Volume 21 Issue 5
Nov.  2021
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2021  >  21(5): 297-308
HE Yi-xiong, LIANG Yu, XIONG Yong, MOU Jun-min, LI Meng-xia, ZHANG Ke. Dynamic adaptive intelligent navigation method for multi-object situation in open water[J]. Journal of Traffic and Transportation Engineering, 2021, 21(5): 297-308. doi: 10.19818/j.cnki.1671-1637.2021.05.025
Citation: HE Yi-xiong, LIANG Yu, XIONG Yong, MOU Jun-min, LI Meng-xia, ZHANG Ke. Dynamic adaptive intelligent navigation method for multi-object situation in open water[J]. Journal of Traffic and Transportation Engineering, 2021, 21(5): 297-308. doi: 10.19818/j.cnki.1671-1637.2021.05.025
shu

Dynamic adaptive intelligent navigation method for multi-object situation in open water

doi: 10.19818/j.cnki.1671-1637.2021.05.025
  • 1.

    School of Navigation, Wuhan University of Technology, Wuhan 430063, Hubei, China

  • 2.

    Hubei Key Laboratory of Inland Shipping Technology, Wuhan University of Technology, Wuhan 430063, Hubei, China

Funds:

National Key Research and Development Program of China 2019YFB1600603

National Natural Science Foundation of China 52071249

Major Special Projects of Transportation Science and Technology of Jiangsu Province 2018Z01

More Information
  • Author Bio:

    HE Yi-xiong(1976-), male, associate professor, captain, PhD, heyixiong7@whut.edu.cn

  • Corresponding author: MOU Jun-min (1974-), male, professor, PhD, moujm@whut.edu.cn
  • Received Date: 2021-05-12
    Available Online: 2021-11-13
  • Publish Date: 2021-10-01
    Abstract
  • An intelligent navigation method of dynamic adaptive target ship collision avoidance action in open water was proposed considering the ship maneuvering characteristics, the requirements of International Regulations for Preventing Collisions at Sea 1972 and good seamanship. The digital twin traffic environment was constructed by classifying and modeling objects. An automatic navigation model was developed by combination of course control method, ship maneuvering motion and sailing resuming model, and ship's nonlinear maneuvering motion was deduced. The requirements of International Regulations for Preventing Collisions at Sea 1972 were quantitatively analyzed based on the automatic navigation model, and the dynamic collision avoidance mechanism was studied. The method to calculate applicable course was established. In the multi-target environment, the maneuvering discrimination method of target ship was proposed. The method to obtain the factors such as the course changing time, amplitude and sailing resuming time which constitute the autonomous navigation scheme under the constraint of rules was studied. Simulation results show that the intelligent navigation method can adapt to the residual error and random motion of the target ships based on the rolling calculations of the information update at the second-level. The proposed intelligent navigation method can accurately achieve the feasible course range and course change amplitude of 1°. The calculation step lengths of program and sailing resumption time are set to 1 and 10 s, respectively, and multiple static objects and six target ships maintaining the course and speed are established in this simulation environment. Own ship remains clear from all targets and sails autonomously to the destination after a series of maneuverings of 9° to starboard, sailing resuming, keeping course and speed, sailing resuming at 640, 1 053, 2 561 and 3 489 s, respectively. Target ships are set to perform uncoordinated collision avoidance actions at 300 s, and own ship remains clear from all targets and sails autonomously to the destination after a series of maneuverings of 9° to starboard, 12° to port, 17° to starboard, and sailing resuming at 980, 2 790, 3 622 and 5 470 s, respectively. Therefore, a ship in any initial states can automatically sail along a planned route to its destination. The proposed method is suitable for intelligent navigation in actual open sea areas with multiple and multiple dynamic and static objects. 5 tabs, 13 figs, 30 refs.

     

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