Skip to main content

Trends in Development of UAV-UGV Cooperation Approaches in Precision Agriculture

  • Conference paper
  • First Online:
Book cover Interactive Collaborative Robotics (ICR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11097))

Included in the following conference series:

Abstract

Multiple unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) heterogeneous cooperation provides a new breakthrough for the effective applications. UGV is generally capable of operating outdoors and over a wide variety of terrain, functioning in place of humans. Multiple UAVs can be used to cover large areas searching for targets. However, sensors on UAVs are typically limited in operating airspeed and altitude, combined with attitude uncertainty, placing a lower limit on their ability to resolve and localize ground features. UGVs on the other hand can be deployed to accurately locate ground targets, but they have the disadvantage of not being able to move rapidly or see through such obstacles as buildings or fences. Analysis of the tasks of existing UAVs in the field of agriculture is presented and main tasks of UGV in context UAV-UGV cooperation are considered.

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. Bechar, A., Vigneault, C.: Agricultural robots for field operations: Concepts and components. Biosys. Eng. 149, 94–111 (2016)

    Article  Google Scholar 

  2. Wolfert, S., Ge, L., Verdouwa, C., Bogaardt, M.J.: Big Data in smart farming – a review. Agric. Syst. 153, 69–80 (2017)

    Article  Google Scholar 

  3. Perez-Ruız, M., Slaughter, D.C., Fathallah, F.A., Gliever, C.J., Miller, B.J.: Co-robotic intra-row weed control system. Biosys. Eng. 126, 45–55 (2014)

    Article  Google Scholar 

  4. Holloway, L., Bear, C., Wilkinson, K.: Re-capturing bovine life: Robot-cow relationships, freedom and control in dairy farming. J. Rural Stud. 33, 131–140 (2014)

    Article  Google Scholar 

  5. Afanas’ev, R.A., Ermolov, I.L.: Future of robots for precision agriculture. Mechatron. Autom. Manage. 12, 828–833 (2016)

    Google Scholar 

  6. Sidorova, V.A., Zhukovsky, E.E., Lekomtsev, P.V., Yakushev, V.V.: Geostatistical analysis of soil characteristics and productivity in the field experiment on precise agriculture. Agrochemistry Fertil. Soils 8, 879–888 (2012)

    Google Scholar 

  7. Sampedro, C.: A flexible and dynamic mission planning architecture for UAV swarm coordination. In: 2016 International Conference on Unmanned Aircraft Systems (ICUAS), Arlington, pp. 355–363 (2016). https://doi.org/10.1109/ICUAS.2016.7502669

  8. Caraballo, L.E. et al.: The block-sharing strategy for area monitoring missions using a decentralized multi-UAV system. In: 2014 International Conference on Unmanned Aircraft Systems (ICUAS), Orlando, FL, pp. 602–610 (2014). https://doi.org/10.1109/ICUAS.2014.6842303

  9. Fortmann, F., Muller, H., Ludtke, A., and Boll, S.: Expert-based design and evaluation of an ambient light display to improve monitoring performance during multi-UAV supervisory control. In: 2015 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision, Orlando, FL, pp. 28–34 (2015)

    Google Scholar 

  10. Mingguo, Z., Chengdong, W., and Dongyue, C.: UAV image identification in urban region satellite image using global feature and local feature. In: 2016 Chinese Control and Decision Conference (CCDC), Yinchuan, pp. 5377–5382 (2016). https://doi.org/10.1109/CCDC.2016.7531959

  11. Chen, T., Li, X., Cong, Y., Qian, S.: UAV formation visual navigation algorithm based on determination sampling type filter. In: 12th IEEE International Conference on Electronic Measurement & Instruments (ICEMI 2015), Qingdao, pp. 747–752 (2015). https://doi.org/10.1109/ICEMI.2015.7494322

  12. Ziyang, Z., Qiushi, H., Chen, G., Ju, J.: Information fusion distributed navigation for UAVs formation flight. In: Proceedings of 2014 IEEE Chinese Guidance, Navigation and Control Conference, Yantai, pp. 1520–1525 (2014). https://doi.org/10.1109/cgncc.2014.7007417

  13. Wargo, C.A., Church, G.C., Glaneueski, J., Strout, M.: Unmanned aircraft systems (UAS) research and future analysis. In: 2014 IEEE Aerospace Conference, 2014, pp. 1–16 (2014). https://doi.org/10.1109/AERO.2014.6836448

  14. Langerwisch, M., Wittmann, T., Thamke, S., Remmersmann, T., Tiderko, A., Wagner, B.: Heterogeneous teams of unmanned ground and aerial robots for reconnaissance and surveillance - a field experiment. In: IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR 2013), pp. 1–6 (2013). https://doi.org/10.1109/SSRR.2013.6719320

  15. Harik, E.H.C., Guérin, F., Guinand, F., Brethé, J.F., Pelvillain, H.: UAVUGV cooperation for objects transportation in an industrial area. In: IEEE International Conference on Industrial Technology (ICIT 2015), pp. 547–552 (2015). https://doi.org/10.1109/ICIT.2015.7125156

  16. Tokekar, P., Hook, J.V., Mulla, D., and Isler, V.: Sensor planning for a symbiotic UAV and UGV system for precision agriculture, 6(32), 1498–1511 (2016). https://doi.org/10.1109/TRO.2016.2603528

  17. Hui, C., Yousheng, C., Xiaokun, L., Shing, W.W.: Autonomous takeoff, tracking and landing of a UAV on a moving UGV using onboard monocular vision. In: Proceedings of the 32nd Chinese Control Conference, pp. 5895–5901 (2013)

    Google Scholar 

  18. Ghamry, K.A., Dong, Y., Kamel, M.A., Zhang Y.: Real-time autonomous take-off, tracking and landing of UAV on a moving UGV platform. In: 24th Mediterranean Conference on Control and Automation (MED 2016), pp. 1236–1241 (2016). https://doi.org/10.1109/MED.2016.7535886

  19. Fu, M., Zhang, K., Yi, Y., and Shi, C.: Autonomous landing of a quadrotor on an UGV. In: IEEE International Conference on Mechatronics and Automation, pp. 988–993 (2016). https://doi.org/10.1109/ICMA.2016.7558697

  20. Marchini, B.D.: Adaptive control techniques for transition to hover flight of fixed-wing UAVs (Master’s thesis). California Polytechnic State University (2013)

    Google Scholar 

  21. Guzey, H.M.: Adaptive consensus-based formation control of fixed-wing MUAV’s. In: Proceedings of IEEE 4th International Conference on Actual Problems of Unmanned Aerial Vehicles Developments, APUAVD 2017, pp. 184–187 (2018)

    Google Scholar 

  22. Andreev, V.P., Pletenev, P.F.: Method of information interaction for distributed control systems of robots with modular architecture. SPIIRAS Proc. 57(2), 134–160 (2018). https://doi.org/10.15622/sp.57.6

    Article  Google Scholar 

  23. Vatamaniuk, I., Panina, G., Saveliev, A., Ronzhin, A.: Convex shape generation by robotic swarm. In: Proceedings of 2016 International Conference on Autonomous Robot Systems and CoПmpetitions, ICARSC 2016. pp. 300–304 (2016). https://doi.org/10.1109/icarsc.2016.33

  24. Jokisch, O., Huber, M.: Advances in the development of a cognitive user interface. In: 13th International Conference on Electromechanics and Robotics “Zavalishin’s Readings”, ER(ZR) 2018. MATEC Web of Conferences. vol. 161, paper 1003, (2018). https://doi.org/10.1051/matecconf/201816101003

  25. Strutz, T., Leipnitz, A.: Adaptive colour-space selection in high efficiency video coding. In: 25th European Signal Processing Conference, EUSIPCO 2017, pp. 1534–1538 (2017)

    Google Scholar 

  26. Vatamaniuk, I., Levonevskiy, D., Saveliev, A., Denisov, A.: Scenarios of multimodal information navigation services for users in cyberphysical environment. In: Ronzhin, A., Potapova, R., Németh, G. (eds.) SPECOM 2016. LNCS (LNAI), vol. 9811, pp. 588–595. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-43958-7_71

    Chapter  Google Scholar 

  27. Rakhmanenko, I.A., Meshcheryakov, R.V.: Identification features analysis in speech data using GMM-UBM speaker verification system. SPIIRAS Proc. 3(52), 32–50 (2017)

    Article  Google Scholar 

  28. Levonevskiy, D., Vatamaniuk, I., Saveliev, A.: Integration of corporate electronic services into a smart space using temporal logic of actions. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds.) ICR 2017. LNCS (LNAI), vol. 10459, pp. 134–143. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66471-2_15

    Chapter  Google Scholar 

  29. Ivanko, D., et al.: Using a high-speed video camera for robust audio-visual speech recognition in acoustically noisy conditions. In: Karpov, A., Potapova, R., Mporas, I. (eds.) SPECOM 2017. LNCS (LNAI), vol. 10458, pp. 757–766. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66429-3_76

    Chapter  Google Scholar 

  30. Pakoci, E., Popović, B., Pekar, D.J.: Improvements in Serbian speech recognition using sequence-trained deep neural networks. SPIIRAS Proc. 58(3), 53–76 (2018). https://doi.org/10.15622/sp.58.3

    Article  Google Scholar 

Download references

Acknowledgments

This work is partially supported by the Russian Foundation for Basic Research (grant № 18-58-76001_ERA.Net) in the framework of the ERA.Net Plus Project 99-HARMONIC.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Quyen Vu .

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

Vu, Q., Raković, M., Delic, V., Ronzhin, A. (2018). Trends in Development of UAV-UGV Cooperation Approaches in Precision Agriculture. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds) Interactive Collaborative Robotics. ICR 2018. Lecture Notes in Computer Science(), vol 11097. Springer, Cham. https://doi.org/10.1007/978-3-319-99582-3_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-99582-3_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99581-6

  • Online ISBN: 978-3-319-99582-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics