Optimizing Search and Rescue Strategies for Deep-Sea Submersibles Using the Monte Carlo Method

Abstract

Deep-sea exploration is an important field where humans challenge the limits of nature. Improving its safety and efficiency is of great significance to deep-sea scientific research and resource development. In response to this, this study established a Monte Carlo-based motion model to predict the location of the crashed submersible. To truly simulate the distress environment of the submersible, this study introduced the Monte Carlo method to randomly generate 2,000 particles to reflect the uncertainty of the initial distress location, ocean current direction, and seawater density. Finally, it was combined with a six-degree-of-freedom kinematic model to generate All possible time-varying trajectories. This study also compared the effects before and after using the corresponding equipment and found that the equipment has a good effect on reducing uncertainties. This paper expands the field focused on floating objects on the sea surface to the search for underwater targets.