Comprehensive Rescue Model for Submersible Based on Monte Carlo Analysis and Genetic Algorithm

Abstract

In recent years, there has been a growing interest in deep-sea exploration activities, yet significant gaps persist in search and rescue methodologies for deep-sea submersibles. This paper introduces a position prediction and search model for deep-sea submersibles, leveraging Monte Carlo analysis and genetic algorithms for optimization. The underwater motion of the submersible is simulated through dynamic system modeling. Monte Carlo analysis is utilized to model the scattering of the submersible within the specified ranges of longitude [10.25, 20.25], latitude [20.00, 36.50], and depth [2500m-4000m]. The analysis reveals a normal distribution of scattering points from the middle to the end. Genetic algorithms are employed to refine search and rescue paths, iterated 1000 times to identify the optimal path spanning from 540.1km to 225.6km. In comparison to complex numerical methods, this model offers ease of programming and implementation while accurately predicting the submersible's position in three-dimensional space. It provides the most efficient search and rescue route, ensuring swift response in the face of improbable communication or mechanical failures.