Skip to main content

Construction of a Three-Dimensional UAV Movement Planner When the Latter Moves in Conditions of Difficult Terrain

  • Conference paper
  • First Online:
Interactive Collaborative Robotics (ICR 2023)

Abstract

The known methods of planning the routes of movement of robotic platforms based on cellular decomposition of the area of movement in a three-dimensional formulation are severely limited in speed. Therefore, the construction of high-speed planning algorithms in a three-dimensional mapped environment is an urgent task. This article proposes a method for planning the movement of robotic platforms in this environment, combining the use of the well-known Dijkstra algorithm for constructing a two-dimensional projection curve with subsequent projection reconstruction and multi-stage correction of the target spatial piecewise polyline curve. The restoration of the original spatial curve by its two-dimensional projection onto the horizontal plane is performed on the basis of a given discrete elevation map of the motion area, and the specified adjustment is made taking into account the requirements, firstly, the minimality of the total length of the final piecewise polyline, and, secondly, taking into account the specified known kinematic limitations of the apparatus. The algorithm for the synthesis of a spatial curve is detailed for the common case when obstacles are represented in the form of rectangular cylinders with polygonal generators. The effectiveness of the developed global scheduler algorithm is confirmed by the results of numerical modeling.

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 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 74.99
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. Hart, P.E., Nilsson, N.J., Raphael, B.A.: Formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybernet. 4(2), 100–107 (1968)

    Article  Google Scholar 

  2. Piskorsky, D.S., Abdullin, F.H., Nikolaeva, A.R.: Optimization of the A-star path planning algorithm. Bull. SUSU. Comput. Technol. Control, Radio Electron. 20(1), 154–160 (2020). (In Russ.)

    Google Scholar 

  3. Stentz, A.: Optimal and efficient path planning for partially known environments. In: Intelligent Unmanned Ground Vehicles. The Springer International Series in Engineering and Computer Science, vol. 388, pp. 203–220 (1997)

    Google Scholar 

  4. Wang, Q., Hao, Y., Chen, F.: Deepening the IDA* algorithm for knowledge graph reasoning through neural network architecture. Neurocomputing 429, 101–109 (2021)

    Article  Google Scholar 

  5. Zhou, R., Hansen, E.A.: Memory-bounded {A*} graph search. In: The Florida AI Research Society Conference (FLAIRS), pp. 203–209 (2002)

    Google Scholar 

  6. Holte, R., Perez, M., Zimmer, R., MacDonald, A.: Hierarchical A*: searching abstraction hierarchies efficiently. In: AAAI/IAAI, vol. 1, pp. 530–535 (1996)

    Google Scholar 

  7. Liu, B., Xiao, X., Stone, P.: A lifelong learning approach to mobile robot navigation. IEEE Robot. Autom. Lett. 6(2), 1090–1096 (2021)

    Article  Google Scholar 

  8. Chen, B.Y., Chen, X.W., Chen, H.P., Lam, W.H.: Efficient algorithm for finding k shortest paths based on re-optimization technique. Transp. Res. Part E: Logistics Transp. Rev. 133, 101819 (2020)

    Article  Google Scholar 

  9. Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. Int. J. Robot. Res. 5(1), 90–98 (1986)

    Article  Google Scholar 

  10. Platonov, A.K., Karpov, I.I., Kirilchenko, A.A.: The method of potentials in the problem of laying a route. M.: Preprint of the Institute of Applied Mathematics of the USSR Academy of Sciences, p. 27 (1974). (In Russ)

    Google Scholar 

  11. Filimonov, A.B., Filimonov, N.B.: Issues of motion control of mobile robots based on the potential guidance method. Mechatron. Autom. Control 20(11), 677–685 (2019). (In Russ.)

    Google Scholar 

  12. Kostyukov, V.A., Medvedev, M., Pshikhopov, V.H.: Planning the movement of ground robots in an environment with obstacles: an algorithm for constructing smoothed individual trajectories. Mechatron. Autom. Control 24(1), 33–45 (2022). (In Russ.)

    Google Scholar 

  13. Pshikhopov, V.K.H., et al.: Path planning for vehicles operating in uncertain 2D environments. Elsevier, Butterworth-Heinemann, p. 312 (2017)

    Google Scholar 

  14. Pshikhopov, V., Medvedev, M.: Decentralized management of a group of homogeneous moving objects in a two-dimensional environment with obstacles. Mechatron. Autom. Control 17(5), 346–353 (2016). (In Russ.)

    Google Scholar 

  15. Pshikhopov, V., Medvedev, M.: Group control of the movement of mobile robots in an uncertain environment using unstable modes. Comput. Sci. Autom. 5(60), 39–63 (2018)

    Google Scholar 

  16. Gaiduk, A.R., Martyanov, O.V., Medvedev, M., Pshikhopov, V.H., Hamdan, N., Farhud, A.: Neural network control system for a group of robots in an uncertain two-dimensional environment. Mechatron. Autom. Control 21(8), 470–479 (2020). (In Russ.)

    Google Scholar 

  17. Nazarahari, M., Khanmirza, E., Doostie, S.: Multi-objective multi-robot path planning in continuous environment using an enhanced genetic algorithm. Expert Syst. Appl. 115, 106–120 (2019)

    Article  Google Scholar 

  18. Hoy, M., Matveev, A.S., Savkin, A.V.: Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey. Robotica 33(3), 463–497 (2015)

    Article  Google Scholar 

  19. Shlyakhov, N.E., Vatamaniuk, I.V., Ronzhin, A.L.: Review of the methods and algorithms of a robot swarm aggregation. Mechatron. Autom. Control 18(1), 22–29 (2017)

    Google Scholar 

  20. Sapronov, L., Lacaze, A.: Path planning for robotic vehicles using generalized Field D. In: Unmanned Systems Technology X, vol. 6962, pp. 447–458. SPIE (2008)

    Google Scholar 

  21. Grigor’ev, M.I., Malozemov, V.N., Sergeev, A.N.: Bernstein polynomials and composite Bézier curves. Comput. Math. Math. Phys. 46, 1872–1881 (2006)

    Google Scholar 

Download references

Acknowledgements

The study was supported by the Russian Science Foundation Grant No. 22-29-00370, https://rscf.ru/project/22-29-00370/.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Vladimir Kostyukov .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kostyukov, V., Evdokimov, I., Gissov, V. (2023). Construction of a Three-Dimensional UAV Movement Planner When the Latter Moves in Conditions of Difficult Terrain. In: Ronzhin, A., Sadigov, A., Meshcheryakov, R. (eds) Interactive Collaborative Robotics. ICR 2023. Lecture Notes in Computer Science(), vol 14214. Springer, Cham. https://doi.org/10.1007/978-3-031-43111-1_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-43111-1_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43110-4

  • Online ISBN: 978-3-031-43111-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics