Abstract
Large complex systems such as systems of systems are difficult to analyze. They are especially difficult to formally verify primarily due to the state-space explosion. This paper addresses this issue and presents a comprehensive approach on a case study of a public domain multiple-unmanned aerial vehicle planning and search algorithm. The studied model contains both high-level swarm logic and low-level non-deterministic/probabilistic communication routing and error details, and its complexity exceeds our formal verification tool’s capability. Our process is generic and performed with two kinds of methods (formal method and traditional stochastic methods) together “co-picturing” the system’s performance. We introduce algorithms to obtain more information about the system’s behaviors, than either of these two methods applied alone. It utilizes off-the-shelf tools and minimizes additional programming development. Large amount of simulations and statistical analyses together with formal verification are performed to demonstrate the approach’s feasibility, and useful experiences are shown through the included extensive case study.
Brian Murray performed this work when employed by United Technologies Research Center.
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References
Gaudiano P, Shargel B, Bonabeau E, Clough BT (2003) Swarm intelligence: a new C2 paradigm with an application to control of Swarms of UAVs. In: Eighth ICCRTS Command and Control Research and Technology Symposium
Dynasoft project. U.S. Army contract No. W911QX-10-C-0073
Zhu H et al (2016) Exploring complex system analysis and verification. In: IEEE International Conference on Automation Science and Engineering (CASE)
Sirigineedi G, Tsourdos A, Zbikowski R, White BA (2009) Modeling and verification of multiple UAV mission using SMV. In: Workshop on Formal Methods for Aerospace (FMA)
Sirigineedi G, Tsourdos A, White BA, Żbikow R (2011) Kripke modelling and verification of temporal specifications of a multiple UAV system. Annals of Mathematics and Artificial Intelligence
Saberi AK, Groote JF, Keshishzadeh S (2013) Analysis of path planning algorithms: a formal verification-based approach. Advances in Artificial Life
Maxa JA (2015) Model-driven approach to design a secure routing protocol for UAV Adhoc networks. In: EDSYS, 15ème Congrès des doctorants
Konur S, Dixon C, Fisher M (2012) Analysing robot swarm behaviour via probabilistic model checking. Robotics and Autonomous Systems
Massink M et al (2012) Analysing robot swarm decision-making with Bio-PEPA. Swarm Intelligence
Saha I, et al (2014) Automated composition of motion primitives for multi-robot systems from safe LTL specifications. In: IEEE International Conference on Intelligent Robots and Systems (IROS)
Chaki S, Giampapa JA (2013) Probabilistic verification of coordinated multi-robot missions. Model Checking Software
Lahijanian M, Andersson SB, Belta C (2012) Temporal logic motion planning and control with probabilistic satisfaction guarantees. IEEE Transactions on Robotics
Belta C (2011) Abstractions for planning and control of robotic swarms. Bio-Inspired Computing and Networking
Kress-Gazit H, Fainekos GE, Pappas GJ (2007) Where’s Waldo? Sensor-based temporal logic motion planning. In: IEEE International Conference on Robotics and Automation
Courcoubetis C, Yannakakis M (1988) Verifying temporal properties of finite-state probabilistic programs. In: IEEE Annual Symposium on Foundations of Computer Science
Ladowski R (2008) A novel communication protocol using geographic routing for swarming UAVs performing a search mission. Thesis, Air Force Institute of Technology
Pack DJ, Mullins BE (2003) Toward finding an Universal search algorithm for Swarm Robots. In: IEEE International Conference on Intelligent Robots and Systems
AIMdyn. [Online]. Available: http://www.aimdyn.com
Sen K, Viswanathan M, Agha G (2004) Statistical model checking of black-box probabilistic systems. In: Computer aided verification
Younes HL (2006) Error control for probabilistic model checking. Verification, model checking, and abstract interpretation
Kim M et al (2007) A probabilistic formal analysis approach to cross layer optimization in distributed embedded systems. In: Formal methods for open object-based distributed systems
Clarke E et al (2003) Counterexample-guided abstraction refinement for symbolic model checking. JACM 50(5):752–794
Hermanns H, Wachter B, Zhang L (2008) Probabilistic cegar. In: Computer aided verification
Karp BN (2000) Geographic routing for wireless networks. Ph.D. Thesis, Harvard University
Moulin M, Gluhovsky L, Bendersky E (2003) Formal verification of Maneuvering target tracking. In: AIAA Conference on Guidance, Navigation and Control
Acknowledgments
This material is based upon work supported by the US Army, Research, Development and Engineering Command (RDECOM) Contracting Center under Contract No. W911QX-10-C-0073. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the US Army, RDECOM Contracting Center. We would also like to thank Teems Lovett, Andrew Babel and Nick Soldner for their feedbacks on the English language.
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Zhu, H., Moulin, M., Murray, B., Fonoberov, V., Mezic, I. (2018). System Analysis and Verification: A Comprehensive Approach and Case Study. In: Madni, A., Boehm, B., Ghanem, R., Erwin, D., Wheaton, M. (eds) Disciplinary Convergence in Systems Engineering Research. Springer, Cham. https://doi.org/10.1007/978-3-319-62217-0_16
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DOI: https://doi.org/10.1007/978-3-319-62217-0_16
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