• Chinese Journal of Ship Research
  • Vol. 20, Issue 1, 135 (2025)
Qunpeng WANG1, Longhao LI2,3, Hongxu GUAN4,5, Jialun LIU3,6,7..., Jianzhe CAI4,5, Zhengrong SHA1 and Jinshui ZHANG1|Show fewer author(s)
Author Affiliations
  • 1School of Shipping and Maritime Studies, Guangzhou Maritime University, Guangzhou 510725, China
  • 2School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
  • 3Intelligent Transport System Research Center, Wuhan University of Technology, Wuhan 430063, China
  • 4School of Navigation, Wuhan University of Technology, Wuhan 430063, China
  • 5Hubei Key Laboratory of Inland Shipping Technology, Wuhan 430063, China
  • 6State Key Laboratory of Maritime Technology and Safety(Wuhan University of Technology), Wuhan 430063, China
  • 7National Engineering Research Center for Water Transport Safety, Wuhan 430063, China
  • show less
    DOI: 10.19693/j.issn.1673-3185.04076 Cite this Article
    Qunpeng WANG, Longhao LI, Hongxu GUAN, Jialun LIU, Jianzhe CAI, Zhengrong SHA, Jinshui ZHANG. Ship local path planning method based on three-dimensional potential field model[J]. Chinese Journal of Ship Research, 2025, 20(1): 135 Copy Citation Text show less
    References

    [3] W S WANG, L T WANG, C Y ZHANG et al. Social interactions for autonomous driving: a review and perspectives. Foundations and Trends® in Robotics, 10, 198-376(2022).

    [4] H WANG, Y J HUANG, A KHAJEPOUR et al. Crash mitigation in motion planning for autonomous vehicles. IEEE Transactions on Intelligent Transportation Systems, 20, 3313-3323(2019).

    [5] NI D H. A unified perspective on traffic flow they, part I: the field they[C]ICCTP 2011: Towards Sustainable Transptation Systems. Reston: E, 2011: 42274243.

    [6] C Y ZHANG, J C ZHU, W S WANG et al. Spatiotemporal learning of multivehicle interaction patterns in lane-change scenarios. IEEE Transactions on Intelligent Transportation Systems, 23, 6446-6459(2022).

    [7] J Q WANG, J WU, X J ZHENG et al. Driving safety field theory modeling and its application in pre-collision warning system. Transportation Research Part C: Emerging Technologies, 72, 306-324(2016).

    [9] L X GAN, Z X YAN, L ZHANG et al. Ship path planning based on safety potential field in inland rivers. Ocean Engineering, 260, 111928(2022).

    [13] Y X HUANG, Y F HU, J B WU et al. Observer-based motion control system for the approach ship with propeller and rudder in the process of underway replenishment. Ocean Engineering, 222, 108586(2021).

    [14] C LIU, CHEN C L PHILIP, Z J ZOU et al. Adaptive NN-DSC control design for path following of underactuated surface vessels with input saturation. Neurocomputing, 267, 466-474(2017).

    [16] C LIU, D Y WANG, Y X ZHANG et al. Model predictive control for path following and roll stabilization of marine vessels based on neurodynamic optimization. Ocean Engineering, 217, 107524(2020).

    [17] C G LIU, R R NEGENBORN, H R ZHENG et al. A state-compensation extended state observer for model predictive control. European Journal of Control, 36, 1-9(2017).

    [19] Y X JI, L T NI, C ZHAO et al. TriPField: a 3D potential field model and its applications to local path planning of autonomous vehicles. IEEE Transactions on Intelligent Transportation Systems, 24, 3541-3554(2023).

    Qunpeng WANG, Longhao LI, Hongxu GUAN, Jialun LIU, Jianzhe CAI, Zhengrong SHA, Jinshui ZHANG. Ship local path planning method based on three-dimensional potential field model[J]. Chinese Journal of Ship Research, 2025, 20(1): 135
    Download Citation