., and Zipara, V. (2007). Toward hetereogeneous robot teams for disaster mitigation: Results and performance metrics from RoboCup Rescue, Journal of Field Robotics, 24(11–12): 943–967.CrossRefGoogle Scholar
  2. 2.
    Carpin, S., Wang, J., Lewis, M., Birk, A., and Jacoff, A. (2005). High fidelity tools for rescue robotics: Results and perspectives, Robocup 2005 Symposium.Google Scholar
  3. 3.
    Carpin, S., Stoyanov, T., Nevatia, Y., Lewis, M., and Wang, J. (2006a). Quantitative assessments of USARSim accuracy. Proceedings of PerMIS 2006.Google Scholar
  4. 4.
    Carpin, S., Lewis, M., Wang, J., Balakirsky, S., and Scrapper, C. (2006b). Bridging the gap between simulation and reality in urban search and rescue. Robocup 2006: Robot Soccer World Cup X, Lecture Notes in Artificial Intelligence, Springer, Berlin.Google Scholar
  5. 5.
    Crandall, J., Goodrich, M., Olsen, D., and Nielsen, C. (2005). Validating human-robot interaction schemes in multitasking environments. IEEE Transactions on Systems, Man, and Cybernetics, Part A, 35(4): 438–449.CrossRefGoogle Scholar
  6. 6.
    Cummings, M., Nehme, C., and Crandall, J. (2007). Predicting Operator Capacity for Supervisory Control of Multiple UAVs. In J. S. Chahl, L. C. Jain, A. Mizutani, and M. Sato-Ilic (Eds.) Innovations in Intelligent Machines vol. 70, Studies in Computational Intelligence, Springer, Berlin.Google Scholar
  7. 7.
    Gerkey, B. and Mataric, M. (2004). A formal framework for the study of task allocation in multi-robot systems. International Journal of Robotics Research, 23(9): 939–954.CrossRefGoogle Scholar
  8. 8.
    Humphrey, C., C. Henk, C., G. Sewell, G., B. Williams, B., and Adams, J. (2007). Assessing the Scalability of a Multiple Robot Interface. Proceedings of the 2nd ACM/IEEE International Conference on Human-Robotic Interaction, ACM, New York, NY.Google Scholar
  9. 9.
    Jacoff, A., Messina, E., and Evans, J. (2001, September). Experiences in deploying test arenas for autonomous mobile robots. In Proceedings of the 2001 Performance Metrics for Intelligent Systems (PerMIS) Workshop, Mexico City, Mexico.Google Scholar
  10. 10.
    Lewis, M., Hughes, S., Wang, J., Koes, M., and Carpin, S. (2005). Validating USARsim for use in HRI research. Proceedings of the 49th Annual Meeting of the Human Factors and Ergonomics Society, Orlando, FL, pp. 457–461.Google Scholar
  11. 11.
    Loizou, S.,Tanner, H., Kumar, V., and Kyriakopoulos, K. (2003). Closed Loop Navigation for Mobile Agents in Dynamic Environments. IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China.Google Scholar
  12. 12.
    Pepper, C., Balakirsky, S., and Scrapper, C. (2007). Robot Simulation Physics Validation, Proceedings of PerMIS’07.Google Scholar
  13. 13.
    Scerri, P., et al. (2004). Coordinating large groups of wide area search munitions. In D. Grundel, R. Murphey, and P. Pandalos (Ed.) Recent Developments in Cooperative Control and Optimization, World Scientific, Singapore, pp. 451–480.Google Scholar
  14. 14.
    Schurr, N., Marecki, J., Tambe, M., Scerri, P., Kasinadhuni, N., and Lewis, J. (2005). The future of disaster response: Humans working with multiagent teams using DEFACTO. In AAAI Spring Symposium on AI Technologies for Homeland Security.Google Scholar
  15. 15.
    Steinfeld, A., Fong, T., Kaber, D., Lewis, M., Scholtz, J., Schultz, A., and Goodrich, M. (2006, March) Common Metrics for Human-Robot Interaction, 2006 Human-Robot Interaction Conference, ACM, New York, NY.Google Scholar
  16. 16.
    Taylor, B., Balakirsky, S., Messina, E., and Quinn, R. (2007). Design and Validation of a Whegs Robot in USARSim, Proceedings of PerMIS’07, ACM, New York, NY.Google Scholar
  17. 17.
    Velagapudi, P., Scerri, P., Sycara, K., Wang, H., Lewis, M., and Wang, J. (2008). Scaling effects in multi-robot control. 2008 International Conference on Intelligent Robots and Systems (IROS08), Nice, France.Google Scholar
  18. 18.
    Wang, J. (2007). Human Control of Cooperating Robots, dissertation, University of Pittsburgh, (accessed 7/22/2008).
  19. 19.
    Wang, J. and Lewis, M. (2007). Human control of cooperating robot teams. 2007 Human-Robot Interaction Conference, ACM, New York, NY.Google Scholar
  20. 20.
    Zaratti, M., Fratarcangeli, M., and Iocchi, L. (2006). A 3D Simulator of Multiple Legged Robots based on USARSim. Robocup 2006: Robot Soccer World Cup X, Springer, LNAI.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.School of Information Sciences, University of PittsburghPittsburghUSA
  2. 2.Quantum Leap Innovations Inc.NewarkUSA

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