Autonomous Robots

, Volume 40, Issue 8, pp 1363–1378 | Cite as

Persistent surveillance for unmanned aerial vehicles subject to charging and temporal logic constraints

  • Kevin Leahy
  • Dingjiang Zhou
  • Cristian-Ioan Vasile
  • Konstantinos Oikonomopoulos
  • Mac Schwager
  • Calin Belta


In this work, we present a novel method for automating persistent surveillance missions involving multiple vehicles. Automata-based techniques are used to generate collision-free motion plans for a team of vehicles to satisfy a temporal logic specification. Vector fields are created for use with a differential flatness-based controller, allowing vehicle flight and deployment to be fully automated according to the motion plans. The use of charging platforms with the vehicles allows for truly persistent missions. Experiments were performed with two quadrotors for two different missions over 50 runs each to validate the theoretical results.


Persistent monitoring Multi-robot systems Aerial robotics Formal methods 



This work was supported in part by NSF Grant Numbers CNS-1035588, NRI-1426907 and CMMI-1400167 and ONR Grant Numbers N00014-12-1-1000, MURI N00014-10-10952 and MURI N00014-09-1051.


  1. Aydin Gol, E., & Belta, C. (2013). Time-constrained temporal logic control of multi-affine systems. Nonlinear Analysis: Hybrid Systems, 10, 21–33.MathSciNetzbMATHGoogle Scholar
  2. Baier, C., & Katoen, J. (2008). Principles of model checking. Cambridge: MIT Press.zbMATHGoogle Scholar
  3. Belta, C., & Habets, L. C. G. J. M. (2006). Controlling a class of nonlinear systems on rectangles. IEEE Transactions on Automatic Control, 51(11), 1749–1759.MathSciNetCrossRefGoogle Scholar
  4. Dantzig, G. B., & Ramser, J. H. (1959). The truck dispatching problem. Management Science, 6(1), 80–91.MathSciNetCrossRefzbMATHGoogle Scholar
  5. Jha, S., Clarke, E., Langmead, C., Legay, A., Platzer, A., & Zuliani, P. (2009). A Bayesian approach to model checking biological systems. In Proceedings of the 7th International Conference on Computational Methods in Systems Biology, CMSB ’09 (pp. 218–234). Berlin: Springer.Google Scholar
  6. Karaman, S., & Frazzoli, E. (2008). Vehicle routing problem with metric temporal logic specifications. In IEEE conference on decision and control (pp. 3953–3958).Google Scholar
  7. Kupferman, O., & Vardi, M. (2001). Model checking of safety properties. Formal Methods in System Design, 19(3), 291–314.MathSciNetCrossRefzbMATHGoogle Scholar
  8. Latvala, T. (2003). Effcient model checking of safety properties. In 10th international SPIN workshop, model checking software (pp. 74–88). Springer.Google Scholar
  9. Leahy, K., Zhou, D., Vasile, C., Oikonomopoulos, K., Schwager, M., & Belta, C. (2014). Provably correct persistent surveillance for unmanned aerial vehicles subject to charging constraints. In Proceedings of the international symposium on experimental robotics (ISER).Google Scholar
  10. Lee, T., Leoky, M., & McClamroch, N. (2010). Geometric tracking control of a quadrotor UAV on SE(3). In 49th IEEE conference on decision and control (CDC), 2010 (pp. 5420–5425). IEEE.Google Scholar
  11. Mellinger, D. & Kumar, V. (2011). Minimum snap trajectory generation and control for quadrotors. In IEEE international conference on robotics and automation (ICRA), 2011 (pp. 2520–2525). IEEE.Google Scholar
  12. Michael, N., Stump, E., & Mohta, K. (2011). Persistent surveillance with a team of MAVs. In Proceedings of the international conference on intelligent robots and systems (IROS 11) (pp. 2708–2714). IEEE.Google Scholar
  13. Mulgaonkar, Y. & Kumar, V. (2014). Autonomous charging to enable long-endurance missions for small aerial robots. In Proceedings of SPIE-DSS (p. 90831S).Google Scholar
  14. Ozay, N., Topcu, U., & Murray, R. M. (2011). Distributed power allocation for vehicle management systems. In 50th IEEE conference on decision and control and European control conference (CDC-ECC), 2011 (pp. 4841–4848). IEEE.Google Scholar
  15. Smith, S., Tumova, J., Belta, C., & Rus, D. (2011). Optimal path planning for surveillance with temporal logic constraints. International Journal of Robotics Research, 30(14), 1695–1708.CrossRefGoogle Scholar
  16. Stump, E. & Michael, N. (2011). Multi-robot persistent surveillance planning as a vehicle routing problem. In Proceedings of the IEEE conference on automation science and engineering (CASE) (pp. 569–575). IEEE.Google Scholar
  17. Sundar, K., & Rathinam, S. (2014). Algorithms for routing an unmanned aerial vehicle in the presence of refueling depots. IEEE Transactions on Automation Science and Engineering, 11(1), 287–294.CrossRefGoogle Scholar
  18. Tkachev, I. & Abate, A. (2013). Formula-free finite abstractions for linear temporal verification of stochastic hybrid systems. In Proceedings of the 16th international conference on hybrid systems: Computation and control (pp. 283–292). Philadelphia, PA.Google Scholar
  19. Toth, P., & Vigo, D. (2001). The vehicle routing problem. Philadelphia: SIAM.zbMATHGoogle Scholar
  20. Ulusoy, A., Smith, S. L., Ding, X. C., Belta, C., & Rus, D. (2013). Optimality and robustness in multi-robot path planning with temporal logic constraints. International Journal of Robotics Research, 32(8), 889–911.CrossRefGoogle Scholar
  21. Vasile, C. & Belta, C. (2014). An automata-theoretic approach to the vehicle routing problem. In Robotics: science and systems conference (RSS). Berkeley, CA, USA.Google Scholar
  22. Wongpiromsarn, T., Topcu, U., & Murray, R. M. (2009). Receding horizon temporal logic planning for dynamical systems. Conference on Decision and Control (CDC), 2009, 5997–6004.Google Scholar
  23. Zhou, D. & Schwager, M. (2014). Vector field following for quadrotors using differential flatness. In Proceedings of the international conference on robotics and automation (ICRA) (pp. 6567–6572).Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Boston UniversityBostonUSA

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