Advertisement

An Autonomic Model-Driven Architecture to Support Runtime Adaptation in Swarm Behavior

  • Mark AllisonEmail author
  • Melvin Robinson
  • Grant Rusin
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 70)

Abstract

The use of unmanned vehicles in swarms requires significant runtime adaptation within the managing software due to the unpredictability of the environment it operates. This is compounded by rapid context changes occurring within the software elevating its operational complexity to a magnitude that renders them infeasible for humans to effectively manage. Our approach to addressing this challenge is model-driven self-adaptation using autonomic methods. This work extends and refines ongoing work on an unmanned vehicle swarm platform based on probabilistic finite state machines as behavioral runtime models and the formation of subswarms in the context of communication constrained search. We present the architecture of our work in progress as a reflection mechanism controlling short and long-term adaptive behavior. We realize short-term behavior change by the continuous transformation of structural models at runtime. To validate the architecture’s autonomic properties, we provide a walkthrough of an indicative scenario pertaining to swarm resilience as proof of principle of the architecture’s ability to dynamically replan under element failure.

Keywords

Autonomic computing Swarm technology Model-driven architecture 

Notes

Acknowledgements

This work is supported by the University of Michigan -Flint Office of Sponsored Research.

References

  1. 1.
    Allison, M., Spradling, M., Knock, N.: Uav collaborative search using probabilistic finite state machines. In: International Command and Control Research and Technology Symposium—Knowledge Systems for Coalition Operations (2017)Google Scholar
  2. 2.
    Bruni, R., Corradini, A., Gadducci, F., Lafuente, A.L., Vandin, A.: A conceptual framework for adaptation. In: International Conference on Fundamental Approaches to Software Engineering, pp. 240–254. Springer (2012)Google Scholar
  3. 3.
    Cao, Y.U., Fukunaga, A.S., Kahng, A.: Cooperative mobile robotics: antecedents and directions. Autonomous robots 4(1), 7–27 (1997)CrossRefGoogle Scholar
  4. 4.
    Cheng, B.H., De Lemos, R., Giese, H., Inverardi, P., Magee, J., Andersson, J., Becker, B., Bencomo, N., Brun, Y., Cukic, B., et al.: Software engineering for self-adaptive systems: a research roadmap. In: Software Engineering for Self-adaptive Systems, pp. 1–26. Springer (2009)Google Scholar
  5. 5.
    Computing, A., et al.: An architectural blueprint for autonomic computing. In: IBM White Paper, vol. 31 (2006)Google Scholar
  6. 6.
    Fickas, S., Feather, M.S.: Requirements monitoring in dynamic environments. In: Proceedings of the Second IEEE International Symposium on Requirements Engineering, pp. 140–147. IEEE (1995)Google Scholar
  7. 7.
    France, R., Rumpe, B.: Model-driven development of complex software: a research roadmap. In: 2007 Future of Software Engineering, pp. 37–54. IEEE Computer Society (2007)Google Scholar
  8. 8.
    Garcia-Dominguez, A., Bencomo, N.: Non-human modelers: challenges and roadmap for reusable self-explanation. In: Federation of International Conferences on Software Technologies: Applications and Foundations, pp. 161–171. Springer (2017)Google Scholar
  9. 9.
    Harman, M., Jones, B.F.: Search-based software engineering. Inf. softw. Technol. 43(14), 833–839 (2001)Google Scholar
  10. 10.
    Horn, P.: Autonomic Computing: IBM\(\backslash \)’s Perspective on the State of Information Technology (2001)Google Scholar
  11. 11.
    Jouault, F., Kurtev, I.: Transforming models with ATL. In: International Conference on Model Driven Engineering Languages and Systems, pp. 128–138. Springer (2005)Google Scholar
  12. 12.
    Kephart, J.O., Chess, D.M.: The vision of autonomic computing. Computer 36(1), 41–50 (2003)MathSciNetCrossRefGoogle Scholar
  13. 13.
    McCune, R.R., Madey, G.R.: Swarm control of uavs for cooperative hunting with DDDAS. Proc. Comput. Sci. 18, 2537–2544 (2013)CrossRefGoogle Scholar
  14. 14.
    Şahin, E.: Swarm robotics: from sources of inspiration to domains of application. In: International Workshop on Swarm Robotics, pp. 10–20. Springer (2004)Google Scholar
  15. 15.
    Schmidt, D.C.: Model-driven engineering. Comput.-Comput. Soc. 39(2), 25 (2006)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Steinberg, D., Budinsky, F., Merks, E., Paternostro, M.: EMF: Eclipse Modeling Framework. Pearson Education (2008)Google Scholar
  17. 17.
    Wätzoldt, S., Giese, H.: Classifying distributed self-* systems based on runtime models and their coupling. In: Models@ run. time, pp. 11–20. Citeseer (2014)Google Scholar
  18. 18.
    Wirtschaftswissenschaftlichen, D., Kradolfer, M., Dittrich, D., Alonso, D.: A workflow metamodel supporting dynamic, reuse-based model evolution (2000)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of Michigan - FlintFlintUSA
  2. 2.University of Texas - TylerTylerUSA

Personalised recommendations