Multi-hop Route Planning Based on Environment Information for Path-Following UAVs

  • Tien-Wen Sung
  • Linyun SunEmail author
  • Kuo-Chi Chang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1153)


Flight path planning is one of the key research issues in the field of unmanned aerial vehicles (UAVs). There have been various approaches proposed to find or plan an optimal or appropriate path for numerous UAV applications. The obtained paths will be followed as a navigation once the UAV flies. Related works have utilized different methods to find the paths for different situations or considerations in the flight environments. Several bio-inspired algorithms such as PSO, GA, ABC, and ACO as well as the graph-based A* algorithm were usually utilized in the solutions of path planning. In this paper, the concept of Floyd-Warshall algorithm and a grid-based map presenting environmental information are utilized to find an optimal path with minimum hop count. A simulator is also developed for this work. Several simulation results with different grid sizes are illustrated. This study presents a preliminary trial work of grid-based multi-hop route planning for UAVs. Both the grid model and concerned environmental information can be extended for further complex researches.


Unmanned aerial vehicles Path planning Grids No-fly zones Obstacles 



This work is supported by the Fujian Provincial Natural Science Foundation in China (Project Number: 2017J01730) and the Education Department of Fujian Province (Project Number: GY-Z19005).


  1. 1.
    Hassanalian, M., Abdelkefi, A.: Classifications, applications, and design challenges of drones: a review. Prog. Aerosp. Sci. 91, 99–131 (2017)Google Scholar
  2. 2.
    Liew, C.F., DeLatte, D., Takeishi, N., Yairi, T.: Recent Developments in aerial robotics: a survey and prototypes overview, pp. 1–14. arXiv:1711.10085v2 (2017)
  3. 3.
    Rosen, K.H.: Discrete Mathematics and Its Applications, 8th edn. McGraw-Hill, New York (2019)Google Scholar
  4. 4.
    Sujit, P.B., Saripalli, S., Sousa, J.B.: Unmanned aerial vehicle path following: a survey and analysis of algorithms for fixed-wing unmanned aerial vehicles. IEEE Control Syst. Mag. 34(1), 42–59 (2014)Google Scholar
  5. 5.
    Lin, Y., Saripalli, S.: Sampling-based path planning for UAV collision avoidance. IEEE Trans. Intell. Transp. Syst. 18(11), 3179–3192 (2017)Google Scholar
  6. 6.
    Radmanesh, M., Kumar, M., Guentert, P.H., Sarim, M.: Overview of path-planning and obstacle avoidance algorithms for UAVs: a comparative study. Unmanned Syst. 6(2), 95–118 (2018)Google Scholar
  7. 7.
    Zhao, Y., Zheng, Z., Liu, Y.: Survey on computational-intelligence-based UAV path planning. Knowl. Based Syst. 158(15), 54–64 (2018)Google Scholar
  8. 8.
    Konatowski, S., Pawłowski, P.: Ant colony optimization algorithm for UAV path planning. In: International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering 2018, pp. 177–182. IEEE (2018)Google Scholar
  9. 9.
    Dhulkefl, E.J., Durdu, A.: Path planning algorithms for unmanned aerial vehicles. Int. J. Trend Sci. Res. Dev. 3(4), 359–362 (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.College of Information Science and EngineeringFujian University of TechnologyFuzhouChina

Personalised recommendations