# Intelligent Ship Scheduling and Path Planning Method for Maritime Emergency Rescue
###### 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. maritime emergency rescue; intelligent navigation; path planning; A* algorithm; B-spline interpolation; regional search +
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in restricted waters such as narrow waters. The D* algorithm is a dynamic programming algorithm based on the A* algorithm which can adapt to dynamic programming in complex environments and has a good [9; 10] effect in real-time planning and scheduling. The intelligent bionic algorithm [11; 12; 13] is a kind of random search algorithm that operates by simulating biological evolution or population behavior; it is widely used in path planning. In reference, a static path planning method based on a simulated annealing algorithm is proposed, which uses neighborhood search and probabilistic jumping strategies to find a feasible and optimal observation path. The authors of [14] proposed a dynamic path planning method based on game theory. By establishing a game model and designing a reasonable negotiation mechanism, the problem of resource competition and conflict was solved, and the overall observation efficiency was improved. The ant colony search algorithm does not rely on a lot of prior knowledge; the structure of the algorithm is simple and easy to understand and implement, and it has strong adaptability for optimizing solutions to complex problems. However, the iteration speed of the algorithm [15; 16; 17] is slow, and it is easy to fall into local optimum. In reference [18], a particle swarm optimization (PSO) algorithm was proposed, which used the idea of genetic mutation for reference to increase the population diversity of particles and effectively improve the convergence speed of the algorithm. The authors of [19] proposed a path planning with dynamic obstacle avoidance method of the manipulator based on a deep reinforcement learning algorithm soft actor-critic (SAC). A comprehensive reward function of dynamic obstacle avoidance and target approach is designed to avoid the moving obstacle in the environment and make real-time planning. In addition, some studies related to trajectory planning and trajectory analysis have been conducted by using AIS and SOS data. The authors of [20] propose a multi-regime vessel trajectory reconstruction model based on a large AIS dataset. Through the removal of outliers, ship navigational state estimation, and vessel trajectory fitting, the proposed trajectory can eliminate the noise in the raw AIS data quite well. Ref. [21] quantified the operational risk management strategies of shipping companies using AIS data, providing a new research direction regarding AIS in the field of shipping risk management.