IDEAS home Printed from https://ideas.repec.org/a/taf/tsysxx/v47y2016i6p1407-1420.html
   My bibliography  Save this article

UAV path planning using artificial potential field method updated by optimal control theory

Author

Listed:
  • Yong-bo Chen
  • Guan-chen Luo
  • Yue-song Mei
  • Jian-qiao Yu
  • Xiao-long Su

Abstract

The unmanned aerial vehicle (UAV) path planning problem is an important assignment in the UAV mission planning. Based on the artificial potential field (APF) UAV path planning method, it is reconstructed into the constrained optimisation problem by introducing an additional control force. The constrained optimisation problem is translated into the unconstrained optimisation problem with the help of slack variables in this paper. The functional optimisation method is applied to reform this problem into an optimal control problem. The whole transformation process is deduced in detail, based on a discrete UAV dynamic model. Then, the path planning problem is solved with the help of the optimal control method. The path following process based on the six degrees of freedom simulation model of the quadrotor helicopters is introduced to verify the practicability of this method. Finally, the simulation results show that the improved method is more effective in planning path. In the planning space, the length of the calculated path is shorter and smoother than that using traditional APF method. In addition, the improved method can solve the dead point problem effectively.

Suggested Citation

  • Yong-bo Chen & Guan-chen Luo & Yue-song Mei & Jian-qiao Yu & Xiao-long Su, 2016. "UAV path planning using artificial potential field method updated by optimal control theory," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(6), pages 1407-1420, April.
    • Handle: RePEc:taf:tsysxx:v:47:y:2016:i:6:p:1407-1420
    DOI: 10.1080/00207721.2014.929191
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207721.2014.929191
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207721.2014.929191?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lingli Yu & Decheng Kong & Xiaoxin Yan, 2018. "A Driving Behavior Planning and Trajectory Generation Method for Autonomous Electric Bus," Future Internet, MDPI, vol. 10(6), pages 1-14, June.
    2. Johannes Schmidt & Armin Fügenschuh, 2023. "A two-time-level model for mission and flight planning of an inhomogeneous fleet of unmanned aerial vehicles," Computational Optimization and Applications, Springer, vol. 85(1), pages 293-335, May.
    3. Pan, Jeng-Shyang & Lv, Ji-Xiang & Yan, Li-Jun & Weng, Shao-Wei & Chu, Shu-Chuan & Xue, Jian-Kai, 2022. "Golden eagle optimizer with double learning strategies for 3D path planning of UAV in power inspection," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 193(C), pages 509-532.
    4. Changxin Huang & Wei Li & Chao Xiao & Binbin Liang & Songchen Han, 2018. "Potential field method for persistent surveillance of multiple unmanned aerial vehicle sensors," International Journal of Distributed Sensor Networks, , vol. 14(1), pages 15501477187, January.
    5. Ahmed, Gamil & Sheltami, Tarek & Mahmoud, Ashraf & Yasar, Ansar, 2020. "IoD swarms collision avoidance via improved particle swarm optimization," Transportation Research Part A: Policy and Practice, Elsevier, vol. 142(C), pages 260-278.
    6. Simone Fiori & Luca Bigelli & Federico Polenta, 2022. "Lie-Group Type Quadcopter Control Design by Dynamics Replacement and the Virtual Attractive-Repulsive Potentials Theory," Mathematics, MDPI, vol. 10(7), pages 1-37, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:tsysxx:v:47:y:2016:i:6:p:1407-1420. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TSYS20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.