Skip to main content
Springer Nature Link
Log in

Aerial Reconnaissance and Ground Robot Terrain Learning in Traversal Cost Assessment

  • Conference paper
  • First Online:
Modelling and Simulation for Autonomous Systems (MESAS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11995))

Abstract

In this paper, we report on the developed system for assessment of ground unit terrain traversal cost using aerial reconnaissance of the expected mission environment. The system combines an aerial vehicle with ground robot terrain learning in the traversal cost modeling utilized in the mission planning for ground units. The aerial vehicle is deployed to capture visual data used to build a terrain model that is then used for the extraction of the terrain features of the expected operational area of the ground units. Based on the previous traversal experience of the ground units in similar environments, the learned model of the traversal cost is employed to predict the traversal cost of the new expected operational area to plan a cost-efficient path to visit the desired locations of interest. The particular modules of the system are demonstrated in an experimental scenario combining the deployment of an unmanned aerial vehicle with a multi-legged walking robot used for learning the traversal cost model.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Alatartsev, S., Stellmacher, S., Ortmeier, F.: Robotic task sequencing problem: a survey. J. Intell. Robot. Syst. 80(2), 279–298 (2015). https://doi.org/10.1007/s10846-015-0190-6

    Article  Google Scholar 

  2. Belter, D., Wietrzykowski, J., Skrzypczyński, P.: Employing natural terrain semantics in motion planning for a multi-legged robot. J. Intell. Rob. Syst. 93(3), 723–743 (2018). https://doi.org/10.1007/s10846-018-0865-x

    Article  Google Scholar 

  3. Brunner, M., Brüggemann, B., Schulz, D.: Rough terrain motion planning for actuated, tracked robots. In: Filipe, J., Fred, A. (eds.) ICAART 2013. CCIS, vol. 449, pp. 40–61. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44440-5_3

    Chapter  Google Scholar 

  4. Felizardo, L.F., Mota, R.L., Shiguemori, E.H., Neves, M.T., Ramos, A.B., Mora-Camino, F.: Using ANN and UAV for terrain surveillance. In: International Conference on Hybrid Intelligent Systems (HIS), pp. 1–5 (2014). https://doi.org/10.1109/HIS.2013.6920414

  5. Gutin, G., Punnen, A.P. (eds.): The Traveling Salesman Problem and Its Variations. Springer, Boston (2007). https://doi.org/10.1007/b101971

    Book  MATH  Google Scholar 

  6. Helsgaun, K.: An effective implementation of the Lin-Kernighan traveling salesman heuristic. Eur. J. Oper. Res. 126(1), 106–130 (2000)

    Article  MathSciNet  Google Scholar 

  7. Helsgaun, K.: LKH solver 2.0.9. http://www.akira.ruc.dk/~keld/research/LKH. Accessed 29 Aug 2019

  8. Hudjakov, R., Tamre, M.: Aerial imagery terrain classification for long-range autonomous navigation. In: International Symposium on Optomechatronic Technologies, pp. 88–91 (2009). https://doi.org/10.1109/ISOT.2009.5326104

  9. Kohonen, T.: The self-organizing map. Proc. IEEE 78(9), 1464–1480 (1990). https://doi.org/10.1109/5.58325

    Article  Google Scholar 

  10. Kottege, N., Parkinson, C., Moghadam, P., Elfes, A., Singh, S.P.N.: Energetics-informed hexapod gait transitions across terrains. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 5140–5147. IEEE (2015). https://doi.org/10.1109/ICRA.2015.7139915

  11. LWPR library (2007). https://github.com/jdlangs/lwpr. Accessed 28 May 2019

  12. Prágr, M., Čížek, P., Bayer, J., Faigl, J.: Online incremental learning of the terrain traversal cost in autonomous exploration. In: Robotics: Science and Systems (RSS) (2019). https://doi.org/10.15607/RSS.2019.XV.040

  13. Prágr, M., Čížek, P., Faigl, J.: Cost of transport estimation for legged robot based on terrain features inference from aerial scan. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1745–1750 (2018). https://doi.org/10.1109/IROS.2018.8593374

  14. Prágr, M., Čížek, P., Faigl, J.: Incremental learning of traversability cost for aerial reconnaissance support to ground units. In: Mazal, J. (ed.) MESAS 2018. LNCS, vol. 11472, pp. 412–421. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-14984-0_30

    Chapter  Google Scholar 

  15. Rossi, G., Tanteri, L., Tofani, V., Vannocci, P., Moretti, S., Casagli, N.: Multitemporal UAV surveys for landslide mapping and characterization. Landslides 15(5), 1045–1052 (2018). https://doi.org/10.1007/s10346-018-0978-0

    Article  Google Scholar 

  16. Sofman, B., Lin, E., Bagnell, J.A., Cole, J., Vandapel, N., Stentz, A.: Improving robot navigation through self-supervised online learning. J. Field Robot. 23(11–12), 1059–1075 (2006). https://doi.org/10.1002/rob.20169

    Article  Google Scholar 

  17. Soleimani, B., Ashtiani, M.Z., Soleimani, B.H., Moradi, H.: A Disaster invariant feature for localization. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1096–1101 (2010). https://doi.org/10.1109/IROS.2010.5651930

  18. Stelzer, A., Hirschmüller, H., Görner, M.: Stereo-vision-based navigation of a six-legged walking robot in unknown rough terrain. Int. J. Robot. Res. 31(4), 381–402 (2012). https://doi.org/10.1177/0278364911435161

    Article  Google Scholar 

  19. Vijayakumar, S., Schaal, S.: Locally weighted projection regression: an o(n) algorithm for incremental real time learning in high dimensional space. In: International Conference on International Conference on Machine Learning (ICML), pp. 1079–1086 (2000)

    Google Scholar 

Download references

Acknowledgement

The presented work has been supported under the OP VVV funded project CZ.02.1.01/0.0/0.0/16_019/0000765 “Research Center for Informatics”. The support under grant No. SGS19/176/OHK3/3T/13 to Miloš Prágr and Petr Váňa is also gratefully acknowledged.

Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering, Czech Technical University in Prague, Technicka 2, 166 27, Prague, Czech Republic

    Miloš Prágr, Petr Váňa & Jan Faigl

Authors
  1. Miloš Prágr
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Petr Váňa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jan Faigl
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Miloš Prágr .

Editor information

Editors and Affiliations

  1. NATO Modelling and Simulation Centre of Excellence, Rome, Italy

    Jan Mazal

  2. MIRPA Lab, University of Palermo, Palermo, Italy

    Adriano Fagiolini

  3. Faculty of Mechanical Engineering, Brno University of Technology, Brno, Czech Republic

    Petr Vasik

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Prágr, M., Váňa, P., Faigl, J. (2020). Aerial Reconnaissance and Ground Robot Terrain Learning in Traversal Cost Assessment. In: Mazal, J., Fagiolini, A., Vasik, P. (eds) Modelling and Simulation for Autonomous Systems. MESAS 2019. Lecture Notes in Computer Science(), vol 11995. Springer, Cham. https://doi.org/10.1007/978-3-030-43890-6_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-43890-6_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-43889-0

  • Online ISBN: 978-3-030-43890-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics