Skip to main content
SpringerLink
Log in

Dynamic Data Driven Application System for Plume Estimation Using UAVs

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

In this article, a full dynamic data-driven application system (DDDAS) is proposed for dynamically estimating a concentration plume and planning optimal paths for unmanned aerial vehicles (UAVs) equipped with environmental sensors. The proposed DDDAS dynamically incorporates measured data from UAVs into an environmental simulation while simultaneously steering measurement processes. In order to assimilate incomplete and noisy state observations into this system in real-time, the proper orthogonal decomposition (POD) is used to estimate the plume concentration by matching partial observations with pre-computed dominant modes in a least-square sense. In order to maximize the information gain, UAVs are dynamically driven to hot spots chosen based on the POD modes. Smoothed particle hydrodynamics (SPH) techniques are used for UAV guidance with collision and obstacle avoidance. We demonstrate the efficacy of the data assimilation and control strategies in numerical simulations. Especially, a single UAV outperforms the ten static sensors in this scenario in terms of the mean square error over the full time interval. Additionally, the multi-vehicle data collection scenarios outperform the single vehicle scenarios for both static sensors at optimal positions and UAVs controlled by SPH.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Corrigan, C.E., Roberts, G.C., Ramana, M.V., Kim, D., Ramanathan, V.: Capturing vertical profiles of aerosols and black carbon over the indian ocean using autonomous unmanned aerial vehicles. Atmos. Chem. Phys. Discuss. 7, 11429–11463 (2007)

    Article  Google Scholar 

  2. Spiess, T., Bange, J., Buschmann, M., Vörsmann, P.: First application of the meteorological mini UAV ‘M2AV’. Meteorol. Z. 16(2), 159–169 (2007)

    Article  Google Scholar 

  3. van den Kroonenberg, A., Martin, S., Beyrich, F., Bange, J.: Spatially-averaged temperature structure parameter over a heterogeneous surface measured by an unmanned aerial vehicle. Bound. Layer Meteorol. 142, 55–77 (2011)

    Article  Google Scholar 

  4. Hardin, P., Jensen, R.: Small-scale unmanned aerial vehicles in environmental remote sensing: challenges and opportunities. GISci. Remote Sens. 48(1), 99–111 (2011)

    Article  Google Scholar 

  5. Allred, J., Hasan, A., Pisano, B., Panichsakul, S., Gray, P., Han, R., Lawrence, D., Mohseni, K.: SensorFlock: a mobile system of networked micro-air vehicles. In: The ACM SenSys 2007: The 5th ACM Conference on Embedded Networked Sensor Systems, Sydney, Australia (2007)

  6. Leven, S., Zufferey, J.-C., Floreano, D.: Dealing with Mid-air collisions in dense collective aerial systems. J. Field Robot. 28(3), 405–423 (2011)

    Article  Google Scholar 

  7. Darema, F.: Dynamic data driven applications systems: a new paradigm for application simulations and measurements. In: Int. Conf. Comput. Sci. (ICCS), vol. 3038, pp. 662–669. Kraków, Poland (2004)

  8. Mandel, J., Bennethum, L., Beezley, J., Coen, J., Douglas, C., Kim, M., Vodacek, A.: A wildland fire model with data assimilation. Math. Comput. Simul. 79, 584–606 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  9. Rodriguez, R., Cortés, A., Margalef, T.: Injecting dynamic real-time data into a DDDAS for forest fire behavior prediction. In: Int. Conf. Comput. Sci. (ICCS), vol. 5454, pp. 489–499. Baton Rouge, LA (2009)

  10. Akcelik, V., Biros, G., Drǎgǎnescu, A., Ghattas, O., Hill, J., van Bloeman Waanders, B.: Dynamic data-driven inversion for terascale simulations: real-time identification of airborne contaminants. In: Proceedings of SC2005, pp. 43–58. Seattle, WA (2005)

  11. Lieberman, C., Fidkowski, K.W., van Bloemen Waanders, B.: Hessian-based model reduction: large-scale inversion and prediction. Int. J. Numer. Methods Fluids 71, 135–150 (2013)

    Article  Google Scholar 

  12. Akcelik, V., Biros, G., Draganescu, A., Ghattas, O., Hill, J.: Inversion of airborne contaminants in a regional model. In: Int. Conf. Comput. Sci. (ICCS), vol. 3993, pp. 481–488. Reading, UK (2006)

  13. Fisher, M., Nocedal, J., Trémolet, Y., Wright, S.J.: Data assimilation in weather forecasting: a case study in PDE-constrained optimization. Optim. Eng. 10(3), 409–426 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  14. Holmes, P., Lumley, J., Berkooz, G.: Turbulence, Coherent Structures, Dynamical Systems and Symmetry. Cambridge Univ. Press, Cambridge, UK (1998)

    MATH  Google Scholar 

  15. Stam, J.: Stable fluids. In: Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, pp. 121–128. Los Angeles, CA (1999)

  16. Antsaklis, P.J., Passino, K.M.: Towards intelligent autonomous control system: architecture and fundamental issues. J. Intell. Robot. Syst. 1, 315–342 (1989)

    Article  Google Scholar 

  17. Loève, M.: Probability Theory. Van Nostrand, NY (1955)

    MATH  Google Scholar 

  18. Cortial, J., Farhat, C., Guibas, L.J., Rajashekhar, M.: Compressed sensing and time-parallel reduced-order modeling for structural health monitoring using a DDDAS. In: Int. Conf. Comput. Sci. (ICCS), vol. 4487, pp. 1171–1179. Beijing, China (2007)

  19. Rowley, C.W., Colonius, T., Murra, R.M.: Model reduction for compressible fows using pod and galerkin projection. Phys. D. 189, 115–129 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  20. Parrilo, P., Paganini, F., Verghese, G., Lesieutre, B., Marsden, J.E.: Model Reduction for Analysis of Cascading Failures in Power Systems, pp. 4208–4212. American Control Conference, San Diego, CA (1999)

  21. Rathinam, M., Petzold, L.R.: Dynamic iteration using reduced order models: a method for simulation of large scale modular systems. SIAM J. Numer. Anal. 40, 1446–1474 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  22. Amabili, M., Sarkar, A., Paidoussis, M.P.: Reduced-order models for nonlinear vibrations of cylindrical shells via the proper orthogonal decomposition method. J. Fluids Struct. 18, 227–250 (2003)

    Article  Google Scholar 

  23. Graham, M., Kevrekidis, I.: Alternative approaches to the Karhunen-Loève decomposition for model reduction and data analysis. Comput. Chem. Eng. 20, 495–506 (1996)

    Article  Google Scholar 

  24. Shvartsman, S., Kevrekidis, I.: Low-dimensional approximation and control of periodic solutions in spatially extended systems. Phys. Rev. E 58, 361–368 (1998)

    Article  Google Scholar 

  25. Barrault, M., Maday, Y., Nguyen, N.C., Patera, A.T.: An ‘Empirical Interpolation’ method: application to efficient reduced-basis discretization of partial differential equations. C. R. Acad. Sci. Paris, Ser. I 339, 667–672 (2004)

    Google Scholar 

  26. Chaturantabut, S., Sorensen, D.C.: Nonlinear model reduction via discrete empirical interpolation. SIAM J. Sci. Comput. 32, 2737–2764 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  27. Hsu, P.-M., Lin, C.-L., Yang, M.-Y.: On the complete coverage path planning for mobile robots. J. Intell. Robot. Syst. 1–19 (2013). doi:10.1007/s10846-013-9856-0

  28. Lazanas, A., Latombe, J.C.: Motion planning with uncertainty: a landmark approach. Artif. Intell. 76, 287–317 (1995)

    Article  Google Scholar 

  29. Rao, N.: Robot navigation in unknown generalized polygonal terrains using vision sensors. IEEE Trans. Syst. Man Cybern. 25(6), 947–962 (1995)

    Article  Google Scholar 

  30. Peng, L., Zhao, Y., Tian, B., Zhang, J., Wang, B.-H., Zhang, H.-T., Zhou, T.: Consensus of self-driven agents with avoidance of collisions. Phys. Rev. E 79, 026113 (2009)

    Article  Google Scholar 

  31. Zhang, G., Ferrari, S., Qian, M.: An information roadmap method for robotic sensor path planning. J. Intell. Robot. Syst. 56(1–2), 69–98 (2009)

    Article  MATH  Google Scholar 

  32. Krause, A., Singh, A., Guestrin, C.: Near-optimal sensor placements in Gaussian processes: theory, efficient algorithms and empirical studies. J. Mach. Learn. Res. 9, 235–284 (2008)

    MATH  Google Scholar 

  33. Pimenta, L., Mendes, M., Mesquita, R., Pereira, G.: Fluids in electrostatic fields: an analogy for multirobot control. IEEE Trans. Magn. 43(4), 1765–1768 (2007)

    Article  Google Scholar 

  34. Pimenta, L., Michael, N., Mesquita, R., Pereira, G., Kumar, V.: Control of swarms based on hydrodynamic models. In: IEEE Int. Conf. Robot. Autom. (ICRA), pp. 1948–1953. Pasadena, CA (2008)

  35. Huhn, S., Mohseni, K.: Cooperative control of a team of AUVs using smoothed particle hydrodynamics with restricted communication. In: Proceedings of the ASME 28th International Conference on Ocean, Offshore and Arctic Engineering, vol. OMAE 2009–79869, pp. 531–538. Honalulu, HA (2009)

  36. Lipinski, D., Mohseni, K.: Cooperative control of a team of unmanned vehicles using smoothed particle hydrodynamics. AIAA Paper 2010–8316, AIAA Guidance, Navigation, and Control Conference, Toronto, Ontario, Canada (2010)

  37. Shaw, A., Mohseni, K.: A fluid dynamic based coordination of a wireless sensor network of unmanned aerial vehicles: 3-D simulation and wireless communication characterization. IEEE Sensors J., Special Issue on Cognitive Sensor Networks 11(3), 722–736 (2011)

    Google Scholar 

  38. Lipinski, D., Mohseni, K.: A master-slave fluid cooperative control algorithm for optimal trajectory planning. In: IEEE Int. Conf. Robot. Autom. (ICRA), pp. 3347–3351. Shanghai, China (2011)

  39. Monaghan, J.: Smoothed particle hydrodynamics. Annu. Rev. Astron. Astrophys. 30, 543–574 (1992)

    Article  Google Scholar 

  40. Liu, G., Liu, M.: Smoothed Particle Hydrodynamics: A Meshfree Particel Method. World Scientific Publishing Company, Hackensack, NJ (2003)

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, Institute for Networked Autonomous Systems, University of Florida, Gainesville, FL, 32611, USA

    Liqian Peng, Doug Lipinski & Kamran Mohseni

  2. Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32611, USA

    Kamran Mohseni

Authors
  1. Liqian Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Doug Lipinski
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kamran Mohseni
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kamran Mohseni.

Additional information

The authors acknowledge the support from the US Air Force Office of Scientific Research (AFOSR).

Rights and permissions

Reprints and permissions

About this article

Cite this article

Peng, L., Lipinski, D. & Mohseni, K. Dynamic Data Driven Application System for Plume Estimation Using UAVs. J Intell Robot Syst 74, 421–436 (2014). https://doi.org/10.1007/s10846-013-9964-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-013-9964-x

Keywords

Search

Navigation