Skip to main content
SpringerLink
Log in

Autonomous Landing-Site Selection for a Small Drone

  • Published:
Russian Engineering Research Aims and scope

Abstract

In emergency landings, independent selection of the landing site increases the autonomy of pilotless helicopters (drones). The proposed approach consists of three stages: assessment of the requirements on the landing site, taking account of local conditions; preliminary selection of possible landing sites by means of on-board vision systems; and selection of the most promising site by the analysis of high-precision 3D images. This approach significantly improves drone efficiency and safety in monitoring the locations of natural disasters and other emergencies.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.

Similar content being viewed by others

REFERENCES

  1. Evdokimenkov, V.N., Kim, N.V., Kozorez, D.A., and Mokrova, M.I., Control of unmanned aerial vehicles during fire situation monitoring, INCAS Bull., 2019, vol. 11, pp. 66–73.

    Google Scholar 

  2. Kim, N. and Chervonenkis, M., Situational control unmanned aerial vehicles for traffic monitoring, Modern Appl. Sci., 2015, vol. 9, no. 5, pp. 1913–1844.

    Google Scholar 

  3. Jähne, B., Digital Image Processing, New York: Springer-Verlag, 2005.

    MATH  Google Scholar 

  4. Kim, N.V. and Bodunkov, N.E., Automated decision making in road traffic monitoring by on-board unmanned aerial vehicle system, in Computer Vision in Control Systems-3: Aerial and Satellite Image Processing, Favorskaya, N.M. and Jain, C.L., Eds., New York: Springer-Verlag, 2018, vol. 6, pp. 149–175.

    Google Scholar 

  5. Vazaev, A.V., Noskov, V.P., Rubtsov, I.V., and Tsarichenko, S.G., Recognition of objects and types of supporting surface based on data from an integrated vision system, Izv. Yuzh.-Ural. Univ., Tekh. Nauki, 2016, no. 2 (175), pp. 127–139.

  6. Vazaev, A.V., Noskov, V.P., and Rubtsov, I.V., Neural network module for selection of standard for recognition of the types of support surface, Materialy XIV Vserossiiskoi nauchno-prakticheskoi konferentsii “Perspektivnye sistemy i zadachi upravleniya” i X Molodezhnoi shkoly-seminara “Upravlenie i obrabotka informatsii v tekhnicheskikh sistemakh” (Proc. XIV All-Russ. Sci.-Pract. Conf. “Advanced Systems and Control Problems” and X Youth School-Seminar “Control and Processing of Information in Engineering Systems”), Rostov-on-Don: Yuzh. Fed. Univ., 2019, pp. 29–33.

  7. Pervan, B.S. and Parkinson, B.W., Cycle ambiguity estimation for aircraft precision landing using the Global Positioning System, J. Guidance, Control Dyn., 1997, vol. 20, no. 4, pp. 681–689.

    Article  Google Scholar 

  8. Soumekh, M., Automatic aircraft landing using interferometric inverse synthetic aperture radar imaging, IEEE Trans. Image Process., 1996, vol. 5, no. 9, pp. 1335–1345.

    Article  Google Scholar 

  9. Kim, N.V. and Kuznetsov, A.G., Landing automation of a small unmanned aerial vehicle in specific situations, Tr. Mosk. Aviats. Inst., 2011, no. 45. http:// www.trudymai.ru/upload/iblock/790/avtomatizatsiya-protsessa-posadki-malogabaritnogo-bespilotnogo-letatelnogo-apparata-v-osobykh-situatsiyakh.pdf.

  10. Sasa, S., Gomi, H., Ninomiya, T., et al., Position and attitude estimation using image processing of runway, Proc. 38th Aerospace Sciences Meeting and Exhibit AIAA, Reno, 2000. https://doi.org/10.2514/6.2000-301

  11. Kong, W., Zhou, D., Zhang, D., and Zhang, J., Vision-based autonomous landing system for unmanned aerial vehicle: a survey, Proc. IEEE Int. Conf. on Multisensor Fusion and Integration for Intelligent Systems (MFI), Beijing, 2014, pp. 1–8.

  12. Khyun En Mok, Accuracy of the navigation system during automatic landing of a civil aircraft at an unequipped airfield, Cand. Sci. (Eng.) Dissertation, Moscow: Moscow Aviat. Inst., 2004.

  13. Kuznetsov, A.G., Accuracy of estimated coordinates of a small unmanned aerial vehicle using a vision system, Cand. Sci. (Eng.) Dissertation, Moscow: Moscow Aviat. Inst., 2011.

  14. Gageik, N., Strohmeier, M., and Montenegro, S., An autonomous UAV with an optical flow sensor for positioning and navigation, Int. J. Adv. Rob. Syst., 2013, vol. 10, no. 10. https://doi.org/10.5772/56813

  15. Atmeh, G.M., Al-Taq, W.I., and Hasan, Z., A nonlinear automatic landing control system for a UAV, Proc. ASME 2011 Int. Mechanical Engineering Congr. and Exposition, Denver, Co, 2011, no. IMECE2011-63264, pp. 383–392. https://doi.org/10.1115/IMECE2011-63264

  16. Singh, S. and Padhi, R., Automatic path planning and control design for autonomous landing of UAVs using dynamic inversion, Proc. 2009 American Control Conf., St. Louis, 2009, pp. 2409–2414. https://doi.org/10.1109/ACC.2009.5160444

  17. Lee, K., Kriesel, J.M. and Nahum, Gat, Autonomous airborne video-aided navigation, Navigation, 2010, vol. 57, no. 3, pp. 163–173.

    Article  Google Scholar 

  18. Mulyanto, T., Nurhakim, M.L.I., and Muhammad, H., Development of pose estimation system based on dual camera techniques for parameter identification of indoor MAV, Proc. 28th Int. Congr. of the Aeronautical Sciences (ICAS), Brisbane, 2012. https://icas.org/ICAS_ARCHIVE/ICAS2012/PAPERS/662.PDF.

  19. Gorbenko, A., Mornev, M., and Popov, V., The problem of placement of visual landmarks, Proc. 5th Spring/Summer Young Researchers’ Colloquium on Software Engineering (SYRCoSE’2011), Yekaterinburg, 2011, pp. 134–141.

  20. Michaelsen, E., Roschkowski, D., et al., Object-oriented landmark recognition for UAV-navigation, Pattern Recognit. Image Anal., 2011, vol. 21, no. 2, pp. 152–155.

    Article  Google Scholar 

  21. Bodunkov, N.E., Semantic descriptions in the problem of autonomous landing of an unmanned aerial vehicle, Tekh. Zrenie, 2018, no. 1 (12), pp. 21–25.

Download references

Funding

Financial support was provided by the Russian Foundation for Basic Research (project 19-08-00613 А).

Author information

Authors and Affiliations

  1. Moscow Aviation Institute, 125993, Moscow, Russia

    N. E. Bodunkov & N. V. Kim

Authors
  1. N. E. Bodunkov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. N. V. Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to N. E. Bodunkov or N. V. Kim.

Ethics declarations

The authors declare that they have no conflicts of interest.

Additional information

Translated by B. Gilbert

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bodunkov, N.E., Kim, N.V. Autonomous Landing-Site Selection for a Small Drone. Russ. Engin. Res. 41, 72–75 (2021). https://doi.org/10.3103/S1068798X2101007X

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S1068798X2101007X

Keywords:

Search

Navigation