Break Out of the Sea Surface: Genetic Algorithm based Search Path Planning Model for Locating and Searching Submersible Lost in Deep Sea

Abstract

Deep-sea tourism and exploration, which require regulatory approval due to safety concerns, are burgeoning entertainment activities. Our study introduces a probabilistic model for locating missing submersibles to facilitate these approvals. The model predicts the submersible's location based on Newton's second law, analyzing three potential malfunction scenarios. It incorporates dynamics to account for variations in water flow and terrain, using a normal distribution to handle random location fluctuations. To address the complexities of deep-sea search and rescue, we propose a method that integrates detailed equipment selection, suited to the extreme conditions. Furthermore, we develop a discrete grid-based model for search path planning. This model simplifies path planning into a sorting optimization problem by connecting grid centers and calculating the probability integral for each grid. Our research also discusses the scalability of our positioning and search models, crucial for adapting to various deep-sea environments. We demonstrate the efficacy of our model through simulations and calculations, proving its potential to enhance safety and efficiency in deep-sea tourism projects. This comprehensive approach ensures that our probabilistic model not only predicts the submersible's location but also optimizes the search process, addressing both regulatory and practical challenges in the field of underwater exploration.