Skip to main content

A Survey of Cooperative Path Planning for Multiple UAVs

  • Conference paper
  • First Online:
Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021) (ICAUS 2021)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 861))

Included in the following conference series:

  • 237 Accesses

Abstract

Cooperative mission for Multi UAVs has become an important trend. Cooperative path planning is one of the most killing part for the issue. The algorithms can be divided into the following categories, including optimal algorithm, graph-theory-based planning method, heuristic-information-based planning algorithm, swarm intelligence algorithm and neural network algorithm and so on. This paper systematically presents the research status and trend of the application of path planning in the field of multi-UAV cooperative mission and selects a variety of classic and up-to-date algorithms and their variants to promote future research.

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 549.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 699.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 699.99
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. Okumura, K., Tamura, Y., Défago, X.: winPIBT: extended prioritized algorithm for iterative multi-agent path finding (2019)

    Google Scholar 

  2. Yuan, L., Xia, J., Chen, Z.: Review of cooperative path planning for multiple UAVs. Flight Mech. 05, 1–5 (2009)

    Google Scholar 

  3. Zou, Z.: Research on trajectory planning model and algorithm of anti-ship missile based on dynamic programming. Wuhan University of Technology (2019)

    Google Scholar 

  4. Zhao, X.: Optimal route planning based on MILP. J. Chin. Acad. Electron. Sci. 10(002), 150–155 (2015)

    Google Scholar 

  5. Papen, A., Vandenhoeck, R., Bolting, J., Defay, F.: Collision-free rendezvous maneuvers for formations of unmanned aerial vehicles. IFAC Papers On Line 50(1), 282–289 (2017)

    Article  Google Scholar 

  6. Kaluđer, H., Brezak, M., Petrović, I.: A visibility graph-based method for path planning in dynamic environments. In: 2011 Proceedings of the 34th International Convention MIPRO IEEE, Croatia, no. 05, pp. 23–27 (2011)

    Google Scholar 

  7. Niu, H., Savvaris, A., Tsourdos, A., Ji, Z.: Voronoi-visibility roadmap-based path planning algorithm for unmanned surface vehicles. J. Navig. 72(4), 1–25 (2019)

    Article  Google Scholar 

  8. Özcan, M., Yaman, U.: A continuous path planning approach on Voronoi diagrams for robotics and manufacturing applications. Procedia Manuf. 38, 1–8 (2019)

    Article  Google Scholar 

  9. Albanis, G., Zioulis, N., Dimou, A., Zarpalas, D., Daras, P.: DronePose: photorealistic UAV-assistant dataset synthesis for 3D pose estimation via a smooth silhouette loss. In: Bartoli, A., Fusiello, A. (eds.) ECCV 2020. LNCS, vol. 12536, pp. 663–681. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-66096-3_44

    Chapter  Google Scholar 

  10. Khatib, O.: Real time obstacle avoidance for manipulators and mobile robots. Int. J. Robot. Res. 5(1), 90–99 (1986)

    Article  Google Scholar 

  11. Deng, Y., Jiang, X.: Trajectory planning algorithm of four rotor UAV based on improved artificial potential field method. Sens. Microsyst. 40(07), 130–133 (2021)

    Google Scholar 

  12. Li, Y.: Formation tracking and obstacle avoidance based on improved PSO and artificial potential field method. Int. Core J. Eng. 7(7), 117–124 (2021)

    Google Scholar 

  13. Imrane, M.L., Melingui, A., Mvogo Ahanda, J.J.B., Biya Motto, F., Merzouki, R.: Artificial potential field neuro-fuzzy controller for autonomous navigation of mobile robots. Proc. Inst. Mech. Eng. 235(7), 1179–1192 (2021)

    Google Scholar 

  14. Maini, P., Sujit, P.B.: Path planning for a UAV with kinematic constraints in the presence of polygonal obstacles. In: International Conference on Unmanned Aircraft Systems, Arlington, VA, USA, pp. 62–67. IEEE (2016)

    Google Scholar 

  15. Ningyi, C., Zhiqian, L., Yuqi, L.: A route planning algorithm based on Dijkstra for intelligent aircraft under multiple constraints. J. Northw. Univ. Technol. 38(06), 1284–1290 (2020)

    Google Scholar 

  16. Szczerba, R.J.: Using framed-octrees to find conditional shortest paths in an unknown 3-D environment (2010)

    Google Scholar 

  17. Liang, L.U., Wang, J., Zong, C., et al.: Simulation of 3D path planning approach for quad-rotor helicopter based on A~* algorithm. J. Hefei Univ. Technol. (Nat. Sci.) 40, 304–309 (2017)

    Google Scholar 

  18. Feng, Q., Gao, J., Deng, X.: Path planner for UAVs navigation based on A* algorithm incorporating intersection. In: 2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC), Nanjing (2016)

    Google Scholar 

  19. Xiao, Z., Yuan, D., Qu, Y.: Multi UAV cooperative path planning based on A* fixed length search algorithm. Flight Mech. 30(01), 92–96 (2012)

    Google Scholar 

  20. Ning, B., Xiangmin, L., Jin, D., Jiayu, T.: Hierarchical cooperative path planning of multiple UAVs based on variable step sparse A * search and MPC. Command Control Simul. 040(002), 65–71 (2018)

    Google Scholar 

  21. Ma, X., Jiao, Z., Wang, Z., et al.: 3-D decentralized prioritized motion planning and coordination for high-density operations of micro aerial vehicles. IEEE Trans. Control Syst. Technol. 26(3), 939–953 (2017)

    Article  Google Scholar 

  22. Xie, C., Li, K., Xu, J., et al.: An improved multi objective particle swarm optimization algorithm MOPSO - II. J. Wuhan Univ. 60(02), 144–150 (2014)

    MATH  Google Scholar 

  23. Ye, A.: Study of the vehicle routing problem with time windows based on improved particle swarm optimization algorithm. In: 2011 International Conference on Computer Science and Service System (CSSS), Nanjing, pp. 4053–4057 (2011)

    Google Scholar 

  24. Liu, X.: Research on multi-UAV cooperative path planning based on improved ant colony algorithm. Zhengzhou University of Aeronautics (2020)

    Google Scholar 

  25. Ali, Z.A., Zhangang, H., Hang, W.B.: Cooperative path planning of multiple UAVs by using max-min ant colony optimization along with Cauchy mutant operator. Fluct. Noise Lett. 20(01), 2150002 (2021)

    Article  Google Scholar 

  26. Dai, X., et al.: Mobile robot path planning based on ant colony algorithm with A* heuristic method. Front. Neurorobot. 13, 15 (2019)

    Article  Google Scholar 

  27. Jin, L., Kepu, S.: Application of neural network in path planning of mobile robot. J. Syst. Simul. 22(s1), 269–272 (2010)

    Google Scholar 

  28. Chen, X., Ai, Y.: Three-dimensional path planning of UAV based on improved neural network. Electro Opt. Control 9, 7–11 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sun, W., Hao, M. (2022). A Survey of Cooperative Path Planning for Multiple UAVs. In: Wu, M., Niu, Y., Gu, M., Cheng, J. (eds) Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021). ICAUS 2021. Lecture Notes in Electrical Engineering, vol 861. Springer, Singapore. https://doi.org/10.1007/978-981-16-9492-9_20

Download citation

Publish with us

Policies and ethics