Journal of Intelligent & Robotic Systems

, Volume 77, Issue 3–4, pp 629–652 | Cite as

A Cooperative Network Framework for Multi-UAV Guided Ground Ad Hoc Networks

  • Vishal Sharma
  • Rajesh Kumar


Cooperative ad hoc networks are becoming very important in various military and civilian applications. The interfacing between different ad hoc networks provides large applications in field of surveillance, navigation, disaster monitoring and homeland security. This paper focuses on implementation of UAV (unmanned aerial vehicles) ad hoc network that forms a guidance system for ground ad hoc network. The network framework proposed in the paper uses neural network to form cognitive and topology maps. Indirect and Bayesian Kalman Filter are used for estimations. These estimations allows updating of pre-constructed cognitive map to form ideal final search map that is shared among all nodes to perform search and track operations. The analysis showed that the proposed framework is capable of forming a search maps that is able to define multiple way points for each UAV in the network to follow a non-redundant path for searching and identifying various user nodes and geographical territories. The effectiveness of the model is demonstrated using simulations.


Cooperative network Cognitive maps Ground Ad Hoc network Kalman Filter Topology organizing maps UAVs ad hoc network Neural network 

Mathematics Subject Classification (2010)

11R52 15A33 62M45 82C32 


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  1. 1.
    Zang, C., Zang, S.: Mobility prediction clustering algorithm for UAV networking. In: GLOBECOM Workshops. IEEE, pp. 1158–1161 (2011)Google Scholar
  2. 2.
    Bekmezci, ., Sahingoz, O.K., Temel, .: Flying ad-hoc networks (FANETs): a survey. Ad Hoc Netw. 11(3), 1254–1270 (2013)Google Scholar
  3. 3.
    Bellur, B., Lewis, M., Templin, F.: An ad-hoc network for teams of autonomous vehicles. In: Proceedings of the First Annual Symposium on Autonomous Intelligence Networks and Systems (2002)Google Scholar
  4. 4.
    How, J.P., Fraser, C., Kulling, K.C., Bertuccelli, L.F., Toupet, O., Brunet, L., Roy, N.: Increasing autonomy of UAVs, Robotics & Automation Magazine. IEEE 16(2), 43–51 (2009)Google Scholar
  5. 5.
    Yang, Y., Minai, A., Polycarpou, M.M.: Evidential mapbuilding approaches for multi-UAV cooperative search. In: Proceedings of the American Control Conference, vol. 1, pp. 116 (2005) Google Scholar
  6. 6.
    Hauert, S., Zufferey, J.C., Floreano, D.: Evolved swarming without positioning information: an application in aerial communication relay. Auton. Robot. 26(1), 21–32 (2009)CrossRefGoogle Scholar
  7. 7.
    Lilien, L., Gupta, A., Kamal, Z.E.: & Yang, Z.: Opportunistic resource utilization networksA new paradigm for specialized ad hoc networks. Comput. Electr. Eng. 36(2), 328–340 (2010)CrossRefzbMATHGoogle Scholar
  8. 8.
    Liu, M., Lin, J., Yuan, Y.: Research of UAV cooperative reconnaissance with self-organization path planning. In: International Conference on Computer, Networks and Communication Engineering, ICCNCE, Atlantis Press, pp. 207–213 (2013)Google Scholar
  9. 9.
    Lilien, L.T., Ben Othmane, L., Angin, P., DeCarlo, A., Salih, R.M., Bhargava, B.: A simulation study of ad hoc networking of UAVs with opportunistic resource utilization networks. J. Netw. Comput. Appl. 38, 3–15 (2013)CrossRefGoogle Scholar
  10. 10.
    Perez, D., Maza, I., Caballero, F., Scarlatti, D., Casado, E., Ollero, A.: A ground control station for a multi-uav surveillance system. J. Intell. Robot. Syst. 69(1-4), 119–130 (2013)CrossRefGoogle Scholar
  11. 11.
    Cevik, P., Kocaman, I., Akgul, A.S., Akca, B.: The small and silent force multiplier: a swarm UAVelectronic attack. J. Intell. & Robot. Syst. 70(1-4), 595–608 (2013)Google Scholar
  12. 12.
    Gu, D.L., Pei, G., Ly, H., Gerla, M., Zhang, B., Hong, X.: UAV aided intelligent routing for ad-hoc wireless network in single-area theater, Wireless Communications and Networking Confernce, IEEE, vol. 3, pp. 1220–1225 (2000)Google Scholar
  13. 13.
    Capitn, J., Merino, L., Caballero, F., Ollero, A.: Decentralized delayed-state information filter (DDSIF): A new approach for cooperative decentralized tracking. Robot. Auton. Syst. 59(6), 376–388 (2011)CrossRefGoogle Scholar
  14. 14.
    Dressler, F., Akan, O.B.: A survey on bio-inspired networking. Comput. Netw. 54(6), 881–900 (2010)CrossRefzbMATHGoogle Scholar
  15. 15.
    Muller, M.: Flying Ad-Hoc Networks, Institute of Media Informatics Ulm University, pp. 53–59 (2012)Google Scholar
  16. 16.
    Li, J., Zhou, Y., Lamont, L., Toulgoat, M., Rabbath, C.A.: Packet Delay in UAV Wireless Networks Under Nonsaturated Traffic and Channel Fading Conditions, Wireless Personal Communications, pp. 1–19 (2013)Google Scholar
  17. 17.
    Yang, Y., Polycarpou, M.M., Minai, A.A.: Multi-UAV cooperative search using an opportunistic learning method. Trans. ASME 129(5), 716 (2007)CrossRefGoogle Scholar
  18. 18.
    Polycarpou, M.M., Yang, Y., Passino, K.M.: A cooperative search framework for distributed agents, Intelligent Control, (ISIC’01). In: Proceedings of the IEEE International Symposium, pp. 1–6 (2001)Google Scholar
  19. 19.
    Trawny, N., Roumeliotis, S.I.: Indirect Kalman filter for 3D attitude estimation, University of Minnesota, Department Computer Science & Engineering Technical Report, pp. 2 (2005)Google Scholar
  20. 20.
    Meinhold, R.J., Singpurwalla, N.D.: Understanding the Kalman filter. Am. Stat. 37(2), 123–127 (1983)MathSciNetGoogle Scholar
  21. 21.
    Benini, A.,Mancini, A., Longhi, S.: An IMU/UWB/Visionbased Extended Kalman Filter for Mini-UAV Localization in Indoor Environment using 802.15. 4a Wireless Sensor Network. J. Intell. Robot. Syst. 70(1-4), 461–476 (2013)CrossRefGoogle Scholar
  22. 22.
    Sakhaee, E., Jamalipour, A., Kato, N.: Aeronautical ad hoc networks. Wireless Communications and Networking Conferece, IEEE, pp. 246–251 (2006)Google Scholar
  23. 23.
    Iordanakis, M., Yannis, D., Karras, K., Bogdos, G., Dilintas, G., Amirfeiz, M., Baiotti, S.: Ad-hoc routing protocol for aeronautical mobile ad-hoc networks. Fifth International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP) (2006)Google Scholar
  24. 24.
    Paunicka, J.L., Corman, D.E., Mendel, B.R.: A CORBA based middleware solution for UAVs. In: Object-Oriented Real-Time Distributed Computing, ISORC- Proceedings. Fourth IEEE International Symposium, pp. 261–267 (2001)Google Scholar
  25. 25.
    Palazzi, C.E., Roseti, C., Luglio, M., Gerla, M., Sanadidi, M.Y., Stepanek, J.: Enhancing transport layer capability in HAPSSatellite integrated architecture. Wirel. Pers. Commun. 32(3-4), 339–356 (2005)CrossRefGoogle Scholar
  26. 26.
    Palazzi, C.E., Roseti, C., Luglio, M., Gerla, M., Sanadidi, M.Y., Stepanek, J.: Satellite coverage in urban areas using Unmanned Airborne Vehicles (UAVs). In: Vehicular Technology Conference, IEEE 59th, Vol. 5, pp. 2886–2890 (2004)Google Scholar
  27. 27.
    Allred, J., Hasan, A.B., Panichsakul, S., Pisano, W., Gray, P., Huang, J., Mohseni, K.: Sensorflock: an airborne wireless sensor network of micro-air vehicles. In: Proceedings of the 5th International Conference on Embedded Networked Sensor Systems (2007)Google Scholar
  28. 28.
    Ajami, A., Balmat, J.F., Gauthier, J.P., Maillot, T.: Path planning and Ground Control Station simulator for UAV. Aerospace Conference, IEEE, pp. 1–13 (2013)Google Scholar
  29. 29.
    Craighead, J., Murphy, R., Burke, J., Goldiez, B.: A survey of commercial & open source unmanned vehicle simulators. Robotics and Automation, IEEE International Conference, pp. 852–857 (2007)Google Scholar
  30. 30.
    Lin, L., Sun, Q., Li, J., Yang, F.: A novel geographic position mobility oriented routing strategy for UAVs. J. Comput. Inf. Syst. 8(2), 709–716 (2012)Google Scholar
  31. 31.
    Bellur, B., Ogier, R.G.: A reliable, efficient topology broadcast protocol for dynamic networks. In: INFOCOM’99. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 1, pp. 178–186 (1999)Google Scholar
  32. 32.
    Rysdyk, R.: UAV path following for constant line-of-sight. In: 2nd AIAA Unmanned Unlimited. Conference and Work shop and Exhibit, San Diego, CA (2003)Google Scholar
  33. 33.
    Akbas, M.I., Turgut, D.: APAWSAN: Actor positioning for aerial wireless sensor and actor networks. In: Local Computer Networks (LCN), IEEE 36th Conference. pp. 563–570 (2011)Google Scholar
  34. 34.
    Aggarwal, P., Syed, Z., Niu, X., El-Sheimy, N.: Cost effective testing and calibration of low cost MEMS sensors for integrated positioning, navigation and mapping systems. In: Proceedings of XIII FIG Conference, pp. 8–13 (2006)Google Scholar
  35. 35.
    Jung, D., Tsiotras, P.: Inertial attitude and position reference system development for a small UAV, AIAA Infotech at aerospace. pp. 7–10 (2007)Google Scholar
  36. 36.
    Durham, C.M., Andel, T.R., Hopkinson, K.M., Kurkowski, S.H.: Evaluation of an OPNET model for unmanned aerial vehicle (UAV) networks. In: Proceedings of the Spring Simulation Multiconference, pp. 66 (2009)Google Scholar
  37. 37.
    Morse, B.S., Engh, C.H., Goodrich, M.A.: UAV video coverage quality maps and prioritized indexing for wilderness search and rescue. In: Proceedings of the 5th ACM/IEEE International Conference on Human-robot Interaction, pp. 227–234 (2010)Google Scholar
  38. 38.
    Lpez, J., Royo, P., Pastor, E., Barrado, C., Santamaria, E.: A middleware architecture for unmanned aircraft avionics. In: Proceedings of the 2007 ACM/IFIP/USENIX International Conference on Middleware Companion. pp. 24 (2007)Google Scholar
  39. 39.
    Perkins, C.E., Royer, E.M.: Ad-hoc on-demand distance vector routing. In: Mobile Computing Systems and Applications, Proceedings, WMCSA’99, Second IEEE Workshop, pp. 90–100 (1990)Google Scholar
  40. 40.
    Konishi, K., Maeda, K., Sato, K., Yamasaki, A., Yamaguchi, H., Yasumoto, K., Higashino, T.: Mobireal simulator-evaluating manet applications in real environments. In: 13th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, pp. 499–502 (2005)Google Scholar
  41. 41.
    Abdessameud, A., Tayebi, A.: Global trajectory tracking control of VTOL-UAVs without linear velocity measurements. Automatica 46(6), 1053–1059 (2010)CrossRefzbMATHMathSciNetGoogle Scholar
  42. 42.
    Yamasaki, T., Sakaida, H., Enomoto, K., Takano, H., Baba, Y.: Robust trajectory-tracking method for UAV guidance using proportional navigation. In: International Conference on Control, Automation and Systems, ICCAS’07, pp. 1404–1409 (2007)Google Scholar
  43. 43.
    Zhan, P., Casbeer, D., Swindlehurst, A.L.: A centralized control algorithm for target tracking with UAVs. Conference Record of the 39th Asilomar Conference on Signals, Systems and Computers, pp. 1148–1152 (2005)Google Scholar
  44. 44.
    Karras, K., Kyritsis, T., Amirfeiz, M., Baiotti, S.: Aeronautical mobile Ad hoc networks, Wireless Conference, EW, 14th European, IEEE, pp. 1–6 (2008)Google Scholar
  45. 45.
    Miles, J., Kamath, G., Muknahallipatna, S., Stefanovic, M., Kubichek, R.F.: Optimal trajectory determination of a single moving beacon for efficient localization in a mobile ad-hoc network. Ad Hoc Netw. 11(1), 238–256 (2013)CrossRefGoogle Scholar
  46. 46.
    Niculescu, D., Nath, B.: DV based positioning in ad hoc networks. Telecommun. Syst. 22(1–4), 267–280 (2003)CrossRefGoogle Scholar
  47. 47.
    Cetin, O., Zagli, I., Yilmaz, G.: Establishing Obstacle and Collision Free Communication Relay for UAVs with Artificial Potential Fields. J. Intell. Robot. Syst. 69(1-4), 361–372 (2013)CrossRefGoogle Scholar
  48. 48.
    Jensen, A., Chen, Y.: Tracking tagged fish with swarming Unmanned Aerial Vehicles using fractional order potential fields and Kalman filtering. Unmanned Aircraft Systems (ICUAS), International Conference, pp. 1144–1149 (2013)Google Scholar
  49. 49.
    Valavanis, K.P.: Advances in unmanned aerial vehicles: state of the art and the road to autonomy, vol. 33. Springer (2007)Google Scholar
  50. 50.
    Rubin, I., Behzad, A., Ju, H.J., Zhang, R., Huang, X., Liu, Y., Khalaf, R.: Ad hoc wireless networks with mobile backbones. In: 15th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, vol. 1, pp. 566–573 (2004)Google Scholar
  51. 51.
    Jara, C.A., Candelas, F.A., Gil, P., Torres, F., Esquembre, F., Dormido, S.: Ejs+ EjsRL: An interactive tool for industrial robots simulation, Computer Vision and remote operation. Robot. Auton. Syst. 59(6), 389–401 (2011)CrossRefGoogle Scholar
  52. 52.
    Zhang, G., Yang, K., Liu, P., Feng, X.: Incentive Mechanism for Multiuser Cooperative Relaying in Wireless Ad Hoc Networks: A Resource-Exchange Based Approach, Wireless Personal Communications, pp. 1–19 (2013)Google Scholar
  53. 53.
    Levin, L., Segal, M., Shpungin, H.: Cooperative data collection in ad hoc networks. Wirel. Netw. 19(2), 145–159 (2013)CrossRefGoogle Scholar
  54. 54.
    Carpenter, G.A., Grossberg, S.: ART 2: Self-organization of stable category recognition codes for analog input patterns. In: Robotics and IECON Conferences, International Society for Optics and Photonics, pp. 272–280 (1988)Google Scholar
  55. 55.
    Acharya, T., Paul, G.: Maximum Lifetime Broadcast Communications in Cooperative Multihop Wireless Ad Hoc Networks: Centralized and Distributed Approaches. Ad Hoc Netw. 11(6), 1667–1682 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringThapar UniversityPatialaIndia
  2. 2.School of Mathematics and Computer ApplicationsThapar UniversityPatialaIndia

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