skip to main content
10.1145/3653081.3653160acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiotaaiConference Proceedingsconference-collections
research-article

A new dynamic planning for airport surface based on an improved artificial potential field algorithm in a connected aircraft environment

Authors Info & Claims
Published:03 May 2024Publication History

ABSTRACT

With the increase of air traffic flow, taxiway conflicts and flight delays at large and busy airports have become important factors affecting airport operational efficiency. Based on 5G technology, connected aircraft environment can quickly aggregate information in different scenarios, and provide precise information such as aircraft position, speed and attitude to the cloud in real-time, and its powerful computing and interactive capabilities make it possible to provide important technical support for collaborative operations at airport scenes. This paper proposes a dynamic route optimization model based on connected aircraft environment for aircraft taxi path planning. The model is constructed by setting three types of taxiway conflict constraints and other operational scheduling constraints. Using traditional artificial potential field algorithms with improved constraint methods can improve accuracy and effectiveness in solving conflicts during airport scene path planning processes. Validation using departure time data for flights at Lukou Airport shows that this method can directly perform dynamic programming under an intelligent networked environment, reducing algorithm complexity and improving computational efficiency while achieving real-time conflict resolution during path planning suitable for dynamic scene path planning.

References

  1. Zhai, W.P., Liu, R.N., Zhu, C.Y. 2022. Optimization of taxiway based on improved Dijkstra algorithm. Journal of Civil Aviation University of China.vol.40, no.1, pp.1–6. https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2022&filename=ZGMH202201001&v=.Google ScholarGoogle Scholar
  2. Zhu, X., XU, J.K., WU, L.L., Zhu, L. 2018. Airport Intelligent Scheduling Based on Floyd. FCFS and SJF Algorithm, Modern information technology. vol.2, no. 6, Art. no. 6. https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2018&filename=XDXK201806058&v=.Google ScholarGoogle Scholar
  3. Gu, R.P., Cui, P., Tang, J.X., Zhao, X.L. 2015. Research on dynamic planning of scene gliding based on D* algorithm. Science Technology and Engineering.vol.15, no.1, Art.no.1. https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2015&filename=KXJS201501060&v=.Google ScholarGoogle Scholar
  4. Liu, F., Zhang X.J., Ma, G.L., Liu L. 2017. Research on A* Algorithm-Based Dynamic Programming Method for Airport Ground Movement. Journal of Air Force Engineering University (Natural Science Edition). vol. 18, no. 4, Art. no. 4. https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C44YLTlOAiTRKibYlV5Vjs7iAEhECQAQ9aTiC5BjCgn0RoLq5HLh6sAhaOiE2WX1MAxjeXiWVtLX2YTOHeUXBAdm&uniplatform=NZKPT.Google ScholarGoogle Scholar
  5. Li, Z., Zhang, J., Zhang, X.J. 2009. A dynamic model for aircraft route optimizing in airport surface management. In 2009 9th International Conference on Electronic Measurement & Instruments. Beijing.pp. 3-1068-3–1072. doi: 10.1109/ICEMI.2009.5274377.Google ScholarGoogle ScholarCross RefCross Ref
  6. Zhu, X.P., Tang, X.M., Han, S.C. 2013. Aircraft Initial Taxiing Route Planning Based on Petri Net and Genetic Algorithm. Journal of Southwest Jiaotong University. vol. 48, no. 3, Art. no. 3. https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFD&dbname=CJFD2013&filename=XNJT201303028&v=.Google ScholarGoogle Scholar
  7. Sun, G.Y., Liu, C.Y. 2016. Aircraft glide path optimization based on swarm algorithm. Aeronautical Computing Technology. vol. 46, no. 1, Art. no. 1. https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2016&filename=HKJJ201601014&v=.Google ScholarGoogle Scholar
  8. Yang, Q.M., Tang, J.J., FU, Q., Liu, Y. 2022. Multi-layer mapping relationship modeling of intelligent and connected transportation system. Technology & Economy in Areas of Communications. vol. 24, no. 1, pp. 1–8, doi: 10.19348/j.cnki.issn1008-5696.2022.01.001.Google ScholarGoogle ScholarCross RefCross Ref
  9. Ma, W.J., Li J.Y., Yu, C.H. 2023. Intersection Control in Mixed Traffic with Connected Automated Vehicles: A Review of Recent Developments and Research Frontiers. China J. Highw. Transp. vol. 36, no. 2, pp. 22–40. doi: 10.19721/j.cnki.1001-7372.2023.02.002.Google ScholarGoogle ScholarCross RefCross Ref
  10. Zu H., Long Y., Han QW., Wang Y., Zeng, L.Q., Chen X., Zhang E.D., Zhuo, X. 2023. Test - based extraction and identification of key scenariosfor digital twins of intelligent networked vehicles. Journal of Chongqing University of Technology (Natural Science). vol. 37, no. 1, pp. 75–84. https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2023&filename=CGGL202301010&v=.Google ScholarGoogle Scholar
  11. An X., Liu Y. 2020. The Application Prospect of Vehicle-road Collaborative Autonomous Driving Technology in Civil Aviation Airport. Journal of Transportation Engineering. vol. 20, no. 4, pp. 57-61+67, 2020, doi: 10.13986/j.cnki.jote.2020.04.010.Google ScholarGoogle ScholarCross RefCross Ref
  12. Zhang, K., Liu, C., Lan, P.Y. 2021. Local Path Planning Based on Improved Artificial Potential Field Method, Automotive Digest (Chinese), no. 7, pp. 59–62, doi: 10.19822/j.cnki.1671-6329.20210057.Google ScholarGoogle ScholarCross RefCross Ref
  13. Xu, W., Cheng Z., Zhu, L., Zhang, Y.H. 2022. A local path planning algorithm based on improved artificial potential field method. Electronic Measurement Technology. vol. 45, no. 19, pp. 83–88. doi: 10.19651/j.cnki.emt.2209505.Google ScholarGoogle ScholarCross RefCross Ref
  14. S. Dalai, M. Irfan, S. Singh, K. Kishore, and S. A. Akbar, 2021. Heuristic Guided Artificial Potential Field for Avoidance of Small Obstacles, in 2021 21st International Conference on Control, Automation and Systems (ICCAS), Jeju, Korea, Republic of: IEEE, Oct. 2021, pp. 765–770. doi: 10.23919/ICCAS52745.2021.9649879.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. H. Li. 2020, Robotic Path Planning Strategy Based on Improved Artificial Potential Field, in 2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE), Beijing, China: IEEE, Oct. 2020, pp. 67–71. doi: 10.1109/ICAICE51518.2020.00019.Google ScholarGoogle ScholarCross RefCross Ref
  16. S. Xie, J. Hu, P. Bhowmick, Z. Ding, and F. Arvin. 2022. Distributed Motion Planning for Safe Autonomous Vehicle Overtaking via Artificial Potential Field, IEEE Trans. Intell. Transport. Syst., vol. 23, no. 11, pp. 21531–21547, Nov. 2022, doi: 10.1109/TITS.2022.3189741.Google ScholarGoogle ScholarCross RefCross Ref
  17. Wang S.F., Zhang Y.X., Liu, Z.F. 2018. A Research on Overtaking Lane Planning for Intelligent Vehicles Basedon Improved Artificial Potential Field Method. Automobile Technology. no. 3, pp. 5–9. doi: 10.19620/j.cnki.1000-3703.20170731.Google ScholarGoogle ScholarCross RefCross Ref
  18. China Flier, 2023. https://aip.chinaflier.com/#/.Google ScholarGoogle Scholar

Index Terms

  1. A new dynamic planning for airport surface based on an improved artificial potential field algorithm in a connected aircraft environment

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      IoTAAI '23: Proceedings of the 2023 5th International Conference on Internet of Things, Automation and Artificial Intelligence
      November 2023
      902 pages
      ISBN:9798400716485
      DOI:10.1145/3653081

      Copyright © 2023 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 3 May 2024

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed limited
    • Article Metrics

      • Downloads (Last 12 months)2
      • Downloads (Last 6 weeks)2

      Other Metrics

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format .

    View HTML Format