Autonomous Swarm of Heterogeneous Robots for Surveillance Operations

  • Georgios OrfanidisEmail author
  • Savvas Apostolidis
  • Athanasios Kapoutsis
  • Konstantinos IoannidisEmail author
  • Elias Kosmatopoulos
  • Stefanos Vrochidis
  • Ioannis Kompatsiaris
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11754)


The introduction of Unmanned vehicles (UxVs) in the recent years has created a new security field that can use them as both a potential threat as well as new technological weapons against those threats. Dealing with these issues from the counter-threat perspective, the proposed architecture project focuses on designing and developing a complete system which utilizes the capabilities of multiple UxVs for surveillance objectives in different operational environments. Utilizing a combination of diverse UxVs equipped with various sensors, the developed architecture involves the detection and the characterization of threats based on both visual and thermal data. The identification of objects is enriched with additional information extracted from other sensors such as radars and RF sensors to secure the efficiency of the overall system. The current prototype displays diverse interoperability concerning the multiple visual sources that feed the system with the required optical data. Novel detection models identify the necessary threats while this information is enriched with higher-level semantic representations. Finally, the operator is informed properly according to the visual identification modules and the outcomes of the UxVs operations. The system can provide optimal surveillance capacities to the relevant authorities towards an increased situational awareness.


Unmanned vehicles (UxVs) Visual-based operations Interoperable architecture Surveillance objectives 



This work was supported by ROBORDER project funded by the European Commission under grant agreement No. 740593. The authors would like to thank the ROBORDER consortium for their valuable overall contribution.


  1. 1.
    Haddal, C.C., Gertler, J.: Homeland security: unmanned aerial vehicles and border surveillance. In: Library of Congress Washington DC Congressional Research Service (2010)Google Scholar
  2. 2.
    Fingas, M., Brown, C.: Review of oil spill remote sensing. Spill Sci. Technol. Bull. 4(4), 199–208 (1997)CrossRefGoogle Scholar
  3. 3.
    Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833–851. Springer, Cham (2018). Scholar
  4. 4.
    Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)CrossRefGoogle Scholar
  5. 5.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)Google Scholar
  6. 6.
    Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). Scholar
  7. 7.
    Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The PASCAL visual object classes (VOC) challenge. Int. J. Comput. Vision 88(2), 303–338 (2010)CrossRefGoogle Scholar
  8. 8.
    Bhuiyan, S.M., Adhami, R.R., Khan, J.F.: Fast and adaptive bidimensional empirical mode decomposition using order-statistics filter based envelope estimation. EURASIP J. Adv. Signal Process. 1, 728356 (2008)CrossRefGoogle Scholar
  9. 9.
    Pasqualetti, F., Dörfler, F., Bullo, F.: Attack detection and identification in cyber-physical systems. IEEE Trans. Autom. Control 58(11), 2715–2729 (2013)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Tuttlebee, W.H.: Software Defined Radio: Enabling Technologies. Wiley, Hoboken (2003)CrossRefGoogle Scholar
  11. 11.
    Billinghurst, M., Clark, A., Lee, G.: A survey of augmented reality. Found. Trends Hum. Comput. Interact. 8(2–3), 73–272 (2015) CrossRefGoogle Scholar
  12. 12.
    Kapoutsis, A.Ch., Chatzichristofis, S.A., Kosmatopoulos, E.B.: A distributed, plug-n-play algorithm for multi-robot applications with a priori non-computable objective functions. Int. J. Robot. Res. 38(7), 813–832 (2019)CrossRefGoogle Scholar
  13. 13.
    Kapoutsis, A.Ch., Chatzichristofis, S.A., Kosmatopoulos, E.B.: DARP: divide areas algorithm for optimal multi-robot coverage path planning. J. Intell. Rob. Syst. 86(3), 663–680 (2017)CrossRefGoogle Scholar
  14. 14.
    Tikanmäki, I., Ruoslahti, H.: Increasing Cooperation between the European Maritime Domain Authorities (2017)Google Scholar
  15. 15.
    Darema, F.: Dynamic data driven applications systems: a new paradigm for application simulations and measurements. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2004. LNCS, vol. 3038, pp. 662–669. Springer, Heidelberg (2004). Scholar
  16. 16.
    Keim, D., Andrienko, G., Fekete, J.-D., Görg, C., Kohlhammer, J., Melançon, G.: Visual analytics: definition, process, and challenges. In: Kerren, A., Stasko, J.T., Fekete, J.-D., North, C. (eds.) Information Visualization. LNCS, vol. 4950, pp. 154–175. Springer, Heidelberg (2008). Scholar
  17. 17.
    Card, S.K.: The Psychology of Human-Computer Interaction. CRC Press, Boca Raton (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Georgios Orfanidis
    • 1
    Email author
  • Savvas Apostolidis
    • 1
    • 2
  • Athanasios Kapoutsis
    • 1
  • Konstantinos Ioannidis
    • 1
    Email author
  • Elias Kosmatopoulos
    • 1
    • 2
  • Stefanos Vrochidis
    • 1
  • Ioannis Kompatsiaris
    • 1
  1. 1.Centre for Research and Technology Hellas (CERTH)-Information Technologies Institute (ITI)ThessalonikiGreece
  2. 2.Democritus University of ThraceXanthiGreece

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