Skip to main content
Springer Nature Link
Log in

Collaborative Multi-MSA Multi-Target Tracking and Surveillance: a Divide & Conquer Method Using Region Allocation Trees

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

Abstract

This paper presents a concurrent region decomposition and allocation algorithm that solves the multi-MSA coordination problem within the context of multi-target tracking and surveillance missions. Our collaboration approach achieves favorable computational characteristics, compared to its alternatives, by taking advantage of a data structure we named region allocation tree and the recursive processing strategy it allows. The region allocation tree data structure identifies the candidate regions, organizes information pertaining to tracking uncertainties and mobile sensor agent assignments, and allows for region decomposition and allocation simultaneously in a single depth-first sweep. Our collaboration approach, here, is used in conjunction with a Bayesian tracking algorithm–as the decision making is carried out in the belief space. Our contributions are also located within the wider discourse on multi-robot coordination. The simulation results demonstrate the effectiveness of our multi-MSA coordination approach.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Feron, E., Johnson, E.N.: Aerial Robotics. In: Springer Handbook of Robotics (2008)

  2. Broggi, A., Zelinsky, A., Parent, M., Thorpe, C.E.: Intelligent Vehicles. In: Springer Handbook of Robotics (2008)

  3. Ozguner, U., Acarman, T., Redmill, K.: Autonomous Ground Vehicles. Artech House (2011)

  4. Antonelli, G., Fossen, T.I., Yoerger, D.R.: Underwater Robotics. In: Springer Handbook of Robotics (2008)

  5. Parker, L.E.: Multiple Mobile Robot Systems. In: Springer Handbook of Robotics (2008)

  6. Fox, D., KO, J., Konolige, K., Limketkai, B., Schulz, D., Stewart, B.: Distributed multirobot exploration and mapping. Proc. IEEE 94(7), 1325–1339 (2006)

    Article  MATH  Google Scholar 

  7. Burgard, W., Moors, M., Stachniss, C., Schneider, F.E.: Coordinated multi-robot exploration. IEEE Trans. Robot. 21(3), 376–386 (2005)

    Article  Google Scholar 

  8. Wurm, K.M., Stachniss, C., Burgard, W.: Coordinated multi-robot exploration using a segmentation of the environment. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS 2008, pp 1160–1165 (2008)

  9. Farinelli, A., Iocchi, L., Daniele, N.: Multirobot systems: a classification focused on coordination. Systems, Man, and Cybernetics, Part B: IEEE Transactions on Cybernetics 34(5), 2015–2028 (2004)

    Article  Google Scholar 

  10. Gerkey, B.P., Matarić, M.J.: A formal analysis and taxonomy of task allocation in multi-robot systems. Int. J. Robot. Res. 23(9), 939–954 (2004)

    Article  Google Scholar 

  11. Ayorkor Korsah, G., Stentz, A., Bernardine Dias, M.: A comprehensive taxonomy for multi-robot task allocation. Int. J. Robot. Res. 32(12), 1495–1512 (2013)

    Article  Google Scholar 

  12. Sariel-Talay, S., Balch, T.R., Erdogan, N.: A generic framework for distributed multirobot cooperation. J. Intell. Robot. Syst. 63(2), 323–358 (2011)

    Article  Google Scholar 

  13. Bernardine Dias, M., Zlot, R., Kalra, N., Anthony, S.: Market-based multirobot coordination: A survey and analysis, Proceedings of the IEEE 94(7), 1257–1270 (2006)

  14. Gerkey, B.P., Matari, M.J.: Sold! auction methods for multirobot coordination. IEEE Trans. Robot. Autom. 18(5), 758–768 (2002)

    Article  Google Scholar 

  15. Zlot, R., Stentz, A.: Market-based multirobot coordination for complex tasks. Int. J. Robot. Res. 25(1), 73–101 (2006)

    Article  Google Scholar 

  16. Choi, H.-L., Brunet, L., How, J.P.: Consensus-based decentralized auctions for robust task allocation. IEEE Trans. Robot. 25(4), 912–926 (2009)

    Article  Google Scholar 

  17. Kivelevitch, E., Cohen, K., Kumar, M.: A market-based solution to the multiple traveling salesmen problem. J. Intell. Robot. Syst. 72(1), 21–40 (2013)

    Article  Google Scholar 

  18. Tang, Z., Ozguner, U.: Sensor fusion for target track maintenance with multiple UAVs based on Bayesian filtering method and hospitability map. In: 42nd IEEE International Conference on Decision and Control, pp. 19–24 (2003)

  19. Tang, Z., Ozguner, U.: Motion planning for multitarget surveillance with mobile sensor agents. IEEE Trans. Robot. 21(5), 898–908 (2005)

    Article  Google Scholar 

  20. Tang, Z., Ozguner, U.: PF-HMap: A Target Track Maintenance Approach for Mobile Sensor Platforms with Intermittent and Regional Measurements. In: 45th IEEE Conference on Decision and Control, pp. 6757–6762. IEEE (2006)

  21. Kassas, Z.M., Ozguner, U.: A nonlinear filter coupled with hospitability and synthetic inclination maps for in-surveillance and out-of-surveillance tracking. Syst., Man, and Cybern., Part C: IEEE Trans. Appl. Rev. 40(1), 87–97 (2010)

    Article  Google Scholar 

  22. Adamey, E., Ozguner, U.: Cooperative multitarget tracking and surveillance with mobile sensing agents: A decentralized approach. In: 2011 4th International IEEE Conference on Intelligent Transportation Systems. ITSC 2011, pp. 1916–1922 (2011)

  23. Adamey, E., Ozguner, U.: A decentralized approach for multi-UAV multitarget tracking and surveillance. Proceedings SPIE 8389, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR III, 838915 (2012)

  24. Stone, L.D., Streit, R.L., Corwin, T.L., Bell, K.L.: Bayesian Multiple Target Tracking Second Edition. Artech House (2013)

  25. Praveen Babu Choppala: Bayesian multiple target tracking. Victoria University of Wellington, PhD thesis (2014)

    Google Scholar 

  26. Cormen, T.H.: Introduction to Algorithms. MIT Press (2009)

  27. Skiena, S.S.: The Algorithm Design Manual. Springer (2008)

  28. Khaleghi, B., Khamis, A., Karray, F.O., Razavi, S.N.: Multisensor data fusion a review of the state-of-the-art. Information Fusion 14(1), 28–44 (2013)

    Article  Google Scholar 

  29. Chen, Z.: Bayesian filtering: From Kalman filters to particle filters, and beyond. Statistics 182(1), 1–69 (2003)

    Article  Google Scholar 

  30. Russell, S., Norvig, P.: Artificial Intelligence Prentice Hall (2009)

  31. Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press (2005)

  32. Hahnel, D., Triebel, R., Burgard, W., Sebastian, T.: Map building with mobile robots in dynamic environments. IEEE International Conference on Robotics and Automation. IEEE ICRA 2003. Proceedings, pp. 1557–1563 (2003)

  33. Thrun, S.: Learning occupancy grid maps with forward sensor models. Auton robot. 15(2), 111–127 (2003)

    Article  Google Scholar 

  34. Collins, T., Collins, J.J., Ryan, C.: Occupancy grid mapping: An empirical evaluation. 2007 Mediterranean Conference on Control & Automation. pp. 1–6. IEEE (2007)

  35. Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82(Series D), 35–45 (1960)

    Article  Google Scholar 

  36. Simon, D.: Optimal State Estimation. Wiley-Interscience (2006)

  37. Doucet, A., Johansen, A.M.: A tutorial on particle filtering and smoothing: Fifteen years later. Handbook of Nonlinear Filtering 12(3), 656–704 (2009)

    MATH  Google Scholar 

  38. Ristic, B., Arulampalam, S., Gordon, N.J.: Beyond the Kalman Filter. Artech House (2004)

Download references

Author information

Authors and Affiliations

  1. Center of Intelligent Transportation Research (CITR) Center of Automotive Research (CAR), The Ohio State University, Columbus, OH, USA

    Emrah Adamey & Ümit Özgüner

  2. Turkish Air Force Academy, Bakirköy/Istanbul, Turkey

    Abdullah Ersan Oğuz

Authors
  1. Emrah Adamey
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Abdullah Ersan Oğuz
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Ümit Özgüner
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Emrah Adamey.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Adamey, E., Oğuz, A.E. & Özgüner, Ü. Collaborative Multi-MSA Multi-Target Tracking and Surveillance: a Divide & Conquer Method Using Region Allocation Trees. J Intell Robot Syst 87, 471–485 (2017). https://doi.org/10.1007/s10846-017-0499-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-017-0499-4

Keywords