Advertisement

UAV 3D Mobility Model Oriented to Dynamic and Uncertain Environment

  • Na Wang
  • Nan Di
  • Fei Dai
  • Fangxin Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11336)

Abstract

Currently, unmanned aerial vehicle (UAV) swarm has been widely used for emergency rescue in disaster areas. In dynamic and uncertain environments, the uneven distribution of events and obstacles seriously affect the efficiency of UAVs’ missions and the safety of airborne operations. The traditional UAV mobility models pay more attention to the UAV’s own moving rules, so as to make the UAV’ flight pattern meet real conditions as much as possible, while ignoring the requirements of UAVs’ mission and uncertainties of environment. Based on the 3D Visit-Density Gauss-Semi-Markov Mobility (3D-VDGMM) model, this paper proposes a 3D Mobility Model oriented to Dynamic and Uncertain environment (3D-DUMM). The 3D-DUMM has made improvements to emergency rescue missions while fully considering the dynamic distributed, dense and irregular obstacles in the rescue area. Simulation experiments show that 3D-DUMM can well captured uncertain events and can safely deal with dynamic and complex rescue environments.

Keywords

Three-Dimensional mobility model Dynamic uncertainty Emergency rescue 

References

  1. 1.
    Erturk, M., Haque, J., Arslan, H.: Challenges of aeronautical data networks. In: Proceedings of IEEE Aerospace Conference, Montana, pp. 1–7, March 2010Google Scholar
  2. 2.
    Bujari, A., Calafate, C.T., Cano, J.C., et al.: Flying ad-hoc network application scenarios and mobility models. Int. J. Distrib. Sens. Netw. 13(10), 155014771773819 (2017)CrossRefGoogle Scholar
  3. 3.
    Zaouche, L., Natalizio, E., Bouabdallah, A.: ETTAF: efficient target tracking and filming with a flying ad hoc network. In: International Workshop on Experiences with the Design and Implementation of Smart Objects, pp. 49–54. ACM (2015)Google Scholar
  4. 4.
    Sheng, Z., Ming-hui, Y., Yi, H., et al.: An exploration of evaluation of mobility model based on analytic hierarchy process in opportunistic network. J. Nanchang Hangkong Univ. Nat. Sci. 31(3), 15–22 (2017)Google Scholar
  5. 5.
    Broyles, D., Jabbar, A., Sterbenz, D.: Design and analysis of a 3-D Gauss Markov mobility model for highly-dynamic airborne networks. In: International Telemetering Conference, Las Vegas, NV, October 2009Google Scholar
  6. 6.
    Rohrer, J.P.: AeroRP performance in highly-dynamic airborne networks using 3D Gauss-Markov mobility model. In: MILCOM 2011 Military Communications Conference, pp. 834–841 (2011). ISSN 2155-7578, ISBN 9781467300797Google Scholar
  7. 7.
    Zheng, B., Zhang, H.Y., Huang, G.C., et al.: Design and implemention of a 3-D smooth mobility mode. J. Xidian Univ. 38(6), 179–184 (2011)Google Scholar
  8. 8.
    Kuiper, E., Nadjm-Tehrani, S.: Mobility models for UAV group reconnaissance applications. In: International Conference on Wireless and Mobile Communications, p. 33. IEEE (2006)Google Scholar
  9. 9.
    He, M., Chen, Q.L., Chen, X.L., et al.: Fish swarm inspired Ad hoc networks node random mobility optimization model in 3D environment. Chin. J. Sci. Instrum. 35(12), 2826–2834 (2014)Google Scholar
  10. 10.
    Regis, P.A., Bhunia, S., Sengupta, S.: Implementation of 3D obstacle compliant mobility models for UAV networks in ns-3, pp. 124–131 (2016)Google Scholar
  11. 11.
    Belkhouche, F., Bendjilali, B.: Reactive path planning for 3-D autonomous vehicles. IEEE Trans. Control Syst. Technol. 20(1), 249–256 (2012)Google Scholar
  12. 12.
    Yi, Z., Fan-yu, D., Yuan, L.: A local path planning algorithm based on improved morphin search tree. Electr. Opt. Control 23(7), 15–19 (2016)Google Scholar
  13. 13.
    Jenie, Y.I., Van Kampen, E.J., De Visser, C.C., et al.: Three-dimensional velocity obstacle method for UAV deconicting maneuvers. In: AIAA Guidance, Navigation and Control Conference, AIAA 2015-0592. AIAA Kissimmee (2015)Google Scholar
  14. 14.
    Zhang, G.M., Wang, N., Wang, R., et al.: UAV 3D mobility model based on visit density. J. Beijing Univ. Posts Telecommun. 40(s1), 112–116 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Army Engineering University of PLANanjingChina
  2. 2.Institute of China Electronic System Engineering CompanyBeijingChina
  3. 3.Shanghai Branch, Coordination Center of China, National Computer Network Emergency Response Technical TeamShanghaiChina

Personalised recommendations