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MRComm: Multi-Robot Communication Testbed

  • Tsvetan Zhivkov
  • Eric SchneiderEmail author
  • Elizabeth SklarEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11650)

Abstract

This work demonstrates how dynamic robot behaviour that responds to different types of network disturbances can improve communication and mission performance in a Multi-Robot Team (MRT). A series of experiments are conducted which show how two different network perturbations (i.e. packet loss and signal loss) and two different network types (i.e. wireless local area network and ad-hoc network) impact communication. Performance is compared using two MRT behaviours: a baseline versus a novel dynamic behaviour that adapts to fluctuations in communication quality. Experiments are carried out on a known map with tasks assigned to a robot team at the start of a mission. During each experiment, a number of performance metrics are recorded. A novel dynamic Leader-Follower (LF) behaviour enables continuous communication through two key functions: the first reacts to the network type by using signal strength to determine if the robot team must commit to grouping together to maintain communication; and the second employs a special task status messaging function that guarantees a message is communicated successfully to the team members. The results presented in this work are significant for real-world multi-robot system applications that require continuous communication amongst team members.

Keywords

Multi-robot team Behaviour-based control Dynamic roles 

References

  1. 1.
    Al-Akkad, A., Raffelsberger, C., Boden, A., Ramirez, L., Zimmermann, A.: Tweeting when online is off? Opportunistically creating mobile ad-hoc networks in response to disrupted infrastructure. In: 11th International ISCRAM Conference Proceedings. Theme 13 - Social Media in Crisis Response and Management, May 2014Google Scholar
  2. 2.
    Andre, T., Neuhold, D., Bettstetter, C.: Coordinated multi-robot exploration: out of the box packages for ROS. In: Published in IEEE Globecom Workshops (GC Wkshps), Austin, TX, USA (2014)Google Scholar
  3. 3.
    Balch, T., Arkin, R.C.: Behaviour-based formation control for multirobot teams. IEEE Trans. Robot. Autom. 14, 926–939 (1998)CrossRefGoogle Scholar
  4. 4.
    Caccamo, S., Parasuraman, R., Freda, L., Gianni, M., Ögren, P.: RCAMP: a resilient communication-aware motion planner for mobile robots with autonomous repair of wireless connectivity. In: International Conference on Intelligent Robots and Systems (IROS) (2017)Google Scholar
  5. 5.
    Dąbrowski, A., Kozik, R., Maciaś, M.: Evaluation of ROS2 communication layer. ROSCon, Seoul, Korea (2016)Google Scholar
  6. 6.
    Fernandez, E., Foote, T., Woodall, W., Thomas, D.: Next-generation ROS: building on DDS. ROSCon, Chicago, US (2014)Google Scholar
  7. 7.
    Fox, D., Burgard, W., Dellaert, F., Thrun, S.: Monte carlo localization: efficient position estimation for mobile robots. In: Published in Proceedings of the Association for the Advancement of Artificial Intelligence (AAAI) (1999)Google Scholar
  8. 8.
    Gerkey, B.P., Mataríc, M.J.: A formal analysis and taxonomy of task allocation in multi-robot systems. Int. J. Robot. Res. 23(9), 939–954 (2004)CrossRefGoogle Scholar
  9. 9.
    Kashino, Z., Nejat, G., Benhabib, B.: A multi-robot sensor-delivery planning strategy for static-sensor networks. In: International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada (2017)Google Scholar
  10. 10.
    Landén, D., Heintz, F., Doherty, P.: Complex task allocation in mixed-initiative delegation: a UAV case study. In: Desai, N., Liu, A., Winikoff, M. (eds.) PRIMA 2010. LNCS (LNAI), vol. 7057, pp. 288–303. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-25920-3_20CrossRefGoogle Scholar
  11. 11.
    Lujak, M., et al.: Towards robots-assisted ambient intelligence. In: Belardinelli, F., Argente, E. (eds.) EUMAS/AT -2017. LNCS (LNAI), vol. 10767, pp. 490–497. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01713-2_34CrossRefGoogle Scholar
  12. 12.
    Murphy, R., et al.: Interacting with trapped victims using robots. In: International Conference on Technologies for Homeland Security, HST (2013)Google Scholar
  13. 13.
    Quigley, M., et al.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software (2009)Google Scholar
  14. 14.
    Schneider, E., Sklar, E.I., Parsons, S.: Mechanism selection for multi-robot task allocation. In: Gao, Y., Fallah, S., Jin, Y., Lekakou, C. (eds.) TAROS 2017. LNCS (LNAI), vol. 10454, pp. 421–435. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-64107-2_33CrossRefGoogle Scholar
  15. 15.
    Schneider, E., Sklar, E.I., Parsons, S.: Evaluating multi-robot teamwork in parameterised environments. In: Alboul, L., Damian, D., Aitken, J.M.M. (eds.) TAROS 2016. LNCS (LNAI), vol. 9716, pp. 301–313. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-40379-3_32CrossRefGoogle Scholar
  16. 16.
    Schneider, E., Sklar, E.I., Parsons, S., Özgelen, A.T.: Auction-based task allocation for multi-robot teams in dynamic environments. In: Dixon, C., Tuyls, K. (eds.) TAROS 2015. LNCS (LNAI), vol. 9287, pp. 246–257. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-22416-9_29CrossRefGoogle Scholar
  17. 17.
    Takahashi, T., Kitamura, Y., Miwa, H.: Organizing rescue agents using ad-hoc networks. In: Pérez, J., et al. (eds.) Highlights on Practical Applications of Agents and Multi-Agent Systems. Advances in Intelligent and Soft Computing, vol. 156. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-28762-6_17CrossRefGoogle Scholar
  18. 18.
    Witkowski, U., et al.: Ad-hoc network communication infrastructure for multi-robot systems in disaster scenarios. In: Proceedings of IARP/EURON Workshop on Robotics for Risky Interventions and Environmental Surveillance, Benicassim, Spain (2008)Google Scholar
  19. 19.
    Zadorozhny, V., Lewis, M.: Information fusion for USAR operations based on crowdsourcing. In: Proceedings of the 16th International Conference on Information Fusion, Istanbul, Turkey (2013)Google Scholar
  20. 20.
    Zhivkov, T., Schneider, E., Sklar, E.I.: Measuring the effects of communication quality on multi-robot team performance. In: Gao, Y., Fallah, S., Jin, Y., Lekakou, C. (eds.) TAROS 2017. LNCS (LNAI), vol. 10454, pp. 408–420. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-64107-2_32CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of InformaticsKing’s College LondonLondonUK

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