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Long-voyage route planning method based on multi-scale Visibility Graph for autonomous ships

Long-voyage route planning method based on multi-scale Visibility Graph for autonomous ships
Long-voyage route planning method based on multi-scale Visibility Graph for autonomous ships
With the increasing demand for the autonomous ship, a fast planning method for long-distance ship routes is needed. In this paper, a multi-scale Visibility Graph(VG) method is proposed for long-voyage route planning, as a solution to the problems of the slow planning and poor route accuracy. First, polygon data of obstacles are extracted from an electronic chart. In order to reduce the number of Visibility Points(VPs), the VPs are expanded from the convex points of these polygons. The small-scale, medium-scale, and large-scale VG models are established respectively. Second, this paper proposes the Local Planning Window(LPW) method, which greatly reduces the complexity of the VG models. The great circle route method is used to decompose the longer route, which further shorten the search time of the VG. The route planning process is designed for the multi-scale VG method. Finally, a long-voyage route planning example is carried out, in which, the utilization rate of the number of obstacle polygons and the number of VPs are analyzed. The data results show that: the complexity of VG models can be reduced greatly, and the search time of the VG will be shortened, by using the multi-scale VG method.
0029-8018
Wu, Gongxing
b9c76abb-6a84-4b43-b776-e677e8aeb327
Incecik, Atilla
25a12ee2-7ba6-47cf-af5d-a79de4c6a2c4
Tezdogan, Tahsin
7e7328e2-4185-4052-8e9a-53fd81c98909
Terziev, Momchil
938f71d0-02b5-414c-8c2d-9cca8cc87397
Wang, LingChao
6c0cc5c2-9556-40e1-b4a9-98dc6952c747
Wu, Gongxing
b9c76abb-6a84-4b43-b776-e677e8aeb327
Incecik, Atilla
25a12ee2-7ba6-47cf-af5d-a79de4c6a2c4
Tezdogan, Tahsin
7e7328e2-4185-4052-8e9a-53fd81c98909
Terziev, Momchil
938f71d0-02b5-414c-8c2d-9cca8cc87397
Wang, LingChao
6c0cc5c2-9556-40e1-b4a9-98dc6952c747

Wu, Gongxing, Incecik, Atilla, Tezdogan, Tahsin, Terziev, Momchil and Wang, LingChao (2021) Long-voyage route planning method based on multi-scale Visibility Graph for autonomous ships. Ocean Engineering, 219. (doi:10.1016/j.oceaneng.2020.108242).

Record type: Article

Abstract

With the increasing demand for the autonomous ship, a fast planning method for long-distance ship routes is needed. In this paper, a multi-scale Visibility Graph(VG) method is proposed for long-voyage route planning, as a solution to the problems of the slow planning and poor route accuracy. First, polygon data of obstacles are extracted from an electronic chart. In order to reduce the number of Visibility Points(VPs), the VPs are expanded from the convex points of these polygons. The small-scale, medium-scale, and large-scale VG models are established respectively. Second, this paper proposes the Local Planning Window(LPW) method, which greatly reduces the complexity of the VG models. The great circle route method is used to decompose the longer route, which further shorten the search time of the VG. The route planning process is designed for the multi-scale VG method. Finally, a long-voyage route planning example is carried out, in which, the utilization rate of the number of obstacle polygons and the number of VPs are analyzed. The data results show that: the complexity of VG models can be reduced greatly, and the search time of the VG will be shortened, by using the multi-scale VG method.

Text
Wu_etal_OE_2021_Long_voyage_route_planning_method_based_on_multi_scale_Visibility_Graph_for_autonomous_ships - Accepted Manuscript
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More information

Accepted/In Press date: 12 October 2020
e-pub ahead of print date: 27 December 2020
Published date: 1 January 2021

Identifiers

Local EPrints ID: 473941
URI: http://eprints.soton.ac.uk/id/eprint/473941
ISSN: 0029-8018
PURE UUID: 594d6460-960e-4ea8-8505-430db475d927
ORCID for Tahsin Tezdogan: ORCID iD orcid.org/0000-0002-7032-3038

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Date deposited: 06 Feb 2023 17:34
Last modified: 17 Mar 2024 07:38

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Contributors

Author: Gongxing Wu
Author: Atilla Incecik
Author: Tahsin Tezdogan ORCID iD
Author: Momchil Terziev
Author: LingChao Wang

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