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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
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)
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)
Cao, Y.U., Fukunaga, A.S., Kahng, A.: Cooperative mobile robotics: antecedents and directions. Autonomous robots 4(1), 7–27 (1997)
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)
Computing, A., et al.: An architectural blueprint for autonomic computing. In: IBM White Paper, vol. 31 (2006)
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)
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)
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)
Harman, M., Jones, B.F.: Search-based software engineering. Inf. softw. Technol. 43(14), 833–839 (2001)
Horn, P.: Autonomic Computing: IBM\(\backslash \)’s Perspective on the State of Information Technology (2001)
Jouault, F., Kurtev, I.: Transforming models with ATL. In: International Conference on Model Driven Engineering Languages and Systems, pp. 128–138. Springer (2005)
Kephart, J.O., Chess, D.M.: The vision of autonomic computing. Computer 36(1), 41–50 (2003)
McCune, R.R., Madey, G.R.: Swarm control of uavs for cooperative hunting with DDDAS. Proc. Comput. Sci. 18, 2537–2544 (2013)
Şahin, E.: Swarm robotics: from sources of inspiration to domains of application. In: International Workshop on Swarm Robotics, pp. 10–20. Springer (2004)
Schmidt, D.C.: Model-driven engineering. Comput.-Comput. Soc. 39(2), 25 (2006)
Steinberg, D., Budinsky, F., Merks, E., Paternostro, M.: EMF: Eclipse Modeling Framework. Pearson Education (2008)
Wätzoldt, S., Giese, H.: Classifying distributed self-* systems based on runtime models and their coupling. In: Models@ run. time, pp. 11–20. Citeseer (2014)
Wirtschaftswissenschaftlichen, D., Kradolfer, M., Dittrich, D., Alonso, D.: A workflow metamodel supporting dynamic, reuse-based model evolution (2000)
Acknowledgements
This work is supported by the University of Michigan -Flint Office of Sponsored Research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Allison, M., Robinson, M., Rusin, G. (2020). An Autonomic Model-Driven Architecture to Support Runtime Adaptation in Swarm Behavior. In: Arai, K., Bhatia, R. (eds) Advances in Information and Communication. FICC 2019. Lecture Notes in Networks and Systems, vol 70. Springer, Cham. https://doi.org/10.1007/978-3-030-12385-7_32
Download citation
DOI: https://doi.org/10.1007/978-3-030-12385-7_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-12384-0
Online ISBN: 978-3-030-12385-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)