Skip to main content
SpringerLink
Log in

Method for Searching Deployment Zones of Ground Seismic Sensors by a Heterogeneous Group of UAVs in an Environment with a Complex Topography

  • Chapter
  • First Online:
Frontiers in Robotics and Electromechanics

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 329))

  • 277 Accesses

Abstract

In this paper, the problems of automation and robotization of field seismic operations using unmanned aerial vehicles (UAVs) are discussed. An original method of aerial monitoring of terrain by a group of UAVs for the purpose of searching deployment zones for ground seismic sensors by a heterogeneous group of UAVs in an environment with a complex topography is proposed. The method consists of several main stages: the construction of an orthophotomap of the observed terrain using a group of fixed-wing UAVs, obtaining the target scheme of seismic sensors placement indicating the boundaries of potential regions of interest, building a local altitude map for each region of interest using a group of multirotor UAVs, and searching for the zone for seismic sensor placement on each of the given regions of interest. According to the results of the developed method approbation, the averaged over the regions of interest share of the successful missions of searching deployment zones for seismic sensors was 0.746, which illustrates the high efficiency of the proposed solution. In addition, the work revealed an inverse dependence of the share of successful searching results on the altitude of the UAV during the flight mission.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover 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. Naugolnov, M., Bozic, M., Bogatyrev, I., Kultysheva, K.: Using of UAVs and computer vision for design and supervisory control of seismic survey works. Prog. 21 Euro. Assoc. Geosci. Eng. 2021(1), 1–5 (2021)

    Google Scholar 

  2. Grigoriev, G., Gulin, V., Nikitin, A., Sivoy, N., Bondarev, E., Islamuratov, M., Zakharova, O., Karpov, I., Liubimov, E., Votsalevskiy, V.: Integrated droneborne geophysics application as a tool for exploration optimization. In: Case Studies. SPE Annual Technical Conference and Exhibition. OnePetro (2021)

    Google Scholar 

  3. Ebadi, F., Norouzi, M.: Road Terrain detection and classification algorithm based on the color feature extraction. In: Artificial Intelligence and Robotics (IRANOPEN), pp. 139–146 (2017)

    Google Scholar 

  4. Bugaev, A.S., Antonov, A.N., Agafonov, B.M., Belotelov, K.S., Vergeles, S.S., Dudkin, P.V., Egorov, E.V., Egorov, I.V., Zhevnenko, D.A., Zhabin, S.N., Zaitsev, D.L.: Measuring devices based on molecular-electronic transducers. J. Commun. Technol. Electron. 63(12), 1339–1351 (2018). https://doi.org/10.1134/S1064226918110025

    Article  Google Scholar 

  5. Egorov, I.V., Shabalina, A.S., Agafonov, V.M.: Design and self-noise of MET closed-loop seismic accelerometers. IEEE Sens. J. 17(7), 2008–2014 (2017). https://doi.org/10.1109/JSEN.2017.2662207

    Article  Google Scholar 

  6. Chikishev, D.A., Zaitsev, D.L., Belotelov, K.S., Egorov, I.V.: The temperature dependence of amplitude-frequency response of the MET sensor of linear motion in a broad frequency range. IEEE Sens. J. 19(21), 9653–9661 (2019). https://doi.org/10.1109/JSEN.2019.2927859

  7. Zaitsev, D., Egorov, I., Agafonov, V.: A Comparative study of aqueous and non-aqueous solvents to be used in low-temperature serial molecular-electronic sensors. Chemosensors 10(3), 111 (2022). https://doi.org/10.3390/chemosensors10030111

    Article  Google Scholar 

  8. Galceran, E., Carreras, M.: A survey on coverage path planning for robotics. Robot. Auton. Syst. 61(12), 1258–1276 (2013)

    Article  Google Scholar 

  9. Darintsev, O., Migranov, A.: Analytical review of approaches to the distribution of tasks for mobile robot teams based on soft computing technologies. Inf. Autom. 21(4), 729–757 (2022). https://doi.org/10.15622/ia.21.4.4

  10. Torres, M., Pelta, D.A., Verdegay, J.L., Torres, J.C.: Coverage path planning with unmanned aerial vehicles for 3D terrain reconstruction. Expert Syst. Appl. 55, 441–451 (2016)

    Article  Google Scholar 

  11. Choset, H.: Coverage for robotics–a survey of recent results. Ann. Math. Artif. Intell. 31(1), 113–126 (2001)

    Article  MATH  Google Scholar 

  12. Acevedo, J.J., Arrue, B.C., Maza, I., Ollero, A.: Distributed approach for coverage and patrolling missions with a team of heterogeneous aerial robots under communication constraints. Int. J. Adv. Rob. Syst. 10(1), 28 (2013)

    Article  Google Scholar 

  13. Albani, D., Nardi, D., Trianni, V.: Field coverage and weed mapping by UAV swarms. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4319–4325 (2017)

    Google Scholar 

  14. Pshikhopov, V., Medvedev, M., Kostjukov, V., Houssein, F., Kadhim, A.: Trajectory planning algorithms in two-dimensional environment with obstacles. Inf. Autom. 21(3), 459–492 (2022). https://doi.org/10.15622/ia.21.3.1

  15. Huang, W.H.: Optimal line-sweep-based decompositions for coverage algorithms. In: Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No. 01CH37164), vol. 1, pp. 27–32 (2001)

    Google Scholar 

  16. Li, Y., Chen, H., Er, M.J., Wang, X.: Coverage path planning for UAVs based on enhanced exact cellular decomposition method. Mechatronics 21(5), 876–885 (2011)

    Article  Google Scholar 

  17. Xu, A., Viriyasuthee, C., Rekleitis, I.: Efficient complete coverage of a known arbitrary environment with applications to aerial operations. Auton. Robot. 36(4), 365–381 (2014)

    Article  Google Scholar 

  18. Toth, P., Vigo, D.: The Vehicle Routing Problem. Society for Industrial and Applied Mathematics, Philadelphia, PA, USA (2002)

    Book  MATH  Google Scholar 

  19. Kivelevitch, E., Sharma, B., Ernest, N., Kumar, M., Cohen, K.: A hierarchical market solution to the min–max multiple depots vehicle routing problem. Unmanned Syst. 2(01), 87–100 (2014)

    Article  Google Scholar 

  20. Zhang, Z.: Microsoft kinect sensor and its effect. IEEE Multimedia 19(2), 4–10 (2012)

    Article  Google Scholar 

  21. Fernald, F.G.: Analysis of atmospheric lidar observations: some comments. Appl. Opt. 23(5), 652–653 (1984)

    Article  Google Scholar 

  22. Keselman, L., Iselin Woodfill, J., Grunnet-Jepsen, A., Bhowmik, A.: Intel realsense stereoscopic depth cameras. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1–10 (2017)

    Google Scholar 

  23. Eigen, D., Fergus, R.: Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2650–2658 (2015)

    Google Scholar 

  24. Laina, I., Rupprecht, C., Belagiannis, V., Tombari, F., Navab, N.: Deeper depth prediction with fully convolutional residual networks. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 239–248 (2016)

    Google Scholar 

  25. Yang Z.L., Guo B.L.: Image mosaic based on SIFT. In: 2008 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 1422–1425 (2008)

    Google Scholar 

  26. Se S., Lowe D., Little J.: Vision-based mobile robot localization and mapping using scale-invariant features. In: Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No. 01CH37164), vol. 2, pp. 2051–2058 (2001)

    Google Scholar 

  27. Kazanin, A.G., Kuoma, D.G., Bazilevich, S.O., Chizhikov, A.A., Prilipko, S.A., Lantsev, V.V., Demonov, A.P., Litvachuk, A.V., Lukovnikov, G.G., Ziborov, A.V., Dolotkazin, I.N., Koshelev, E.A., Petrov, B.E., Yerofeev, Yu.G., Agafonov, V.M.: Practical experience to operate molecular geophones in “crab” sea bottom recorders. Oil. Gas. Innov. 10(251), 23–26 (2021)

    Google Scholar 

  28. Izmailov, A.F., Solodov, M.V.: Numerical optimization methods (2003)

    Google Scholar 

  29. Gladkov, L.A., Kureychik, V.V., Kureychik, V.M.: Genetic algorithms (2010)

    Google Scholar 

  30. Gazebo. https://gazebosim.org/home. 10 Jan 2022

  31. Intel RealSense Gazebo ROS plugin. https://github.com/pal-robotics/realsense_gazebo_plugin. 10 Jan 2022

  32. Using Gazebo Simulator with SITL. https://ardupilot.org/dev/docs/using-gazebo-simulator-with-sitl.html. 10 Jan 2022

  33. Shchelkunov, A.E., Kovalev, V.V., Morev, K.I., Sidko, I.V.: The metrics for tracking algorithms evaluation. Izvestiya SFedU. Eng. Sci. 1(211), 233–245 (2020)

    Google Scholar 

Download references

Acknowledgements

This research is supported by RSF project No. 22-69-00231, https://rscf.ru/project/22-69-00231/.

Author information

Authors and Affiliations

  1. St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, 39, 14Th Line, 199178, St. Petersburg, Russia

    Roman Iakovlev, Valeria Lebedeva & Andrey Ronzhin

  2. LLC R-Sensors, 4/1, Likhachevsky Proezd, Dolgoprudny, 141701, Moscow Region, Russia

    Ivan Egorov

  3. Immanuel Kant Baltic Federal University (IKBFU), 14, Aleksandra Nevskogo Str., 236041, Kaliningrad, Russia

    Vitaly Bryksin

Authors
  1. Roman Iakovlev
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Valeria Lebedeva
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ivan Egorov
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vitaly Bryksin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Andrey Ronzhin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Valeria Lebedeva .

Editor information

Editors and Affiliations

  1. St. Petersburg Federal Research Center of the Russian Academy of Sciences, St. Petersburg, Russia

    Andrey Ronzhin

  2. Research Institute of Robotics and Control Systems, Southern Federal University, Rostov-on-Don, Russia

    Viacheslav Pshikhopov

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Iakovlev, R., Lebedeva, V., Egorov, I., Bryksin, V., Ronzhin, A. (2023). Method for Searching Deployment Zones of Ground Seismic Sensors by a Heterogeneous Group of UAVs in an Environment with a Complex Topography. In: Ronzhin, A., Pshikhopov, V. (eds) Frontiers in Robotics and Electromechanics. Smart Innovation, Systems and Technologies, vol 329. Springer, Singapore. https://doi.org/10.1007/978-981-19-7685-8_22

Download citation

Publish with us

Policies and ethics

Search

Navigation