Skip to main content

UAV 3D Mobility Model Oriented to Dynamic and Uncertain Environment

  • Conference paper
  • First Online:
Algorithms and Architectures for Parallel Processing (ICA3PP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,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.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Erturk, M., Haque, J., Arslan, H.: Challenges of aeronautical data networks. In: Proceedings of IEEE Aerospace Conference, Montana, pp. 1–7, March 2010

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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 2009

    Google Scholar 

  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 9781467300797

    Google Scholar 

  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. 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. 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. 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. 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. 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. 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. 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 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Nan Di or Fei Dai .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, N., Di, N., Dai, F., Liu, F. (2018). UAV 3D Mobility Model Oriented to Dynamic and Uncertain Environment. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11336. Springer, Cham. https://doi.org/10.1007/978-3-030-05057-3_48

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-05057-3_48

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05056-6

  • Online ISBN: 978-3-030-05057-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics