Skip to main content

Advertisement

SpringerLink
Log in

A geometrical path planning method for unmanned aerial vehicle in 2D/3D complex environment

  • Original Research Paper
  • Published:
Intelligent Service Robotics Aims and scope Submit manuscript

Abstract

This paper presents a geometrical path planning method, and it can help unmanned aerial vehicle to find a collision-free path in two-dimensional and three-dimensional (2D and 3D) complex environment quickly. First, a list of tree is designed to describe obstacles, and it is used to query the obstacles which block the line from starting point to finishing point (blocking obstacle). Specially, the list also stores the edge information of blocking obstacle. For the obstacles with short distance, a reasonable way to fly over is studied. Then, a shortest path planning method based on geometrical computation is proposed according to different shapes of obstacles. The obstacles are convex and divided into two cases of 2D and 3D. 2D environment includes rectangular obstacle, trapezoidal obstacle, triangular obstacle, circular obstacle and elliptic obstacle. In 3D, it includes cuboid, sphere and ellipsoid. To compare with other methods, the simulation is made in different environments. In 2D environment with circular obstacles, the method is similar to the artificial potential field. In 2D environment with rectangular obstacles, the performance of the proposed method is better than A-star. Compared with genetic algorithm, the proposed method gives a better result in 3D environment with cuboid obstacles. In 3D environment with hybrid obstacles, it is similar to interfered fluid dynamical system. Through comprehensive comparison and analysis, the conclusion is that the method has good adaptability and does not require grid modeling. It can find a shorter path in 2D/3D complex environment within a short time, so it has the ability of real-time path planning.

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
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Chang YC, Yamamoto Y (2009) Path planning of wheeled mobile robot with simultaneous free space locating capability. Intell Serv Robot 2(1):9–22

    Article  Google Scholar 

  2. Park JH, Huh UY (2016) Path planning for autonomous mobile robot based on safe space. J Electr Eng Technol 11(5):1441–1448

    Article  Google Scholar 

  3. Lefebvre N, Schjølberg I, Utne IB (2016) Integration of risk in hierarchical path planning of underwater vehicles. IFAC-PapersOnLine 49(23):226–231

    Article  Google Scholar 

  4. Zhao J, Liu W (2015) Study on dynamic routing planning A-star algorithm based on cooperative vehicles infrastructure technology. J Comput Inf Syst 11(12):4283–4292

    Google Scholar 

  5. Lissovoi A, Witt C (2015) Runtime analysis of ant colony optimization on dynamic shortest path problems. Theor Comput Sci 561(PA):73–85

    Article  MathSciNet  MATH  Google Scholar 

  6. Mohiuddin MA, Khan SA, Engelbrecht AP (2016) Fuzzy particle swarm optimization algorithms for the open shortest path first weight setting problem. Appl Intell 45(3):598–621

    Article  Google Scholar 

  7. Shorakaei H, Vahdani M, Imani B, Gholami A (2016) Optimal cooperative path planning of unmanned aerial vehicles by a parallel genetic algorithm. Robotica 34(4):823–836

    Article  Google Scholar 

  8. Amer H, Salman N, Hawes M, Chaqfeh M, Mihaylova L, Mayfield M (2016) An improved simulated annealing technique for enhanced mobility in smart cities. Sensors 16(7):1–23

    Article  Google Scholar 

  9. Liang X, Wang H, Li D, Liu C (2014) Three-dimensional path planning for unmanned aerial vehicles based on fluid flow. In: 2014 IEEE aerospace conference, Big Sky, MT, USA, 1–8 March 2014

  10. Liang X, Meng G, Luo H, Chen X (2016) Dynamic path planning based on improved boundary value problem for unmanned aerial vehicle. Clust Comput 19(4):2087–2096

    Article  Google Scholar 

  11. Jeddisaravi K, Alitappeh RJ, Reza J, Pimenta LC, Guimarães FG (2016) Multi-objective approach for robot motion planning in search tasks. Appl Intell 45(2):305–321

    Article  Google Scholar 

  12. Contreras-Cruz MA, Ayala-Ramirez V, Hernandez-Belmonte UH (2015) Mobile robot path planning using artificial bee colony and evolutionary programming. Appl Soft Comput J 30(5):319–328

    Article  Google Scholar 

  13. Lozano-Pérez T, Wesley MA (1979) An algorithm for planning collision-free paths among polyhedral obstacles. Commun ACM 22(10):560–570

    Article  Google Scholar 

  14. Isler V, Kannan S, Khanna S (2005) Randomized pursuit-evasion in a polygonal environment. IEEE Trans Robot 21(5):875–884

    Article  MATH  Google Scholar 

  15. Lubiw A, Snoeyink J, Vosoughpour H (2017) Visibility graphs, dismantlability, and the cops and robbers game. Comput Geom Theory Appl 66:14–27

    Article  MathSciNet  MATH  Google Scholar 

  16. Sun D, Li M (2016) Evaluation function optimization of A-star algorithm in optimal path selection. Revista Tecnica de la Facultad de Ingenieria Universidad del Zulia 39(4):105–111

    Google Scholar 

  17. Ammar A, Bennaceur H, Châari I, Koubâaand A, Alajlan M (2016) Relaxed Dijkstra and A* with linear complexity for robot path planning problems in large-scale grid environments. Soft Comput 20(10):4149–4171

    Article  Google Scholar 

  18. Koceski S, Panov S, Koceska N, Zobel PB, Durante F (2014) A novel quad harmony search algorithm for grid-based path finding. Int J Adv Robot Syst 11(9):1–11

    Article  Google Scholar 

  19. Rajabi-Bahaabadi M, Shariat-Mohaymany A, Babaei M, Ahn CW (2015) Multi-objective path finding in stochastic time-dependent road networks using non-dominated sorting genetic algorithm. Expert Syst Appl 42(12):5056–5064

    Article  Google Scholar 

  20. Saravanakumar S, Asokan T (2013) Multipoint potential field method for path planning of autonomous underwater vehicles in 3D space. Intell Serv Robot 6(4):211–224

    Article  Google Scholar 

  21. Yao P, Wang H, Su Z (2015) UAV feasible path planning based on disturbed fluid and trajectory propagation. Chin J Aeronaut 28(4):1163–1177

    Article  Google Scholar 

  22. Yao P, Wang H, Su Z (2016) Cooperative path planning with applications to target tracking and obstacle avoidance for multi-UAVs. Aerosp Sci Technol 54(1):10–22

    Article  Google Scholar 

  23. Korayem MH, Nekoo SR (2016) The SDRE control of mobile base cooperative manipulators: collision free path planning and moving obstacle avoidance. Robot Auton Syst 86:86–105

    Article  Google Scholar 

  24. Uzol O, Yavrucuk I, Sezer-Uzol N (2010) Panel-method-based path planning and collaborative target tracking for swarming micro air vehicles. J Aircr 47:544–550

    Article  Google Scholar 

  25. Sullivan J, Waydo S, Campbell M (2003) Using stream functions for complex behavior and path generation. In: AIAA-2003-5800

  26. Wang H, Lyu W, Yao P, Liang X, Liu C (2015) Three-dimensional path planning for unmanned aerial vehicle based on interfered fluid dynamical system. Chin J Aeronaut 28(1):229–239

    Article  Google Scholar 

  27. Qureshi AH, Ayaz Y (2016) Potential functions based sampling heuristic for optimal path planning. Auton Robots 40(6):1079–1093

    Article  Google Scholar 

  28. Korayem MH, Hoshiar AK, Nazarahari M (2016) A hybrid co-evolutionary genetic algorithm for multiple nanoparticle assembly task path planning. Int J Adv Manuf Technol 87(9–12):3527–3543

    Article  Google Scholar 

  29. Yao P, Wang H, Su Z (2015) Real-time path planning of unmanned aerial vehicle for target tracking and obstacle avoidance in complex dynamic environment. Aerosp Sci Technol 47(1):269–279

    Article  Google Scholar 

  30. Bircher A, Kamel M, Alexis K, Oleynikova H, Siegwart R (2016) Receding horizon path planning for 3D exploration and surface inspection. Auton Robot 42(2):291–306

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by National Natural Science Foundation of China under Grant 61503255 and 51505470, Aeronautical Science Foundation of China under Grant 2016ZC54011, Natural Science Foundation of Liaoning Province under Grant 2015020063 and Youth Innovation Promotion Association (CAS). The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.

Author information

Authors and Affiliations

  1. School of Automation, Shenyang Aerospace University, South DaoYi Street No.37, ShenBei New District, Shenyang, 110136, Liaoning, China

    Xiao Liang & Guanglei Meng

  2. School of Automation, Northwestern Polytechnical University, Chang’an Campus of Northwest Polytechnical University, Dongxiang Street No.1, Chang’an District, Xi’an, 710129, Shanxi, China

    Yimin Xu

  3. Shenyang Institute of Automation, Chinese Academy of Sciences, Nanta Street No.114, Shenhe District, Shenyang, 110016, Liaoning, China

    Haitao Luo

Authors
  1. Xiao Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guanglei Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yimin Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Haitao Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiao Liang.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liang, X., Meng, G., Xu, Y. et al. A geometrical path planning method for unmanned aerial vehicle in 2D/3D complex environment. Intel Serv Robotics 11, 301–312 (2018). https://doi.org/10.1007/s11370-018-0254-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11370-018-0254-0

Keywords

Search

Navigation