Citation: Ying, W.; Wang, Z.; Li, H.;
Du, S.; Zhao, M. Intelligent Ship
Scheduling and Path Planning
Method for Maritime Emergency
Rescue. Algorithms 2024, 17, 197.
https://doi.org/10.3390/a17050197
Academic Editor: Günther Raidl
Received: 1 March 2024
Revised: 26 April 2024
Accepted: 29 April 2024
Published: 8 May 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
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conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
Intelligent Ship Scheduling and Path Planning Method for
Maritime Emergency Rescue
Wen Ying
1
, Zhaohui Wang
1
, Hui Li
1
, Sheng Du
2
and Man Zhao
1,
*
1
School of Computer Science, China University of Geosciences, Wuhan 430074, China
2
School of Automation, China University of Geosciences, Wuhan 430074, China; dusheng@cug.edu.cn
* Correspondence: zhaoman@cug.edu.cn; Tel.: +86-18627151803
Abstract: Intelligent ship navigation scheduling and planning is of great significance for ensuring
the safety of maritime production and life and promoting the development of the marine economy.
In this paper, an intelligent ship scheduling and path planning method is proposed for a practical
application scenario wherein the emergency rescue center receives rescue messages and dispatches
emergency rescue ships to the incident area for rescue. Firstly, the large-scale sailing route of the task
ship is pre-planned in the voyage planning stage by using the improved A* algorithm. Secondly, the
full-coverage path planning algorithm is used to plan the ship’s search route in the regional search
stage by updating the ship’s navigation route in real time. In order to verify the effectiveness of the
proposed algorithm, comparative experiments were carried out with the conventional algorithm
in the two operation stages of rushing to the incident sea area and regional search and rescue. The
experimental results show that the proposed algorithm can adapt to emergency search and rescue
tasks in the complex setting of the sea area and can effectively improve the efficiency of the operation,
ensure the safety of the operation process, and provide a more intelligent and efficient solution for
the planning of maritime emergency rescue tasks.
Keywords: maritime emergency rescue; intelligent navigation; path planning; A* algorithm; B-spline
interpolation; regional search
1. Introduction
Ship navigation scheduling and planning have broad application prospects in maritime
emergency rescue, maritime safety governance, maritime shipping control, and other
fields [
1
]. Specifically, in the field of emergency rescue, after receiving the maritime rescue
news alert, the search and rescue center needs to immediately dispatch rescue vessels to
the incident area and carry out search and rescue operations. However, due to the opaque
information and inflexibility of the information of ships, facilities, and operators, there will
be inaccurate intelligent scheduling and low efficiency. The application of intelligent ship
navigation scheduling and planning can make the search and rescue command center more
flexible and allow it to have a more reasonable allocation of search and rescue resources,
which is conducive to improving the efficiency of maritime search and rescue operations.
The existing studies on ship navigation scheduling and planning are mainly based
on traditional planning algorithms and intelligent bionic path planning algorithms [
2
].
The traditional planning algorithms mainly include the depth-first search algorithm [
3
],
Dijkstra algorithm [
4
], A* algorithm [
5
], and the artificial potential field algorithm [
6
].
The authors of [
7
] designed a global path planning algorithm with a mandatory point
constraint by integrating an improved A* algorithm and simulated annealing algorithm
for the problem of non-point to point global path planning. The authors of [
8
] improved
the hybrid path planning algorithm based on a static environmental potential field and
dynamic environmental attraction and repulsion, combined with the typical mariner ship
type and proportional differential control, which was suitable for local minimum problems
Algorithms 2024, 17, 197. https://doi.org/10.3390/a17050197 https://www.mdpi.com/journal/algorithms
