A Framework for Analyzing Adaptive Autonomous Aerial Vehicles

  • Ian A. Mason
  • Vivek NigamEmail author
  • Carolyn Talcott
  • Alisson Brito
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10729)


Unmanned aerial vehicles (UAVs), a.k.a. drones, are becoming increasingly popular due to great advancements in their control mechanisms and price reduction. UAVs are being used in applications such as package delivery, plantation and railroad track monitoring, where UAVs carry out tasks in an automated fashion. Devising how UAVs achieve a task is challenging as the environment where UAVs are deployed is normally unpredictable, for example, due to winds. Formal methods can help engineers to specify flight strategies and to evaluate how well UAVs are going to perform to achieve a task. This paper proposes a formal framework where engineers can raise the confidence in their UAV specification by using symbolic, simulation and statistical and model checking methods. Our framework is constructed over three main components: the behavior of UAVs and the environment are specified in a formal executable language; the UAV’s physical model is specified by a simulator; and statistical model checking algorithms are used for the analysis of system behaviors. We demonstrate the effectiveness of our framework by means of several scenarios involving multiple drones.



Nigam was partially supported by Capes and CNPq. This work has been partially developed under contracting of Diehl Aerospace GmbH and Airbus Defense GmbH. Talcott and Mason were partially supported by ONR grant N00014-15-1-2202. Nigam and Talcott were partially supported by Capes Science without Borders grant 88881.030357/2013-01.


  1. 1.
    Arduplane, arducopter, ardurover.
  2. 2.
    Ascens: Autonomic service-component ensembles.
  3. 3.
    Bae, K., Ölveczky, P.C., Feng, T.H., Lee, E.A., Tripakis, S.: Verifying hierarchical ptolemy II discrete-event models using real-time maude. Sci. Comput. Program. 77(12), 1235–1271 (2012)CrossRefzbMATHGoogle Scholar
  4. 4.
    Barros, J., Brito, A., Oliveira, T., Nigam, V.: A framework for the analysis of UAV strategies using co-simulation. In: SBESC (2016)Google Scholar
  5. 5.
    Bistarelli, S., Montanari, U., Rossi, F.: Semiring-based constraint satisfaction and optimization. J. ACM 44(2), 201–236 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Why BNSF railway is using drones to inspect thousands of miles of rail lines.
  7. 7.
    Clavel, M., Durán, F., Eker, S., Lincoln, P., Martí-Oliet, N., Meseguer, J., Talcott, C.: All About Maude: A High-Performance Logical Framework. Springer, Heidelberg (2007). zbMATHGoogle Scholar
  8. 8.
    Dantas, Y.G., Lemos, M.O.O., Fonseca, I.E., Nigam, V.: Formal specification and verification of a selective defense for TDoS attacks. In: Lucanu, D. (ed.) WRLA 2016. LNCS, vol. 9942, pp. 82–97. Springer, Cham (2016). CrossRefGoogle Scholar
  9. 9.
    Dantas, Y.G., Nigam, V., Fonseca, I.E.: A selective defense for application layer DDos attacks. In: JISIC (2014)Google Scholar
  10. 10.
    Das, J., Cross, G., Qu, A.M.C., Tokekar, P., Mulgaonkar, Y., Kumar, V.: Devices, systems, and methods for automated monitoring enabling precision agriculture. In: CASE (2015)Google Scholar
  11. 11.
  12. 12.
    Hölzl, M., Rauschmayer, A., Wirsing, M.: Engineering of software-intensive systems. In: Software-Intensive Systems and New Computing Paradigms (2008)Google Scholar
  13. 13.
    Hölzl, M., Wirsing, M.: Towards a system model for ensembles. In: Agha, G., Danvy, O., Meseguer, J. (eds.) Formal Modeling: Actors, Open Systems, Biological Systems. LNCS, vol. 7000, pp. 241–261. Springer, Heidelberg (2011). CrossRefGoogle Scholar
  14. 14.
    The JSBSim flight dynamics model.
  15. 15.
    Kanovich, M., Ban Kirigin, T., Nigam, V., Scedrov, A., Talcott, C.: Timed multiset rewriting and the verification of time-sensitive distributed systems. In: Fränzle, M., Markey, N. (eds.) FORMATS 2016. LNCS, vol. 9884, pp. 228–244. Springer, Cham (2016). CrossRefGoogle Scholar
  16. 16.
    Kernbach, S., Schmickl, T., Timmis, J.: Collective adaptive systems: challenges beyond evolvability. In: Fundamentals of Collective Adaptive Systems. European Commission (2009)Google Scholar
  17. 17.
    Networked cyber physical systems.
  18. 18.
    Kim, M., Stehr, M.-O., Kim, J., Ha, S.: An application framework for loosely coupled networked cyber-physical systems. In: EUC (2010)Google Scholar
  19. 19.
    Kim, M., Stehr, M.-O., Talcott, C., Dutt, N., Venkatasubramanian, N.: Combining formal verification with observed system execution behavior to tune system parameters. In: Raskin, J.-F., Thiagarajan, P.S. (eds.) FORMATS 2007. LNCS, vol. 4763, pp. 257–273. Springer, Heidelberg (2007). CrossRefGoogle Scholar
  20. 20.
    Kim, M., Stehr, M.-O., Talcott, C., Dutt, N., Venkatasubramanian, N.: A probabilistic formal analysis approach to cross layer optimization in distributed embedded systems. In: Bonsangue, M.M., Johnsen, E.B. (eds.) FMOODS 2007. LNCS, vol. 4468, pp. 285–300. Springer, Heidelberg (2007). CrossRefGoogle Scholar
  21. 21.
    Kim, M., Stehr, M.-O., Talcott, C., Dutt, N., Venkatasubramanian, N.: XTune: a formal methodology for cross-layer tuning of mobile embedded systems. Trans. Embed. Comput. Syst. (2011)Google Scholar
  22. 22.
  23. 23.
    Lassaigne, R., Peyronnet, S.: Probabilistic verification and approximation schemes. Ann. Pure Appl. Log. 152(1–3), 122–131 (2008)CrossRefzbMATHGoogle Scholar
  24. 24.
    Liquid robotics.
  25. 25.
    Loreti, M., Hillston, J.: Modelling and analysis of collective adaptive systems with CARMA and its tools. In: Bernardo, M., De Nicola, R., Hillston, J. (eds.) SFM 2016. LNCS, vol. 9700, pp. 83–119. Springer, Cham (2016). Google Scholar
  26. 26.
    Mason, I.A., Talcott, C.L.: IOP: the interoperability platform and IMaude: an interactive extension of maude. In: WRLA 2004 (2004)Google Scholar
  27. 27.
    MAVLink micro air vehicle marshalling/communication library.
  28. 28.
    Nigam, V., Talcott, C., Aires Urquiza, A.: Towards the automated verification of cyber-physical security protocols: bounding the number of timed intruders. In: Askoxylakis, I., Ioannidis, S., Katsikas, S., Meadows, C. (eds.) ESORICS 2016. LNCS, vol. 9879, pp. 450–470. Springer, Cham (2016). CrossRefGoogle Scholar
  29. 29.
    Ölveczky, P.C., Meseguer, J.: Abstraction and completeness for real-time maude. In: WRLA (2007)Google Scholar
  30. 30.
    Ölveczky, P.C., Meseguer, J.: Semantics and pragmatics of real-time maude. High.-Order Symb. Comput. 20(1–2), 161–196 (2007)CrossRefzbMATHGoogle Scholar
  31. 31.
  32. 32.
    Sen, K., Viswanathan, M., Agha, G.A.: VESTA: a statistical model-checker and analyzer for probabilistic systems. In: QEST (2005)Google Scholar
  33. 33.
  34. 34.
    Talcott, C., Nigam, V., Arbab, F., Kappé, T.: Formal specification and analysis of robust adaptive distributed cyber-physical systems. In: Bernardo, M., De Nicola, R., Hillston, J. (eds.) SFM 2016. LNCS, vol. 9700, pp. 1–35. Springer, Cham (2016). Google Scholar
  35. 35.
    Talcott, C., Arbab, F., Yadav, M.: Soft agents: exploring soft constraints to model robust adaptive distributed cyber-physical agent systems. In: De Nicola, R., Hennicker, R. (eds.) Software, Services, and Systems. LNCS, vol. 8950, pp. 273–290. Springer, Cham (2015). CrossRefGoogle Scholar
  36. 36.
    Drone swarms: The buzz of the future.

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Ian A. Mason
    • 1
  • Vivek Nigam
    • 2
    • 3
    Email author
  • Carolyn Talcott
    • 1
  • Alisson Brito
    • 2
  1. 1.SRI InternationalMenlo ParkUSA
  2. 2.Federal University of ParaíbaJoão PessoaBrazil
  3. 3.fortissMunichGermany

Personalised recommendations