Author(s)

Ziyue Zhang, Yujing Sun, Jiayun Chen

Corresponding Author

Ziyue Zhang

Abstract

Predictive submersible models can be used to find the location of submersibles more efficiently. In this paper, the Inner Ionian Sea is used as the research object to establish a model for predicting the position of submersibles. At first, the Particle Swarm Optimization algorithm was employed to calculate the shortest path for the submersible to navigate through all shipwrecks, while considering the ocean current motion and incorporating a viscous Lamb Eddy model. The differential equation governing the submersible's position is then presented, enabling the determination of its position coordinates based on time. The uncertainties of ocean currents, seawater density, and seabed topography necessitate the inclusion of two types of equipment on the submersible: multi-beam sonar and underwater acoustic beacons. And then the present study focuses on the examination of sonar and ROV, considering the equipment carried by the main vessel. The weights assigned to the frequency, opening angle, rated depth, and price of side scan sonar and multi-beam sounding sonar are 0.2, 0.5, 0.3, and -0.3 respectively. Among them, the EM2040 series of multi-beam sonar achieves the highest score. The rated depth and price of the ROV were then assigned weights of 0.7 and -0.3, respectively, resulting in the selection of the Hydronre ROV. Finally, when budget allows, electromagnetic technology can also be employed to transport an AUV device that integrates multiple sensors on board the rescue vessel. This study pioneeringly develops an integrated safety location model and a maritime search and rescue model. The introduction of this comprehensive framework addresses the void in the current market's demand for efficient and precise underwater search and rescue systems. The novel application of the particle swarm optimization algorithm within a GIS for optimal path planning not only enhances the efficiency and precision of path determination but also mitigates uncertainty and risk during exploratory endeavors.

Keywords

The Particle Swarm Optimization Algorithm, Multi-beam Sounding Sonar, Side Scan Sonar, Eddy Model