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Path Planning of Unmanned Aerial Vehicles: Current State and Future Challenges

  • Aditi ZearEmail author
  • Virender Ranga
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1045)

Abstract

Path planning is the preliminary requirement of unmanned aerial vehicles (UAVs) for their autonomous functions. This paper discusses the significant usage of UAVs in distinct applications and the need for path planning in order to increase their service rate in different applications. UAV’s path planning can be either start to goal or coverage path planning. Path planning techniques can be generally categorized as roadmap/skeleton, approximate/exact cell decomposition, potential field, sampling-based, and bio-inspired/machine learning-based methods. These methods are briefly discussed in this paper. Finally, the present state of the art of UAV’s path planning using these techniques is discussed. This paper will be a seed source for the researchers who are actively working on UAVs to implement efficient path planning techniques according to their usability in different applications.

Keywords

Multi-UAV Optimal path planning Collision avoidance Adhoc networks 

Notes

Acknowledgements

This research work is being supported by SERB-DST, Government of India.

References

  1. 1.
    Goerzen, C., Kong, Z., Mettler, B.: A survey of motion planning algorithms from the perspective of autonomous UAV guidance. J. Intell. Rob. Syst. 57(1–4), 65 (2010)CrossRefGoogle Scholar
  2. 2.
    Goddemeier, N., Daniel, K., Wietfeld, C.: Role-based connectivity management with realistic air-to-ground channels for cooperative UAVs. IEEE J. Sel. Areas Commun. 30(5), 951–963 (2012)CrossRefGoogle Scholar
  3. 3.
    Yang, L., Qi, J., Song, D., Xiao, J., Han, J., Xia, Y.: Survey of robot 3D path planning algorithms. J. Control Sci. Eng. 2016, 5 (2016)Google Scholar
  4. 4.
    Debnath, S.S.K., Omar, R., Latip, N.B.A.: A review on energy efficient path planning algorithms for unmanned air vehicles. In: Computational Science and Technology, pp. 523–532 . Springer, Berlin (2019)Google Scholar
  5. 5.
    Jawhar, I., Mohamed, N., Al-Jaroodi, J., Agrawal, D.P., Zhang, S.: Communication and networking of uav-based systems: Classification and associated architectures. J. Netw. Comput. Appl. 84, 93–108 (2017)CrossRefGoogle Scholar
  6. 6.
    Bekmezci, I., Sahingoz, O.K., Temel, Ş.: Flying Ad-Hoc networks (fanets): A survey. Ad Hoc Netw. 11(3), 1254–1270 (2013)CrossRefGoogle Scholar
  7. 7.
    Sánchez-García, J., Reina, D., Toral, S.: A distributed PSO-based exploration algorithm for a UAV network assisting a disaster scenario. Future Gener. Comput. Syst. 90, 129–148 (2019)CrossRefGoogle Scholar
  8. 8.
    Tuna, G., Nefzi, B., Conte, G.: Unmanned aerial vehicle-aided communications system for disaster recovery. J. Netw. Comput. Appl. 41, 27–36 (2014)CrossRefGoogle Scholar
  9. 9.
    Loscri, V., Natalizio, E., Mitton, N.: Performance evaluation of novel distributed coverage techniques for swarms of flying robots. In: 2014 IEEE Wireless Communications and Networking Conference (WCNC), pp. 3278–3283. IEEE, New York (2014)Google Scholar
  10. 10.
    Cheng, Z., Wang, E., Tang, Y., Wang, Y.: Real-time path planning strategy for UAV based on improved particle swarm optimization. J. Comput. 9(1), 209–215 (2014)CrossRefGoogle Scholar
  11. 11.
    Li, J., Chen, J., Wang, P., Li, C.: Sensor-oriented path planning for multiregion surveillance with a single lightweight UAV SAR. Sensors 18(2), 548 (2018)CrossRefGoogle Scholar
  12. 12.
    Yoon, J., Jin, Y., Batsoyol, N., Lee, H.: Adaptive path planning of UAVs for delivering delay-sensitive information to Ad-Hoc nodes. In: 2017 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6. IEEE, New York (2017)Google Scholar
  13. 13.
    Xiao, Z., Zhu, B., Wang, Y., Miao, P.: Low-complexity path planning algorithm for unmanned aerial vehicles in complicated scenarios. IEEE Access 6, 57049–57055 (2018)CrossRefGoogle Scholar
  14. 14.
    Ghorbel, M.B., Rodriguez-Duarte, D., Ghazzai, H., Hossain, M.J., Menouar, H.: Energy efficient data collection for wireless sensors using drones. In: 2018 IEEE 87th Vehicular Technology Conference (VTC Spring), pp. 1–5. IEEE, New York (2018)Google Scholar
  15. 15.
    Wang, Z., Li, Y., Li, W.: An approximation path planning algorithm for fixed-wing UAVs in stationary obstacle environment. In: Proceedings of the 33rd Chinese Control Conference, pp. 664–669. IEEE, New York (2014)Google Scholar
  16. 16.
    Liu, Z., Zhang, Y., Yuan, C., Ciarletta, L., Theilliol, D.: Collision avoidance and path following control of unmanned aerial vehicle in hazardous environment. J. Intell. Rob. Syst. 1–18 (2018)Google Scholar
  17. 17.
    Salamat, B., Tonello, A.: Stochastic trajectory generation using particle swarm optimization for quadrotor unmanned aerial vehicles (UAVs). Aerospace 4(2), 27 (2017)CrossRefGoogle Scholar
  18. 18.
    Altmann, A., Niendorf, M., Bednar, M., Reichel, R.: Improved 3D interpolation-based path planning for a fixed-wing unmanned aircraft. J. Intell. Rob. Syst. 76(1), 185–197 (2014)CrossRefGoogle Scholar
  19. 19.
    Copot, C., Hernandez, A., Mac, T.T., De Keyse, R.: Collision-free path planning in indoor environment using a quadrotor. In: 2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR), pp. 351–356. IEEE, New York (2016)Google Scholar
  20. 20.
    Mengying, Z., Hua, W., Feng, C.: Online path planning algorithms for unmanned air vehicle. In: 2017 IEEE International Conference on Unmanned Systems (ICUS), pp. 116–119. IEEE, New York (2017)Google Scholar
  21. 21.
    Wang, Q., Chang, X.: The optimal trajectory planning for UAV in UAV-aided networks. In: International Conference on Cloud Computing and Security, pp. 192–204. Springer, Berlin (2016)Google Scholar
  22. 22.
    Arantes, M.S., Arantes, J.S., Toledo, C.F.M., Williams, B.C.: A hybrid multi-population genetic algorithm for UAV path planning. In: Proceedings of the Genetic and Evolutionary Computation Conference 2016, pp. 853–860. ACM, New York (2016)Google Scholar
  23. 23.
    Roberge, V., Tarbouchi, M., Labonté, G.: Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Trans. Ind. Inform. 9(1), 132–141 (2013)CrossRefGoogle Scholar
  24. 24.
    Pehlivanoglu, Y.V., Baysal, O., Hacioglu, A.: Path planning for autonomous UAV via vibrational genetic algorithm. Aircraft Eng. Aerosp. Technol.: Int. J. 79(4), 352–359 (2007)CrossRefGoogle Scholar
  25. 25.
    Kroumov, V., Yu, J., Shibayama, K.: 3D path planning for mobile robots using simulated annealing neural network. Int. J. Innovative Comput. Inf. Control 6(7), 2885–2899 (2010)Google Scholar
  26. 26.
    Kothari, M., Postlethwaite, I.: A probabilistically robust path planning algorithm for UAVs using rapidly-exploring random trees. J. Intell. Rob. Syst. 71(2), 231–253 (2013)CrossRefGoogle Scholar
  27. 27.
    Kim, S., Oh, H., Suk, J., Tsourdos, A.: Coordinated trajectory planning for efficient communication relay using multiple UAVs. Control Eng. Practice 29, 42–49 (2014)CrossRefGoogle Scholar
  28. 28.
    Perez-Carabaza, S., Besada-Portas, E., Lopez-Orozco, J.A., Jesus, M.: Ant colony optimization for multi-UAV minimum time search in uncertain domains. Appl. Soft Comput. 62, 789–806 (2018)CrossRefGoogle Scholar
  29. 29.
    Elbanhawi, M., Simic, M.: Sampling-based robot motion planning: A review. IEEE Access 2, 56–77 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer EngineeringNIT KurukshetraHaryanaIndia

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